Othman Lakhal
email: othman.lakhal@univ-lille1.fr

Achille Melingui
email: achille.melingui@univ-lille1.fr

Rochdi Merzouki
email: rochdi.merzouki@polytech-lille.fr

Hybrid Approach for Modeling and Solving of Kinematics of Compact Bionic Handling Assistant Manipulator

This paper deals with a methodology for a real-time solving of a complex kinematics of a class of continuum manipulators, namely the Compact Bionic Handling Assistant (CBHA). First, a quantitative approach is used to model kinematically the CBHA inspired from the modeling of parallel rigid manipulators. For this case, the CBHA is modeled as a series of vertebrae, where each vertebra is connected to the next one through a flexible link. The latter named an inter-vertebra is modeled by a 3UPS-1UP (Universal-Prismatic-Spherical) joints. The kinematic models of the CBHA are derived from the Inverse Kinematic Equations (IKE) of each inter-vertebra. A qualitative approach based on neural networks is used to provide approximated solutions of the IKE for real-time implementation. Thus, the combination of the advantages of quantitative and qualitative approaches allows proposing a hybrid methodology for accurate modeling and solving the kinematics of this class of continuum robots. A set of experiments are conducted using a CBHA in order evaluate the level of efficiency of the proposed hybrid approach.

I. INTRODUCTION

In the last decade, continuum robots have been the subject of intensive research [START_REF] Walker | Robot strings: long, thin continuum robots[END_REF]- [START_REF] Robinson | Continuum robots-a state of the art[END_REF] mainly thanks to their ability to reproduce some biological behaviors such as elephant trunks [START_REF] Behrens | An elephant's trunk-inspired robotic arm -trajectory determination and control[END_REF], octopus [START_REF] Sfakiotakis | Octopus-inspired eight-arm robotic swimming by sculling movements[END_REF], or tentacles [START_REF] Martinez-Alfaro | Mobile robot path planning and tracking using simulated annealing and fuzzy logic control[END_REF]. They are manufactured with flexible materials, allowing them to adapt and maneuver in congested and narrow environments [START_REF] Walker | Continuum robot arms inspired by cephalopods[END_REF]. These properties make them suited for a large number of applications, including surveillance, rescue and exploration.

Continuum robots are often kinematically redundant and highly nonlinear. The lack of sufficient discrete joints renders their kinematic analysis more complex than their rigidlink counterparts. Contributions on kinematic modeling of continuum robots can be distinguished from two approaches: qualitative and quantitative approaches. All of them are based on the available information to describe the robot's behaviors. Qualitative approaches consist of dividing the parametric space into many classes according to the functioning modes. The mathematical and approximative relations between the effects (observation of experts, sensor measurements and statistical data) and causes (input references) are determined using learning techniques. For quantitative approaches, the existing methods are also called model-based methods, which are induced from the physical model (Kinematic, Differential or Dynamic) of the robot in normal functioning mode.

Regarding model-based methods, various approaches have been proposed for solving the kinematic problem of continuum robots. Hannan et al. [START_REF] Hannan | Novel kinematics for continuum robots[END_REF] used differential geometry and the Denavit-Hartenberg procedure to develop an intuitive kinematic model of a continuum manipulator for planar and spatial scenarios. Generally, this geometrical approach considers the structural assumption of constant curvature of the backbone [START_REF] Webster | Design and kinematic modeling of constant curvature continuum robots: A review[END_REF]. Jones and Walker [START_REF] Jones | Kinematics for multisection continuum robots[END_REF] extended this result by considering multiple sections of the manipulator. The same approach was used in [START_REF] Bardou | Control of a multiple sections flexible endoscopic system[END_REF] to control a flexible endoscopic system, and in [START_REF] Zhao | Design and analysis of a kind of biomimetic continuum robot[END_REF] to describe the kinematics of a tendon-based manipulator. Godage et al. [START_REF] Godage | Novel modal approach for kinematics of multisection continuum arms[END_REF] presented a new three dimensional kinematic model for multi-section continuum arms using a novel shape function-based approach [START_REF] Godage | Shape function-based kinematics and dynamics for variable length continuum robotic arms[END_REF], which incorporates geometrically constrained structure of the arm. Another interesting approach is the one presented in [START_REF] Rolf | Constant curvature continuum kinematics as fast approximate model for the bionic handling assistant[END_REF]; where the authors used torus segments to represent the sections of the Bionic Handling Assistant (BHA) manipulator. Escande et al. [START_REF] Escande | Geometric modelling of multisection bionic manipulator: Experimental validation on robotinoxt[END_REF] developed the forward kinematic model of the Compact Bionic Handling Assistant(CBHA) based on the constant curvature assumption.This singularity can be well handled as studied in [START_REF] Reiter | Learningbased configuration estimation of a multi-segment continuum robot[END_REF] and [START_REF] Wei | Performance evaluation for multi-arm manipulation of hollow suspended organs[END_REF]. A geometrical approach to solve the inverse kinematics of continuum robots was proposed and applied to OctArm in [START_REF] Neppalli | A geometrical approach to inverse kinematics for continuum manipulators[END_REF], where the endpoint for all sections of a multi-section arm are assumed to be known. However, most of the aforementioned modeling techniques lead to less accurate models especially when precision is needed in the robot task, such as handling. In addition, it is often difficult to deduce the inverse kinematics directly from the forward kinematics. Usually, we make use of numerical methods (least squares [START_REF] Hollerbach | The calibration index and taxonomy for robot kinematic calibration methods[END_REF], Newton-Raphson methods [START_REF] Goldenberg | A complete generalized solution to the inverse kinematics of robots[END_REF], etc.) which are computationally intensive.

Concerning qualitative approaches, Giorelli et al. [START_REF] Giorelli | A feed-forward neural network learning the inverse kinetics of a soft cable-driven manipulator moving in three-dimensional space[END_REF] approximated the Inverse Kinematic Model (IKM) of a soft manipulator using a feed-forward Neural Networks (NN). To control a soft extensible manipulator, Braganza et al. [START_REF] Braganza | A neural network controller for continuum robots[END_REF] used NN to compensate for the dynamic uncertainties. Rolf et al. [START_REF] Rolf | Efficient exploratory learning of inverse kinematics on a bionic elephant trunk[END_REF] introduced goal babbling approach to solve the inverse kinematic model of the Bionic Handling Assistant (BHA) robot. Melingui et al. [START_REF] Melingui | Qualitative approach for inverse kinematic modeling of a compact bionic handling assistant trunk[END_REF] used NN in distal supervised learning scheme to approximate the IKM of the CBHA robot. Then, an adaptive neural network control of the CBHA robot is proposed in [START_REF] Melingui | Adaptive neural network control of a compact bionic handling arm[END_REF]. Generally, qualitative approaches lead to more approximate accurately complex models. However, the higher the degree of freedom robots becomes significant (beyond 10 DOF) over the calculation of their kinematic models becomes difficult or long to obtain.

Concerning the modeling of kinematic behaviors of continuum manipulators as a series of parallel robots, Mahl et al. [START_REF] Mahl | Forward kinematics of a compliant pneumatically actuated redundant manipulator[END_REF] proposed a forward kinematic model of a continuum robot considering each section as a series of 3-DOF parallel mechanisms. Rongjie et al. [START_REF] Kang | Kinematic model and inverse control for continuum manipulators[END_REF] solved the problem of multiple solutions of the inverse kinematic model of continuum manipulators using a spline interpolation method. The latter is used to generate the backbone curve of the manipulator assuming that the proximal and distal parts of the continuum manipulator remain straight, and the bends only occur on the middle part of the manipulator. Chirikjian and Burdick [START_REF] Chirikjian | A modal approach to hyperredundant manipulator kinematics[END_REF] developed a modal-approach for hyper-redundant manipulator kinematics. The hyper-redundant manipulators are abstractly represented by backbone curves that either exactly or closely capture the important macroscopic geometric features of the robot. An optimal curve is used to generate the continuous backbone curve. The optimality criteria are used to choose the curve that satisfies task constraints, while optimizing additional criterion. A serial-parallel platforms have been modeled as a basic element of a hyper-redundant robot in Chibani et al [START_REF] Chibani | Generating optimal reference kinematic configurations for hyperredundant parallel robots[END_REF]. An emulation to the CBHA robot has been produced. Espinoza et al [START_REF] Espinoza | Inverse kinematics of a 10 dof modular hyper-redundant robot resorting to exhaustive and error-optimization methods: A comparative study[END_REF] proposed an inverse kinematic modeling for a 10 DOF hyper-redundant robot based on error optimization algorithms.

An alternative to model the kinematic behavior of robots is to combine the two previous approaches in order to take the best of them. In literature, the use of qualitative approaches to improve the performance of quantitative models can be found in [START_REF] Karlik | An improved approach to the solution of inverse kinematics problems for robot manipulators[END_REF]- [START_REF] Guez | Solution to the inverse kinematics problem in robotics by neural networks[END_REF], where neural networks were used to approximate a large number of robot manipulator models in presence of nonlinearities. In our previous work, A multilayer neural network has been used in [START_REF] Lakhal | Forward kinematic of a class of continuum bionic handling arm[END_REF] to approximate the solutions of the inverse kinematic equations of the CBHA robot, where the time-allocation for the learning process was considerable, due to the complex kinematic of the robot. A qualitative approach has also been used to approximate the CBHA kinematics [START_REF] Melingui | Qualitative approach for inverse kinematic modeling of a compact bionic handling assistant trunk[END_REF], [START_REF] Melingui | Qualitative approach for forward kinematic modeling of a compact bionic handling assistant trunk[END_REF] and to develop a kinematic controller [START_REF] Melingui | Adaptive neural network control of a compact bionic handling arm[END_REF], [START_REF] Melingui | Compact bionic handling arm control using neural networks[END_REF].

Hybrid approach introduced in this paper is an extension of initiated works on CBHA modeling [START_REF] Lakhal | Forward kinematic of a class of continuum bionic handling arm[END_REF] and [START_REF] Melingui | Adaptive neural network control of a compact bionic handling arm[END_REF], to a real-time solving in the CBHA modeling is extended in this proposed work by adding a real-time resolution and implementation in case of trajectory tracking. A methodology of kinematic modeling and synthesis of its approximate solutions is proposed for the case of continuum manipulator of type CBHA. The modeling approach is issued from a quantitative modeling of parallel rigid manipulators. Structurally, the CBHA is assimilated to a series of parallel manipulator under the following assumptions: the CBHA's backbone represents a series of vertebrae, an inter-vertebra is modeled as three Universal-Prismatic-Spheric and one Universal-Prismatic (3UPS-1UP) joints, the robot torsion is not considered because the robot is driven at low velocities, also its memory effect of the material. The CBHA's kinematic models are deduced merely from the inverse kinematic equations of each intervertebra and generalized for whole the hyper-redundant robot. The obtained inverse kinematic equations are highly nonlinear and mathematically intractable for the CBHA manipulators. For this issue, a multilayer neural network is used to provide approximated solutions of the IKE which can be used for realtime implementation. The advantages of the proposed hybrid modeling-solving approach compared to quantitative methods is that the obtained models are more accurate and compu-tationally inexpensive. Compared to qualitative approaches, the proposed approach can be implemented for platforms including a significant number of DoFs; because the training database is deducted directly using the inverse kinematic equations. This avoids to operate the robot for a long-period for learning database.

The remainder of the paper is organized as follows: the next Section describes the CBHA manipulator. Section III develops the IKE of the inter-vertebra which is considered as 3UPS-1UP joints. Section IV and Section V present respectively the forward and inverse kinematic modeling of the CBHA using a multilayer neural network for solving the nonlinearity of the inter-vertebra. Section VI describes the obtained experimental results and section VII gives a conclusion and future work.

II. COMPACT BIONIC HANDLING ASSISTANT MANIPULATOR

The CBHA manipulator is mounted on an omnidirectional mobile platform named Robotino. This combination forms a mobile-bionic manipulator called RobotinoXT, as shown in Fig. 1. The CBHA consists of 2 sections made from elastic material, a rotational wrist, and a compliant gripper as depicted in Fig. 2. The key feature of the CBHA is the flexibility of its sections formed by 3 polyamide tubes, controlled by electropneumatic actuators. These tubes change their posture when air flow is circulating through them. Since each section is formed by 3 tubes, a bending section can be controlled by applying differential pressures in the tubes. If equal pressures Fig. 2. The CBHA of the RobotinoXT are applied to each tube, the manipulator extends in a straight line. This extension is limited by an inextensible cable placed in the middle of the backbone, as shown in Fig. 3. The elongations of each tube can be measured from wire-potentiometer sensors placed along each tube. The voltages provided by wirepotentiometer sensors are proportional to the extension of each tube l i,j , where i = 1, .., 3 and j = 1, 2 correspond to the wirepotentiometer and section number, respectively. Finally, the CBHA is a class of continuum manipulators, with a continuous shape and two sections. Each section has 3 DoF of mobility and under actuated.

III. CBHA MANIPULATOR KINEMATICS

As we have stated in the introduction section, the kinematic behaviors of continuum manipulators can be reproduced under functional and structural assumptions. In this section, the assumptions used for the kinematic modeling are first introduced, and the IKE are derived thereafter.

A. Assumptions

The kinematic models of the CBHA are developed under some assumptions, described below:

• The manipulator is considered as a series of N = 17 vertebrae. • An inter-vertebra is flexible and non deformable with a 3DoF mobility. It is modeled with a 3UPS-1UP joints. • The manipulator's yaw motion is not allowed wih the existing mechanical links between the tubes. Based on these assumptions, a parallel robot composed by two rigid platforms and 3 DoF of mobility is used to emulate the behavior of each inter-vertebra. Knowing that the robot comprises 17 vertebrae, the entire CBHA can be emulated by a series of 16 parallel robots each comprising 3 DoF. Using such modeling, CBHA can be considered as a 48 DoF system. That is why the CBHA robot has the properties of hyper-redundant robots. The schema of an inter-vertebra is given in Fig. 4. It consists of a lower and a upper vertebrae connected by three limbs with identical kinematic configuration, and a central leg. The limbs are modeled by a kinematic configuration of type UPS, in which only the prismatic joints are active allowing to control the position and orientation of the upper vertebra relative to the lower vertebra. q m,k represents the variation of length of prismatic joint, where m = 1, .., 3 is the number of active joints and k = 1, .., N -1 is the number of inter-vertebrae. The central leg is modeled by a kinematic configuration of type PU located in the center of an intervertebra. It is considered as a passive joint.

B. Inverse Kinematic Equation of the CBHA's Inter-Vertebra

The IKEs of an inter-vertebra are obtained by calculating the joint variables q m,k corresponding to the pose (position and orientation) of the upper vertebra's centre relative to the lower vertebra frame. In the case of CBHA manipulator, the intervertebra is considered as a 3-DoF parallel robot, because of movements constraints related to the passive kinematic chain UP. In fact, the rotation with respect to the z axis, denoted by Rot(z, Φ k ), and the translations relative to the x and y axes denoted by T rans(x, X k ) and T rans(y, Y k ), respectively, are not considered, because it does not exist a movement on these axis. Only the translation along the z axis is possible which is denoted by T rans(z, Z k ). Hence, the IKEs can be formulated as follows:

q m,k = f (Z k , ψ k , θ k ) (1)
where the angles θ k and ψ k indicate pitch and roll angles, respectively.

k {O k , x k , y k , z k } is attached to the lower vertebra of origin O k , centre of the triangle A 1,k A 2,k A 3,k .
Knowing that the entire shape of the CBHA is conical, it is necessary to determine the circumcircle radius r n of the considered vertebra, where n = 1, .., N is the number of vertebrae. Let r max and r min , respectively the radius of the base and the apex of the backbone, the radius of each vertebra r n can be calculated by:

r n = k N (r min -r max ) + r max (2)
where N = 17 is the number of vertebrae. Thus, the coordinates of the three connection points are given by the coordinates (3), relative to the centre of the n th vertebra.

A 1,k =   -r k 0 0   ; A 2,k =   1 2 r k √ 3 2 r k 0   ; A 3,k =   1 2 r k - √ 3 2 r k 0   . (3)
The length of the prismatic joint for the active kinematic chain of each UPS is represented by: q m,k with m = 1, .., 3 and k = 1, .., N -1. The coordinates of the connection point A m,k+1 relative to the frame A m,k are given as follows:

Q m,k 1 = k k+1 T A m,k+1 1 - A m,k 1 (4)
where, k k+1 T is the transformation matrix of the upper vertebra frame relative to the lower vertebra. Q m,k is 3x1 vector like A m,k+1 .

k k+1 T =     Cθ k Sθ k Sψ k Sθ k Cψ k 0 0 Cψ k -Sψ k 0 -Sθ k Sψ k Cθ k Cθ k Cψ k Z k 0 0 0 1     . (5)
The notations C and S mean cosine and sine functions, respectively. Thus, the prismatic variable q m,k is equal to the distance between the connection points A m,k+1 and A m,k :

q 2 m,k = Q m,k T Q m,k (6)
Therefore, after introducing of (3) into (4), the equation ( 6) can be rewritten in the format of (1):

q 1,k = sqrt Z 2 k + 2Z k r k+1 Sθ k -2r k+1 r k Cθ k + r 2 k+1 + r 2 k , (7)
q 2,k = sqrt Z 2 k + Z k r k+1 √ 3Cθ k Sψ k -Sθ k -r k+1 r k √ 3 2 Sθ k Sψ k + 3 2 Cψ k + 1 2 Cθ k +r 2 k+1 + r 2 k , (8)
q 3,k = sqrt Z 2 k -Z k r k+1 √ 3Cθ k Sψ k + Sθ k + r k+1 r k √ 3 2 Sθ k Sψ k -3 2 Cψ k -1 2 Cθ k + r 2 k+1 + r 2 k . (9)
IV. FORWARD KINEMATIC MODEL OF THE CBHA In this section, the Forward Kinematic Model (FKM) of an inter-vertebra is first derived from the IKE using neural networks approximation. The FKM of the entire CBHA's backbone is deduced thereafter.

A. Forward Kinematic Model of the CBHA's inter-vertebra

The relation between the joint configuration q m,k of an inter-vertebra and the parameters of the upper-vertebra (Z k , θ k , and ψ k ) relative to the lower vertebra frame is the FKM of the parallel robot module 3UPS-1UP, defined as:

[Z k , θ k , ψ k ] = f -1 (q m,k ). ( 10
)
The pose [Z k , θ k , ψ k ] of the upper vertebra frame corresponding to a particular joint configuration q m,k relative to the lower vertebra frame can be obtained by solving the IKEs (( 7), [START_REF] Walker | Continuum robot arms inspired by cephalopods[END_REF], and ( 9)). However, as we can notice, these equations are highly nonlinear. Numerical techniques such as least square, Newton-Raphson methods [START_REF] Arshad | Solution of forward kinematics model of six degrees of freedom parallel robot manipulator[END_REF] which are used to provide approximated solutions are usually computationally intensive. In this work, an approximated solution is provided by a Multilayer Neural Network (MNN). The approximation of the solution can be summarized to a regression problem with non-noisy data, because the IKEs are supposed to be well identified.

Neural Network (NN) is a technique that models mechanisms based on learning and problem solving functions of the human brain. NNs possess many useful properties and capabilities [START_REF] Haykin | Neural networks: a comprehensive foundation[END_REF] in terms of nonlinearity approximation, input-output mapping, adaptive controlling, very-large-scale integration, implementability, etc. In NN model, the signals flow consecutively through the different layers from the input to the output layer. The intermediary layers are called hidden layers. In each layer, each elementary unit calculates a scalar product between a vector of weights and the output vector given by the previous layer. The choice of the appropriate network architecture (i.e. number of hidden layers, number of nodes in each layer, activation function, etc.) has to be identified during the training process [START_REF] Haykin | Neural networks: a comprehensive foundation[END_REF]. After the choice of the NN architecture, the values of the weights are adjusted via the training process (back-propagation, delta rule, etc.). The performance of the obtained model is closely related to the type and quality of the input-output data pairs, and noisy data can significantly degrade its performance.

Multilayer Perceptron (MLP) is one of the typical examples of Artificial NNs and consists of an input layer, some hidden layers, and an output layer. NNs are considered as "universal approximators"; Hornik et al. [START_REF] Hornik | Multilayer feedforward networks are universal approximators[END_REF] proved that an MLP with two layers of weights and sigmoid activation functions can approximate any nonlinear functional relationship (mapping) with an arbitrary accuracy, provided that enough of hidden neurons are available. The solution of the IKEs based on NN approximation offers advantages such as reduced computational resources and rapid execution for real-time purposes.

In this work, each bending section of the CBHA is divided into a finite number of inter-vertebrae. However, the measurement of the length of each inter-vertebra increases the implementation cost. Ideally for the case of the CBHA, the measurement of the length of each inter-vertebra requires 48 length sensors for each section, 92 length sensors for the entire CBHA's backbone. An alternative is to fragment, when possible the wire-cable length sensor to provide the length of each parallel platform module. A fragmentation based on the minimum potential energy approximation has been proposed in [START_REF] Mahl | A variable curvature continuum kinematics for kinematic control of the bionic handling assistant[END_REF] for the BHA robot. However, the latter is still based on assumptions: disregard of the potential energy due to gravitation, potential energy of the trunk is equal to energy stored in its spring-like bellows actuators, which are not always verified. Referred to CBHA, the length of each inter-vertebra is different due to tapered structure of the CBHA, with tilted position on the mobile platform (Robotino). However, since the length of an inter-vertebra is a fraction of the tube length, the elongation of each inter-vertebra at any time represents a percentage of the total length of the tube. This percentage can be obtained by considering the minimum and the maximum elongations of each inter-vertebra listed in Table I. The first and the fourth rows represent the index of the vertebra. The second and the fifth rows give the minimum elongations, while the third and the sixth give maximum elongations. The percentage distribution of the inter-vertebra's length for the entire CBHA is depicted in Fig. 6. For FKM modeling, each section of the CBHA is modeled by one NN model of 24-inputs to 24-outputs. The lengths of the inter-vertebrae (q 1,k , q 2,k , q 3,k ) are considered as inputs, while the variable Z k , the pitch (θ k ), and the yaw (ψ k ) angles are considered as outputs. k = 1, 2, ..., 8 for the first section, and k = 9, 10, ..., 16 for the second sections.

 min =-30°≤  k ≤  max =30°  min =-30°≤  k ≤  max =30° 11mm≤ Z k 1 ≤13mm 10mm≤ Z k 2 ≤12mm (q 1,k , q 2,k , q 3,k )=f(Z k , r k , r k+1 ,  k ,  k )
q min ≤ q m,k ≤ q max Save Yes Non

Fig. 7. Database of learning generation

As it is listed in Table I, each inter-vertebra length is bounded. These bounds allow fixing the range of the pitch (θ k ) and the yaw (ψ k ) angles. In fact, the variables Z k , θ k , and ψ k are initialized with a large range, as shown in Fig. 7. According to Fig. 7, the IKEs are computed, and the samples of input-output pairs included in the range of the lengths of the inter-vertebrae (q 1,k , q 2,k , q 3,k ) are saved. A learning base of 30071 samples is obtained; 11559 samples for the first section and 18512 samples for the second section, respectively. The learning base is divided randomly: 70% for the training set, 15% for the validation set, and 15% for the test set. The training set is used during the learning phase and the test set is only employed to assess the performance of the neural network model. The validation set is used during the learning phase.

In order to minimize the Mean Square Error (MSE) calculated in the validation set, the weight matrices are adjusted by means of the back-propagation descent method, including the momentum term. For an optimal generalization of neural network models and to avoid over-fitting, the early-stopping method for training is implemented. The latter requires that after a period of training (epochs) using the training set, the weight matrices of the NN are fixed, and the NN operates in the forward mode using the validation set. The process is repeated until the MSE on the validation set reaches its minimum value. In order to empirically select the best model for each regressors, the value of each parameter was varied in a given predefined range according to a grid search over the learning base. A MLP of two layers is used for each CBHA's section, and it is tested with 2 up to 20 neurons (with a step of 2 neurons) in each hidden layer. The optimal MLP architecture is obtained for an architecture with 18 neurons in each hidden layer for the two sections by taking the MSE in the validation set as a performance criterion . The assessment of the trained MLP in terms of MSE on the test-samples yields the values reported in Table II. In the view of the obtained performance, we notice that each NN model approximates the variables Z k , θ k , and ψ k with a good degree of accuracy. In next subsection, the FKM of the entire CBHA is deduced. The modeling process of the FKM is summarized in Fig. 9. The lengths provide by the tube-length's sensors are divided with respect of the percentage distribution of the intervertebrae. The elongations q m,k obtained are used as inputs of the NN models. NN1 model provides the predicted pose of the vertebrae of the first section, while those of the second section are provided by NN2 model. The Cartesian coordinate of the tip arm {X t , Y t , Z t } is obtained by using the transformation matrices k k+1 T , for k = 1, .., N -1.

B. FKM of the CBHA manipulator

V. INVERSE KINEMATIC MODEL OF THE CBHA

In this section, the IKM of the CBHA is presented. As we stated at Section III, the entire CBHA is emulated by a series of 16 parallel robots each comprising 3 DoF. Using such modeling, the CBHA can be considered as a 48 DoF manipulator. Numerically, it is sufficient to calculate the IKM of each vertebra system, making the relation between the center of the upper-platform with the joint coordinates of the inter-vertebra. It comes more complicated for deducing the numerical relationship between the top of the CBHA and the 48 joint coordinates. Based on the approximation and generalization capabilities of the learning-based techniques the IKM can be solved. The principle is to compute the corresponding lengths of each inter-vertebra from a given Tool Center Position (TCP). At the end, the IKM of the CBHA is obtained by summing the elongations of the inter-vertebrae.

In contrast to FKM, four MLPs are used to approximate the IKM of the CBHA. The first one approximates the endposition {X s1 , Y s1 , Z s1 } of the first section from the Cartesian coordinates of the CBHA's tip {X t , Y t , Z t }. The last three models use the position of the arm's tip {X t , Y t , Z t } and the end-position of the first section {X s1 , Y s1 , Z s1 } as inputs, and provides Z k , θ k , and ψ k as outputs. III shows the results achieved by each neural network model on the test set samples. The performance is less accurate for the endposition of the first section. This is mainly due to CBHA hyper-redondancy, because many solutions of the CBHA's tip are possible for the same end-position of the first section. Nevertheless, the performance achieved remains acceptable. To select a particular inverse kinematics function, a squared penalty term is added to the objective function of the neural network. The cost functional yields:

J = 1 2 (X d -X) T (X d -X) + λ 1 2 X 2 (11)
where X and X d are respectively the predicted and the desired position. . denotes the Euclidean norm. In [START_REF] Wu | Convergence of batch bp algorithm with penalty for fnn training[END_REF], it has been demonstrated that, if the coefficient λ is larger, X becomes small. The penalty term λ provides a possibility to control efficiency the magnitude of X. This allows selecting a particular inverse solution. The whole MSE listed in Table III and Table II are the errors obtained on the test samples. The database has been divided in three sets: training, validation, and test. During the NN training, the matrices are updated using the MSE calculated at the same stage. MSE in validation set is used for stopping the training, and the MSE in the test set is used for assessing the performance of the NN model.

VI. EXPERIMENTAL RESULTS

This section focuses on the validation of the developed kinematic models. Regarding the comparative study, performances of modeling approaches vary from one robot to another [START_REF] Rolf | Efficient exploratory learning of inverse kinematics on a bionic elephant trunk[END_REF], [START_REF] Giorelli | A feed-forward neural network learning the inverse kinetics of a soft cable-driven manipulator moving in three-dimensional space[END_REF], [START_REF] Braganza | A neural network controller for continuum robots[END_REF], [START_REF] Escande | Kinematic calibration of a multisection bionic manipulator[END_REF], [START_REF] Jones | Practical kinematics for real-time implementation of continuum robots[END_REF]. They depend on the structure of the continuum manipulator, the state of its actuators, and its geometry (sizes, shape). Hence, a direct comparison of these approaches can be done, if and only if the latter can be implemented on the same manipulator. This paper discusses only recent contributions done on the CBHA and the BHA manipulators. The structure is almost the same, the difference being that the CBHA possesses two sections, while the BHA has three sections. In this section, the optical 3-D stereo-vision system is first presented for the real-time trajectory tracking. The validations of the FKM and IKM are presented thereafter. The section ends with a discussion.

A. Trajectory Generation

For generating the trajectory of the CBHA's tip, three reflective markers are attached to each head of its two sections. The reflective markers are tracked by a stereo-vision system in order to reconstruct the trajectory generated by each head. The desired trajectory of the tip of the manipulator is generated by pressure variation. The process of the trajectory tracking is presented in Fig. 11. The trajectory generated by the head of the second CBHA's section is considered as desired trajectory. The tracking set-up is based on the motion capture system OptiTrack by NaturalPoint, and consists of a set of 10 infrared cameras capable of an accurate 3D tracking of a point cloud within their combined visible workspace, in our case approximately a cube of 1 meter side. After calibration, the system has a precision of 0.3 mm for measuring the 3D displacements of the two sections, as well as the base point considered as the inertial frame of the system. The system has already been used in [START_REF] Duriez | Control of elastic soft robots based on real-time finite element method[END_REF].

B. Validation of the forward kinematic model of the CBHA.

The forward kinematics of the CBHA are validated by following the experimental setup presented in Fig. 12. The desired posture of the CBHA is generated by applying a Results of the FKM validation are represented in Fig. 13. The trajectories generated by the FKM and the stereo-vision system are depicted in Fig. 13 (a). The associated Euclidean errors are represented in Fig. 13-(b)-(c)-(d).

Refer to Euclidean errors presented in Fig. 13, it can be concluded that the proposed FKM is able to predict the CBHA's tip pose with positioning errors of less than 8mm.

C. Validation of the inverse kinematics of the CBHA

The validation of the inverse CBHA's kinematics is proceeded as shown on Fig. 14. The desired posture of the CBHA is generated by applying a set of the desired pressures to CBHA's tubes. The desired positions of CBHA's tip is applied to IKM, and the predicted lengths generated by the IKM are used as input to length-pressure controller. The pressures generated by the controller are applied to CBHA robot by mean of the internal PID controllers. The elongations The outputs of the FKM constitute the predicted positions. These positions are compared to those provided by the stereo vision system. Note that the CBHA system embeds six internal PID controllers allowing the control of the pressure in each tube. The length-pressure controller has been developed in our previous works [START_REF] Melingui | Compact bionic handling arm control using neural networks[END_REF]. of the CBHA can also be deduced from the positions and orientations of the vertebrae, as shown in Fig. 16. The predicted CBHA's postures obtained by using the IKM and FKM models are represented in Fig. 16-(a)-(b). The real CBHA's posture obtained by using the values provided by the length sensors is represented in 16 (c). We notice that both predicted CBHA's postures are closed to the real CBHA's posture, and those obtained by FKM function are the closest.

D. Discussions

In the view of the obtained results, the proposed kinematic models achieve good performance. The proposed FKM is able to predict the CBHA's tip pose with positioning errors of less than 8mm, which represents 2.2% of the total length of the CBHA. The IKM predicts the CBHA's lengths which lead to the tip pose with positioning errors of less than 11mm, which represents 3% of the total length of the CBHA. The CBHA includes memory effects and non-stationary behaviors due to trunk's materials (polyamide). When the CBHA's trunk spends long time in bending-posture, it does not return to its initial posture when it is released. These non-stationary behaviors appear mainly in high CBHA dynamics, and cannot be coped by a simple kinematic controller [START_REF] Melingui | Compact bionic handling arm control using neural networks[END_REF]. Remaining models' errors are expected to result from torsional effects which have been neglected in modeling process. Table IV lists recent contributions in kinematic modeling of continuum robots. We notice that in the case of the CBHA modeling and real-time implementation, learning-based methods achieve good performance, while quantitative methods are less accurate. This lack of performance should result from modeling assumptions inherent in quantitative approaches. The proposed hybrid approach is intermediary to previous approaches, and shows average performance. The accuracy of tracking tasks by the CBHA is shown through the video [START_REF] Melingui | His research interests include system of systems, modeling, and supervision of mechatronics systems applied to robotics and intelligent transport. He is[END_REF], for online targets tracking. These targets correspond to the centre of tool coordinates of an external rigid robot manipulator which communicate with the CBHA through the network inside the CBHA's workspace. The aim is that the rigid manipulator transfers to the CBHA the coordinates of its centre of tool at the end of each trajectory, calculated in the local CBHA's frame. Then, the CBHA free-load tracks the imposed target with a good accuracy.

VII. CONCLUSIONS

In this paper, we presented a methodology of kinematic modeling calculation of a class of continuum manipulator, including its approximate modeling for real-time solving and implementation. The proposed modeling approach is able to model hyper-redundant or continuum manipulators that consist of multiple bending sections. The difficulty of solving the inverse kinematic equations is circumvented by providing approximated solutions based on neural network approach. Both the forward and inverse kinematics are derived from the inverse kinematic equations. The obtained kinematic models are computationally inexpensive and can be easily implemented in real-time. They can reconstruct the tool center position of a free load CBHA with an interesting degree of accuracy. The validation of the kinematic models using a 3-D stereo vision system for trajectory tracking demonstrate the efficiency of the proposed modeling approach. In future work, dynamic behaviors of the CBHA in interaction with external environment can be considered for control purpose.

Fig. 1 .

 1 Fig. 1. RobotinoXT

Fig. 3 .

 3 Fig. 3. Length Measurements on the CBHA

Fig. 4 .

 4 Fig. 4. Schematic of an inter-vertebra modeled as parallel robot with 3UPS-1UP

Fig. 5 .

 5 Fig. 5. Configuration of spatial 3UPS-1UP manipulator

Fig. 6 .

 6 Fig. 6. Elongation of the inter-vertebrae during movement

Fig. 8

 8 Fig.8depicts the different frames used to describe the kinematics of the CBHA:

Fig. 8 .Fig. 9 .

 89 Fig. 8. The associated frames of the CBHA

Fig. 10 .

 10 Fig. 10. Algorithm for Inverse kinematic modeling

Fig. 10

 10 Fig.10summarizes the process of the IKM solving. The same learning base developed for the Forward kinematics is used to derive the inverse kinematics. TableIIIshows the results achieved by each neural network model on the test set samples. The performance is less accurate for the endposition of the first section. This is mainly due to CBHA hyper-redondancy, because many solutions of the CBHA's tip are possible for the same end-position of the first section. Nevertheless, the performance achieved remains acceptable. To select a particular inverse kinematics function, a squared penalty term is added to the objective function of the neural network. The cost functional yields:

Fig. 11 .

 11 Fig. 11. Stereo-vision system for trajectory generation

Fig. 12 .

 12 Fig. 12. Architecture of the FKM validation

Fig. 13 .

 13 Fig. 13. Kinematic Validation: (a) Desired and Estimated trajectories; (b) Euclidean error in X axis; (c) Euclidean error in Y axis; and (d) Euclidean error in Z axis.

Fig. 14 .

 14 Fig. 14. Architecture of the kinematic models validation

Fig. 15 Fig. 15 .

 1515 Fig. 15. Inverse kinematic Validation: (a) Desired and IKM trajectories; (b) Euclidean error in X axis; (c) Euclidean error in Y axis; and (d) Euclidean error in Z axis.

Fig. 16 .

 16 Fig. 16. Posture of the CBHA: (a) Estimated posture with the IKM ; (b) Estimated posture with the FKM; and (c) real posture of the CBHA.

•

  The first frame {X base , Y base , Z base } is attached to the base of the CBHA robot; • The second frame {X s1 , Y s1 , Z s1 } is attached to the top of the first bending section; • And the last one {X s2 , Y s2 , Z s2 } is attached to the top of the second bending section; The coordinates of the frames {X s1 , Y s1 , Z s1 } and {X t , Y t , Z t } are given relative to the base frame {X base , Y base , Z base }, while those of the frame {X s2 , Y s2 , Z s2 }

	are	expressed	relative	to	frame
	{X

s1 , Y s1 , Z s1 }.

TABLE IV RECENT

 IV CONTRIBUTIONS IN KINEMATIC MODELING OF CBHA AND BHA ROBOT

	Robots	Characteristics	Modeling Method	Kinematic modeling
	CBHA	Continuous Shape, Structure: Soft, 2 sections, 3 tubes per section, 17 vertebras,	Hybrid approach: Geome-try and Neural Network, Proposed approach	Forward (FKM), accuracy: 8 mm; Inverse (IKM), accuracy: 11 mm; Validation: Simulation and real-time experiments.
		3 DoF per inter-vertebra, Pneumatic actuation,
		Length: 0.50 m.	Quantitative: Geometrical-	Forward (FKM), accuracy: 8 mm;
			based approach Escande et	Validation: Simulation and real-
			al. [43]	time experiments.
			Qualiative: Network, Melingui et Neural al. [24], [36]	Forward (FKM), accuracy: 4 mm; Inverse (IKM), accuracy: 5 mm; Validation: Simulation and real-time experiments.
	BHA	Continuous shape, Structure: Soft, 3 sections, 3 tubes per section, Pneumatic actuation,	Quantitative: Geometrical-based approach, Mahl et al. [41]	Forward (FKM), accuracy: 20 mm, Inverse (IKM), accuracy: 50 mm,Validation: Simulation and real-time experiments
		30 vertebrae, 3 DoF per inter-vertebra, Length: 1.00 m	Qualitative: Goal babbling	Forward (FKM), accuracy: 5 mm; Inverse (IKM), accuracy: 6 mm;
			learning, Rolf et al. [2]	Validation: Simulation and real-
				time experiments.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TMECH.2015.2490180, IEEE/ASME Transactions on Mechatronics


Stefanie Royek

Martin Bayer

Jens Pfannstiel

Jürgen Pleiss

Gwyneth Ingram

Annick Stintzi
email: annick.stintzi@uni-hohenheim.de

Andreas Schaller
email: andreas.schaller@uni-hohenheim.de

Processing of a plant peptide hormone precursor facilitated by posttranslational tyrosine sulfation

Keywords: Biological Sciences, Plant peptide hormone, post-translational modification, proteolytic processing, tyrosine sulfation

Most peptide hormones and growth factors are matured from larger inactive precursor proteins by proteolytic processing and further post-translational modification. Whether or how posttranslational modifications contribute to peptide bioactivity is still largely unknown. We address this question here for TWS1 (Twisted Seed 1), a peptide regulator of embryonic cuticle formation in Arabidopsis thaliana. Using synthetic peptides encompassing the N-and C-terminal processing sites and the recombinant TWS1 precursor as substrates, we show that the precursor is cleaved by the subtilase SBT1.8 at both the N-and the C-terminus of TWS1. Recognition and correct processing at the N-terminal site depended on sulfation of an adjacent tyrosine residue. Arginine 302 of SBT1.8 was found to be required for sulfo-tyrosine binding and for accurate processing of the TWS1 precursor. The data reveal a critical role for post-translational modification, here tyrosine sulfation of a plant peptide hormone precursor, in mediating processing specificity and peptide maturation.

Main Text Introduction

In plants and animals alike, peptide hormones and growth factors are derived from larger precursor proteins by post-translational processing and modification. Peptides with different, even antagonistic activities may be produced from the same precursor, highlighting the relevance of post-translational maturation events [START_REF] Zhang | Obestatin, a peptide encoded by the ghrelin gene, opposes ghrelin's effects on food intake[END_REF][START_REF] Steiner | Evidence for a precursor in the biosynthesis of insulin[END_REF][START_REF] Chrétien | 60 YEARS OF POMC: From the prohormone theory to proopiomelanocortin and to proprotein convertases (PCSK1 to PCSK9)[END_REF][START_REF] Stührwohldt | Regulation of plant peptide hormones and growth factors by post-translational modification[END_REF]. Secretion, stability, and metabolic clearance of peptide hormones may also be regulated post-translationally [START_REF] Stührwohldt | Regulation of plant peptide hormones and growth factors by post-translational modification[END_REF]. The activity of human cholecystokinin and of phytosulfokine (PSK) and Casparian strip Integrity Factors (CIFs) in plants, for example, depends on post-translational tyrosine sulfation to facilitate receptor binding and activation [START_REF] Mobbs | Structures of the human cholecystokinin 1 (CCK1) receptor bound to Gs and Gq mimetic proteins provide insight into mechanisms of G protein selectivity[END_REF][START_REF] Okuda | Molecular mechanism for the recognition of sequence-divergent CIF peptides by the plant receptor kinases GSO1/SGN3 and GSO2[END_REF][START_REF] Wang | Allosteric receptor activation by the plant peptide hormone phytosulfokine[END_REF].

Here, we show an additional role for tyrosine sulfation as a critical step in peptide hormone biogenesis. We found that sulfo-tyrosine determines the specificity of processing of the Twisted Seed 1 (TWS1) precursor. Only in its tyrosine-sulfated form is the precursor cleavage site recognized and accurately processed by subtilase (SBT) 1.8.

TWS1 belongs to the CIF family of sulfated peptides (Fig. 1A) that control Casparian strip and embryonic cuticle formation. The Casparian strip is an apoplastic diffusion barrier regulating water and ion homeostasis in roots. It forms a continuous impermeable band around radial cell walls of the endodermis, preventing apolastic movement of water and nutrients from the cortex into the root stele. Its integrity is controlled by CIF1 and CIF2 that are produced and secreted from the stele [START_REF] Doblas | Root diffusion barrier control by a vasculature-derived peptide binding to the SGN3 receptor[END_REF][START_REF] Nakayama | A peptide hormone required for Casparian strip diffusion barrier formation in Arabidopsis roots[END_REF]. The mature peptides diffuse extracellularly into adjacent tissues. Perception in the endodermis by the polarly localized GASSHO1/SCHENGEN3 (GSO1/SGN3)-SGN1 receptor complex triggers spatially controlled lignification for continued Casparian strip formation. Upon completion, the Casparian strip isolates the receptor complex from the source of the signal, and signaling is thus terminated [START_REF] Doblas | Root diffusion barrier control by a vasculature-derived peptide binding to the SGN3 receptor[END_REF][START_REF] Nakayama | A peptide hormone required for Casparian strip diffusion barrier formation in Arabidopsis roots[END_REF]. Integrity of the embryonic cuticle is monitored by a similar TWS1-dependent signaling pathway with, however, an interesting twist. Other than CIF1 and CIF2, TWS1 bears an inhibitory C-terminal extension (Fig. 1A). It is secreted from the embryo in this inactive form and passes through the nascent cuticle into the seed endosperm, where the Cterminal extension is cleaved off by the subtilase ALE1 [START_REF] Doll | A two-way molecular dialogue between embryo and endosperm is required for seed development[END_REF]. Fully matured TWS1 is then perceived by the GSO1 and GSO2 receptors at the embryo surface, and the activated receptors signal for continued cuticle production. Only when all the gaps are filled, the now impermeable cuticle separates the TWS1 precursor peptide from its activating protease, and the signaling pathway is shut off [START_REF] Doll | A two-way molecular dialogue between embryo and endosperm is required for seed development[END_REF].

Results and Discussion

In contrast to C-terminal processing and the critical role of ALE1 in the activation of TWS1, the proteases responsible for N-terminal maturation of CIF family peptides are still elusive. The Nterminus of CIFs is marked by a conserved DY motif (Fig. 1A) and sulfation of tyrosine in P2' (the second position downstream of the cleavage site) by tyrosyl protein sulfotransferase is required for bioactivity [START_REF] Doll | A two-way molecular dialogue between embryo and endosperm is required for seed development[END_REF]. Aiming to identify the N-terminal processing enzyme for TWS1, we searched publicly available databases for proteases that are co-expressed with ALE1 in developing Arabidopsis seeds (Dataset S1), and identified SBT1.8 as a possible candidate. Promoter:reporter gene analysis confirmed expression of SBT1.8 in the endosperm, in addition to weaker expression in the suspensor and provascular tissues of the embryo (Fig. 1B). Synthetic peptides encompassing the N-terminal (EDY; sulfo-tyrosine underlined) and C-terminal (HG) processing sites of TWS1 were tested as substrates for SBT1.8. The EDY-bond was efficiently cleaved by SBT1.8 resulting in the correctly processed N-terminal TWS1 peptide as the main cleavage product (Fig. 1C, upper panel). The second peptide was trimmed by SBT1.8 from the Cterminus up to the HG-bond at the C-terminal processing site (Fig. 1C, lower panel). The data suggest that SBT1.8 cleaves the TWS1 precursor at both the N-and the C-terminal processing sites to produce the mature TWS1 peptide.

In order to confirm this result, we tested the full-length TWS1 precursor produced as a GST fusion in E. coli as substrate of SBT1.8. A single cleavage product was observed for the GST-TWS1xSBT1.8 digest that was identical in size to that produced by ALE1 (Fig. 1D). Cleavage at the C-terminal HG-bond was confirmed by mass spectrometry (Fig. 1E). The data suggest that SBT1.8 acts redundantly with ALE1 in C-terminal maturation of TWS1. Surprisingly, however, processing at the N-terminal site, as seen for the synthetic peptide (Fig. 1C), was not observed in this experiment.

Puzzled by this discrepancy, we pondered whether sulfo-tyrosine that was included in the peptide substrate but lacking from the recombinant precursor might be important for cleavage by SBT1.8. Hence, we produced the tyrosine-sulfated precursor using an expanded genetic code in E. coli. Briefly, the relevant tyrosine codon within the GST-TWS1 expression construct was replaced by an amber stop codon. The construct was then co-expressed in E. coli with a suppressor tRNA recognizing the amber stop, and a matching aminoacyl-tRNA synthetase specific for sulfo-tyrosine, which was added to the growth medium [START_REF] Liu | Efficient expression of tyrosinesulfated proteins in E. coli using an expanded genetic code[END_REF]. Consequently, sulfotyrosine is co-translationally incorporated at the genetically encoded position, resulting in the production of full-length sulfated GST-sTWS1, in addition to a truncated protein terminated at the amber stop (Fig. 2A). Upon digestion with SBT1.8, the C-terminal cleavage product was observed again (Fig. 2A, blue arrow), in addition to a novel band corresponding in size to the N-terminal cleavage product (Fig. 2A, red arrow). The data suggest that sulfation of P2' tyrosine enables recognition of the N-terminal site and cleavage by SBT1.8. This conclusion was confirmed by use of sulfated and non-sulfated synthetic peptides to directly compare the effect of tyrosine sulfation on cleavage efficiency. SBT1.8 produced the correctly processed peptide from the sulfated substrate (sTWS1; Fig. 2B), with only a minor contamination of a second peptide, resulting from mis-cleavage after the sulfo-tyrosine residue. In contrast, for the non-sulfated substrate (TWS1), the mis-cleaved peptide was the main reaction product. This mis-cleavage was in fact also observed for the non-sulfated GST-TWS1 precursor protein, albeit only at longer incubation times, or higher concentration of the protease (Fig. 2C). The data suggest that tyrosine sulfation prevents mis-cleavage by increasing the specificity of processing, directing SBT1.8 to the correct N-terminal processing site.

We then used structural modelling to find out how the sulfate may contribute to substrate binding and cleavage site recognition. Figure 2D shows the sulfated substrate peptide modelled into the active site of SBT1.8. The model suggests a critical role for R302 in substrate binding as it interacts with the negatively charged sulfonate of the P2' sulfo-tyrosine residue (Fig. 2D, Movie S1, Dataset S2). Site-directed mutagenesis confirmed the relevance of R302 for substrate recognition. N-terminal cleavage of the GST-sTWS1 precursor was no longer observed when R302 was substituted with lysine, methionine, serine, glutamate, valine, or isoleucine (Fig. 2E). Importantly, activity of SBT1.8 per se was not affected by these mutations, as C-terminal cleavage was not impaired (Fig. 2E). The data indicate that R302 is required for cleavage at the correct N-terminal processing site.

However, R302 is not solely responsible for cleavage site recognition, as SBT1.1 and SBT5.4, which also feature arginine in the corresponding position, fail to cleave the GST-sTWS1 precursor at the N-terminal processing site (Fig. 2F). Additional specificity may be provided by S333. Modeling suggests that the side chains of R302 and S333 interact with Asn in P3' of the TWS1 substrate (Fig. 2D, Movie S1). S333 is not conserved, neither in SBT1.1 nor in SBT5.4, thus explaining why these proteases fail to cleave TWS1 at the N-terminal site. While SBT1.1 and SBT5.4 are clearly not involved, other proteases are present (Dataset S1) and may contribute to TWS1 formation in the endosperm. Interestingly, the P3' Asn residue is uniquely found in TWS1, and replaced by Gly in other CIF peptides (Fig. 1A). This observation suggests that SBT1.8 interacts specifically with TWS1 and may not be involved in the processing of the CIF peptides regulating Casparian strip formation.

We report here an Arabidopsis protease (SBT1.8) that specifically recognizes the posttranslational modification (tyrosine sulfation) of its peptide precursor substrate (proTWS1). Tyrosine sulfation prevents mis-cleavage of proTWS1, suggesting that the sulfo-tyrosinedependent interaction with SBT1.8 evolved to increase the specificity of precursor processing for the biogenesis the TWS1 peptide. Post-translational modification of peptide hormones, particularly tyrosine sulfation of TWS1 and of other sulfo-peptides in plants and animals alike, was previously shown to be required for high-affinity binding to cognate receptors, and hence, for peptide activity [START_REF] Mobbs | Structures of the human cholecystokinin 1 (CCK1) receptor bound to Gs and Gq mimetic proteins provide insight into mechanisms of G protein selectivity[END_REF][START_REF] Okuda | Molecular mechanism for the recognition of sequence-divergent CIF peptides by the plant receptor kinases GSO1/SGN3 and GSO2[END_REF][START_REF] Wang | Allosteric receptor activation by the plant peptide hormone phytosulfokine[END_REF][START_REF] Doll | A two-way molecular dialogue between embryo and endosperm is required for seed development[END_REF]. We show here that post-translational modification, as a prerequisite for accurate precursor processing, may also contribute to bioactivity by controlling peptide hormone biogenesis.

Materials and Methods

Light microscopy for reporter gene analysis was performed as described [START_REF] Musielak | Use of SCRI Renaissance 2200 (SR2200) as a versatile dye for imaging of developing embryos, whole ovules, pollen tubes and roots[END_REF]. Expression and purification of recombinant proteases and substrate proteins, and mass spectrometric analysis of cleavage products were performed as in ref. [START_REF] Doll | A two-way molecular dialogue between embryo and endosperm is required for seed development[END_REF]. Structural modeling followed the strategy outlined in ref. [START_REF] Tan-Wilson | The PA domain is crucial for determining optimum substrate length for soybean protease C1: Structure and kinetics correlate with molecular function[END_REF] with tomato SBT3 as template structure [START_REF] Ottmann | Structural basis for Ca 2+ -independence and activation by homodimerization of tomato subtilase 3[END_REF]. Procedures are detailed in SI Appendix, Extended Methods.

Figure 1 .

 1 Figure 1. Processing of the TWS1 precursor by endosperm proteases ALE1 and SBT1.8. (A) Alignment of Arabidopsis CIF family peptide precursors with N-and C-terminal maturation sites indicated by arrowheads; Y = sulfo-tyrosine; mature peptides in bold. (B) Promotor:reporter gene analysis of SBT1.8 expression (green) during Arabidopsis embryo and seed development; bar = 10 µm (left) or 100 µm (right column. (C) Mass spectrometry (MS) cleavage assay with synthetic peptide substrates (sequence of substrate peptides black, cleavage products blue) including Nterminal (EDY; top) and C-terminal (HG; bottom) processing sites. Bar graphs show the abundance (peak ion intensity) of peptide cleavage products (blue) and of residual substrate peptides (black). (D) SDS-PAGE analysis of cleavage products (blue arrow) generated form recombinant GST-TWS1 (black arrow) by SBT1.8 and ALE1. (E) MS/MS identification of the cleavage product produced by SBT1.8 in (D). Identity and sequence of the C-terminal peptide was confirmed by the almost complete y-ion series (blue), and additional ions from the b series (red).

Figure 2 .

 2 Figure 2. Tyrosine sulfation facilitates N-terminal cleavage by SBT1.8. (A) Cleavage of GST-TWS1 and sulfated GST-sTWS1 by SBT1.8. In the buffer control, full-length and truncated forms of the GST-sTWS1 precursor are marked by black and gray arrowheads, respectively. Blue and red arrowheads mark the C-terminal and N-terminal cleavage products in the SBT1.8 digest. (B) MS assay comparing relative abundance (%) of cleavage products (30 min digest) for the sulfated (sTWS1) and unmodified (TWS1) N-terminal precursor peptides. Residual substrate peptides in gray, correctly processed and mis-cleaved peptides as full and open red bars, respectively. (C) Time course of GST-TWS1 cleavage at a molar protease:substrate ratio of 1:110 (top) and 1:11 (bottom). (D) Substrate peptide (yellow stick) including the N-terminal processing site (arrowhead) modeled into the active site of SBT1.8. Residues of the catalytic triad (D32, H106, S434; numbering of residues refers to the mature protease lacking the prodomain) are shown in green. P2' sulfo-tyrosine interacts by -stacking with the aromatic ring of F381 in the hydrophobic S2' pocket, and via the sulfate moiety with R302 of SBT1.8. The side chain of P3' Asn hydrogenbonds with R302 and S333. (E, F) SDS-PAGE analysis of cleavage products generated from GST-TWS1 and GST-sTWS1 by SBT1.8 and site-directed R302 mutants (E), and by SBT5.4 and SBT1.1 (F). Protein bands corresponding to C-and N-terminal cleavage products are indicated by blue and red arrowheads, respectively.

Acknowledgments

This work was supported by a grant from the German Research Foundation (DFG) to A.S. (SCHA591/17-1). S.R. was supported by a PhD fellowship from the Carl-Zeiss Foundation. Cooperation between the Schaller and Bayer labs was facilitated by the German Research Foundation SFB/CRC1101 (projects B01 and D06). The Exploris 480 mass spectrometer was funded in part by the German Research Foundation (DFG-INST36/171-1-FUGG).


Nicolas Max Doll
email: nicolas.doll@ugent.vib.be

Eduardo Berenguer

Jekaterina Truskina

Gwyneth Ingram
email: gwyneth.ingram@ens-lyon.fr

AtEXT3 is not essential for early embryogenesis or plant viability in Arabidopsis

EXTENSINs (EXTs) are a subclass of the very diverse family of cell wall proteins known as hydroxyproline-rich glycoproteins. The EXT subclass is defined by the presence of at least two repeats of the Ser-Pro 3-5 motif in which the proline residues can be hydroxylated and, subsequently, O-glycosylated with arabinose containing oligosaccharides leading to the formation of hydrophilic domains (Cannon et al., 2008;[START_REF] Velasquez | O-glycosylated cell wall proteins are essential in root hair growth[END_REF][START_REF] Ogawa-Ohnishi | Identification of three hydroxyproline O-arabinosyltransferases in Arabidopsis thaliana[END_REF]. In addition, many EXTs from land-plants also contain distinct and characteristic hydrophobic Tyr-containing motifs [START_REF] Schnabelrauch | Isolation of pl 4.6 extensin peroxidase from tomato cell suspension cultures and identification of Val-Tyr-Lys as putative intermolecular cross-link site[END_REF][START_REF] Held | Di-isodityrosine Is the Intermolecular Cross-link of Isodityrosine-rich Extensin Analogs Cross-linked in Vitro*[END_REF]Cannon et al., 2008). These have recently been termed cross-linking (CL)-EXTs, because of their ability to undergo peroxidase-mediated covalent cross linking in vitro through their Tyr residues, a phenomenon that has been proposed to underlie important structural functions in plant cell walls [START_REF] Marzol | Filling the Gaps to Solve the Extensin Puzzle[END_REF]. EXTs have a remarkable ability to self-assemble into dendritic networks in vitro (Cannon et al., 2008). This capacity, proposed to be based on self-ordering due to the alternation of hydrophobic and hydrophilic domains [START_REF] Lamport | Role of the extensin superfamily in primary cell wall architecture[END_REF], may mediate observed interactions with other cell wall components, including pectins [START_REF] Valentin | Elaboration of extensin-pectin thin film model of primary plant cell wall[END_REF].

Despite an abundance of information regarding the biochemical behavior of EXTs in vitro, information on their biological functions in vivo remains remarkably scarce, possibly due to extensive redundancy between the multiple genes encoding EXTs and EXT-like proteins within plant genomes. Work in Arabidopsis has identified potential roles for AtEXTs in promoting root-hair elongation [START_REF] Velasquez | O-glycosylated cell wall proteins are essential in root hair growth[END_REF], pollen development [START_REF] Choudhary | EXTENSIN18 is required for full male fertility as well as normal vegetative growth in Arabidopsis[END_REF] and early embryogenesis [START_REF] Hall | The Cell Wall Hydroxyproline-Rich Glycoprotein RSH Is Essential for Normal Embryo Development in Arabidopsis[END_REF][START_REF] Saha | Self-rescue of an EXTENSIN mutant reveals alternative gene expression programs and candidate proteins for new cell wall assembly in Arabidopsis[END_REF][START_REF] Chen | Identification of the Abundant Hydroxyproline-Rich Glycoproteins in the Root Walls of Wild-Type Arabidopsis, an ext3 Mutant Line, and Its Phenotypic Revertant[END_REF]. This last function is our focus in this comment.

Following an enhancer-trap (transposon)-based mutagenesis screen, Hall and Cannon [START_REF] Hall | The Cell Wall Hydroxyproline-Rich Glycoprotein RSH Is Essential for Normal Embryo Development in Arabidopsis[END_REF]) isolated a seedling-lethal mutant called root-shoot-hypocotyldefective (rsh) which, when homozygous, produces disorganized embryos that arrest either before (Cannon et al., 2008) or just after [START_REF] Hall | The Cell Wall Hydroxyproline-Rich Glycoprotein RSH Is Essential for Normal Embryo Development in Arabidopsis[END_REF] germination. Mutants show clear defects in cell-plate formation. A transposon insertion in the promoter of the AtEXT3 gene was stated to be the causal lesion in rsh mutants. Consistent with this, complementation of the rsh phenotype using an AtEXT3 genomic DNA fragment was reported. However, in two subsequent studies, "self-rescue" of the rsh phenotype to a fully wild-type phenotype was reported in up to 20% of genetically atext3 homozygous plants [START_REF] Saha | Self-rescue of an EXTENSIN mutant reveals alternative gene expression programs and candidate proteins for new cell wall assembly in Arabidopsis[END_REF][START_REF] Chen | Identification of the Abundant Hydroxyproline-Rich Glycoproteins in the Root Walls of Wild-Type Arabidopsis, an ext3 Mutant Line, and Its Phenotypic Revertant[END_REF]. No intermediate phenotypes were reported, and "rescue" was transmitted stably to all the progeny in subsequent generations. This calls into question the validity of genetic complementation experiments, since the reported "selfrescue" phenomenon would interfere with this approach. A mechanism involving compensatory regulation of other AtEXT-encoding genes was evoked to explain this phenomenon. Nonetheless it appears difficult, based on the published data, to exclude the possibility that the reported rsh phenotype is caused by a lesion linked to the original transposon insertion in AtEXT3, but affecting a separate locus, and lost by segregation in "rescued" lines.

We became interested in the AtEXT3 gene as a result of our discovery that the developing embryo of Arabidopsis is covered with an endosperm-derived "sheath" rich in epitopes detected by anti-EXT antibodies [START_REF] Moussu | ZHOUPI and KERBEROS Mediate Embryo/Endosperm Separation by Promoting the Formation of an Extracuticular Sheath at the Embryo Surface[END_REF]. In silico data [START_REF] Le | Global analysis of gene activity during Arabidopsis seed development and identification of seed-specific transcription factors[END_REF] concur with published in situ hybridization data [START_REF] Francoz | Complementarity of medium-throughput in situ RNA hybridization and tissue-specific transcriptomics: case study of Arabidopsis seed development kinetics[END_REF] in showing that AtEXT3 is expressed primarily in the embryo-surrounding endosperm during Arabidopsis seed development, rather than in the embryo itself. We were therefore keen to further investigate the function of AtEXT3 and we generated a series of new alleles in the Col-0 background using the CRISPR-Cas9 based technique. We selected 4 alleles containing deletions or insertions within the 5' coding sequence of the AtEXT3 gene (Figure 1A). All are predicted to produce either strongly truncated or frame-shifted proteins (Figure 1B). Being aware of recent work suggesting that 5' mutations generated by CRISPR can lead to the production of functional proteins due to the use of downstream in-frame ATGs [START_REF] Smits | Biological plasticity rescues target activity in CRISPR knock outs[END_REF], we verified the AtEXT3 coding sequence (which lacks introns) for such sequences. None are present. We therefore conclude that all four alleles are null. Homozygous plants were all fully phenotypically wild-type in terms of root growth, plant growth and reproductive development (Figure C-E). No defective seeds or seedlings were detected for any line, in any generation.

Our results suggest that loss of AtEXT3 do not lead to detectable defects in development in the Col-0 background. However, the rsh mutant originally described by Hall and Cannon was identified in the Landsberg erecta (L-er) background. To eliminate the possibility that a suppressor exists in Col-0 that is not present in the L-er background, we carried out CRISPR experiments in the L-er background, this time using guides predicted to interrupt the EXTENSIN 2 domains of the AtEXT3 protein. Of multiple null alleles brought to homozygosity we selected three deletions predicted to produce either strongly truncated or frame-shifted proteins (Figure 2). We were again unable to detect any defects in root growth, plant growth or reproductive development in any of these lines. In addition, we confirmed the absence of phenotypes during seed development, and germination (Figure S1).

Taken together, our results indicate that AtEXT3 is not essential for embryogenesis and plant development in Arabidopsis, suggesting that its function is likely redundant with other related proteins. We feel that it is important that our result be communicated to the scientific community, since the role of AtEXT3 has been very widely cited as a proof of the important function of EXTs in plant development and particularly in cytokinesis. Although fully convinced that EXTs are of primary importance in plants, we propose that the roles of AtEXT3 may be almost fully redundant with those of other related proteins. Supporting this hypothesis, phenotypeless atext3 mutants were shown to have extensive changes in the expression of other AtEXT-encoding genes, or the accumulation of AtEXT proteins [START_REF] Saha | Self-rescue of an EXTENSIN mutant reveals alternative gene expression programs and candidate proteins for new cell wall assembly in Arabidopsis[END_REF][START_REF] Chen | Identification of the Abundant Hydroxyproline-Rich Glycoproteins in the Root Walls of Wild-Type Arabidopsis, an ext3 Mutant Line, and Its Phenotypic Revertant[END_REF]. It is possible that a similar phenomenon occurs in the CRISPR generated atext3 mutants. Finally, our study does not call into question the presence of AtEXT3 in cell walls post germination, or the beautiful biochemical studies of AtEXT3 behavior in vitro that have been published (Cannon et al., 2008).

Material and methods CRISPR/Cas9 allele generation

The CRISPR/Cas9 alleles of AtEXT3 (AT1G21310) were generated through the method described by Wang and co-workers for double guides assembly [START_REF] Wang | Egg cell-specific promoter-controlled CRISPR/Cas9 efficiently generates homozygous mutants for multiple target genes in Arabidopsis in a single generation[END_REF]. The 2 guides were designed on the CRISPR-P v2.0 software and chosen to target the 5'end of the gene (first guide: 5'-GAAGAATAGAAATAGTTAG-3', second guide: 5'-TATTCTTCTCCTCCACCAC-3').

For the cloning, the primers used were 5'-ATATATGGTCTCGATTGGAAGAATAGAAATAGTTAGGTT-3' and 5'-TGGAAGAATAGAAATAGTTAGGTTTTAGAGCTAGAAATAGC -3' for the first guide and 5'-AACGTGGTGGAGGAGAAGAATACAATCTCTTAGTCGACTCTAC -3' and 5'-ATTATTGGTCTCGAAACGTGGTGGAGGAGAAGAATACAA -3' for the second guide. The pHEE401 plasmid was used as the destination vector.

For the generation of the CRISPR/Cas9 alleles of EXT3 in the L-er background the method described by Stuttmann and co-workers was used [START_REF] Stuttmann | Highly efficient multiplex editing: one-shot generation of 8× Nicotiana benthamiana and 12× Arabidopsis mutants[END_REF]. Two guides (first guide: 5'-GGTGGGGAGTGGTATACCGG-3', second guide: 5'-ATACAAATCTCCACCTCCAC-3') were inserted into the pDGE332 and pDGE334 shuttle vectors and recombined into the pDGE347 recipient vector.

Stable transformation of Arabidopsis thaliana (L.) Heynh.

The plasmids generated were first transformed into the C58PMP90 Agrobacterium strain by electroporation at 2,2kV in a 1mL cuvette (Eurogenetec). Agrobacteria were then grown at 28°C for 2h in LB liquid media without antibiotics before being spread on Petri dishes containing YEB solid media with rifampicin (50 mg.L -1 ), gentamicin (20 mg.L -1 ) and kanamycin (50 mg.L -1 ) for the pHEE401 based plasmid or rifampicin (50 mg.L -1 ), gentamicin (20 mg.L -1 ) and spectinomycin (100 mg.L -1 ) for the pDGE347 based plasmid. After 2 days, the Agrobacteria were used to transformed Col-0 plants by the floral-dip method [START_REF] Logemann | An improved method for preparing Agrobacterium cells that simplifies the Arabidopsis transformation protocol[END_REF]. Selection of transgenic plants was performed on MS plates with 30 mg.L -1 of hygromycin.

Identification of the mutant alleles and generation of homozygous plants.

Amplification of the region around the cutting sites was performed by PCR on DNA extracted from transgenic Col-0 plants using the 5'-GGGTGTGAAGGGAAGGCACTAAATC-3' and 5'-GTAAACGTAGTGCTTCTTTGGTGG-3' primers. For the transgenic L-er plants the primers used were 5'-GGGTGTGAAGGGAAGGCACTAAATC-3' and 5'-AGGTGGGGGTGGGGAATGGTA-3'.

Sequencing was carried out with the 5'-GTAAACGTAGTGCTTCTTTGGTGG-3' (Col-0) or and 5'-GGGTGTGAAGGGAAGGCACTAAATC-3' (L-er) primers. Homozygous mutants for atext3 were selected from the T2 plants. In the Col-0 background the CRISPR/Cas9 cassette was removed by selection of hygromycin sensitive plants and validated by an absence of cassette amplification by PCR with the 5'-TGTCCCAGGATTAGAATGATTAGGC-3' and 5'-AGCCCTCTTCTTTCGATCCATCAAC-3' primers. Homozygous mutants were confirmed by sequencing. In L-er transgenic plants non-fluorescent seeds were selected in the T2 and homozygous mutants were confirmed by sequencing.

Plant growth

For the selection processes and in vitro growth (Figure 1C and 2C), seeds were gas sterilized with Chlorine gas (3mL of HCl (33%) in 100mL of bleach) for at least 3 hours in a hermetic box. Seeds were then sown on MS medium with 0.5% of sucrose and, for the selection of Col-0 mutants, with 30 mg.L -1 of hygromycin. Stratification was carried out 2 days at 4°C in darkness. Plates were then transferred into growth chambers in long day condition (16h light, 21°C). For the in vitro growth experiments, plants were grown for 14 days (Col 0) or 10 days (L-er) and pictures were taken with an EPSON perfection V300 PHOTO scanner. For germination tests, plants were grown for 12 days (100 seeds per plate). In all the other cases, seedlings were transferred after 7 days onto soil (Argile 10 (favorit)) and grown in long day conditions (16h light, 21°C). Images of plants at 29 DAS and 46 DAS (Figure 1D andE) or 28 DAS and 46 DAS (Figure 2D and2E) were taken with a Cannon EOS 450D camera with a Sigma 50 mm f/2,8 DG Macro objective. For analysis of silique filling, individual siliques were opened at the mature green stage, and seeds were counted under a binocular microscope.

Seed clearing

To visualize developing seeds, the siliques at 7 DAP were dissected with a needle and forceps on adhesive tape on a microscope slide and mounted in the clearing solution (1 vol glycerol / 7 vol chloral hydrate liquid solution, VWR Chemicals). After 48h incubation at 4°C samples were imaged under Zeiss Axio Imager M2 microscope.

Figure 1 :

 1 Figure 1: Loss of AtEXT3 function gives no obvious phenotypes in the Arabidopsis Col-0 background. A) DNA sequence of the AtEXTENSIN3 (AT1G21310) gene between position +60 and +118 from the predicted start of translation. The CRISPR/Cas9 alleles generated are represented below and named atext3-1 to -4. The protospacer adjacent motif (PAM) sequences of the 2 guides used are underlined. The mutations in the mutant alleles sequence are indicated in red. B) Predicted consequences of the mutations on the AtEXT3 protein. The 3 EXTENSIN-2 domains predicted by the Protein Families database (Pfam) are represented as well as the position of the 2 guides used. For the predicted mutant proteins, sequences that differ from those of Col-0 due to a frameshift are represented in grey. The amino acid positions from the predicted first methionine are indicated. C) 14 days after stratification (DAS) seedlings, grown in vitro for Col-0 and for the four AtEXT3 null alleles. D) 29 DAS plants grown on soil for Col-0 and for the four AtEXT3 null alleles. E) 46 DAS plants grown on soil for Col-0 and for the four AtEXT3 null alleles. Scale bars in C and D = 2cm and in E = 3cm.Figure 2: Loss of AtEXT3 function gives no obvious phenotypes in the Arabidopsis L-er background. A) DNA sequence of the AtEXTENSIN3 (AT1G21310) gene between position +145 and +245 from the predicted start of translation. The CRISPR/Cas9 alleles generated are represented below and named atext3-5 to -7. The protospacer adjacent motif (PAM) sequences of the 2 guides used are underlined. The mutations in the mutant alleles are indicated in red. B) Predicted consequence of the mutations on the AtEXT3 protein. The 3 EXTENSIN-2 domains predicted by the Protein Families database (Pfam) are represented as well as the position of the 2 guides used. For the mutant proteins, sequences that differ from L-er due to a shift in the ORF are represented in grey. The amino acid positions from the predicted first methionine are indicated. C) 10 days after stratification (DAS) seedlings, grown in vitro for Ler and for the three AtEXT3 null alleles. D) 28 DAS plants grown on soil for L-er and for the three AtEXT3 null alleles. E) 46 DAS plants grown on soil for L-er and for the three AtEXT3


Nicolas Bailly

Jean-Marie Dilhac

Christophe Escriba

Claude Vanhecke

Nicolas Mauran

Marise Bafleur

ENERGY SCAVENGING BASED ON TRANSIENT THERMAL GRADIENTS: APPLICATION TO STRUCTURAL HEALTH MONITORING OF AIRCRAFTS

Keywords: energy scavenging, thermoelectric generator, voltage booster, aircraft structural health monitoring

Results about energy capture from the environment associated with the self-powering of a wireless sensor network (WSN) for structural health monitoring of aircrafts (SHM) are presented. Thermal gradients taking place during specific flight phases are converted into electrical energy by using a small water tank coupled to a thermoelectric generator which output is connected to a storage circuit. The proposed system is experimentally assessed in an environmental chamber. Regarding the energy storage issue, considering the operating temperature (-55°C) and the amount of energy, supercapacitors are suggested instead of batteries. For capacitors charging and later use of energy, a circuit based on a voltage booster associated with a DC-DC regulator is designed and simulated.

INTRODUCTION

Aircraft in-service structural health monitoring (SHM) by wireless sensors based on MEMS technology, bonded on the aircraft structure, is considered as a powerful tool to both decrease inspection costs and ensure safety [START_REF] Mitchell | Energy Harvesting Applications and architectures at Boeing commercial airplanes[END_REF]. In-service monitoring of corrosion, of cracks and of impact damage, is therefore considered by major aircraft manufacturers [START_REF] Mitchell | Energy Harvesting Applications and architectures at Boeing commercial airplanes[END_REF].

In that context, a network of a relatively large number of self-powered MEMS-based nodes, would perform sensing, data processing and wireless transmission of information. The ultimate objective of such a Wireless Sensor Network (WSN) is to provide a warning when damage is initiated, before the functions of the structure are compromised.

A critical issue for such a WSN is the requirement of energy autonomy that cannot be provided by batteries because of batteries limited lifetime and storage performance together with their inability to operate at the very low temperatures encountered in the aeronautical application. As a result, each node must get sufficient energy from its environment [START_REF] Chandrakasan | Design considerations for distributed microsensor systems[END_REF]. A possible source of energy could be thermal gradients. This paper investigates the potentialities of this method in the very specific aeronautical application context.

DESCRIPTION OF SCAVENGING SYSTEM

Description of Application

Despite the fact that various schemes may be considered in the context of aircraft structural health monitoring, we will restrict ourselves to the following. We will consider nodes operating from unpowered locations in the aircraft. Application precludes external devices mounted on the airfoils outside the aircraft, therefore excluding the use of solar energy, which is known to be one of the most efficient way (in terms of power density) of capturing energy from a system environment.

For our application, the WSN lifetime should be similar to that of the aircraft, i.e. between ten and twenty years. Moreover, our application being related to SHM of large-size aircrafts, data sensing and information processing are to be performed by nodes while at high altitude, up to 12 000 m and temperatures as low as -60° Celsius.

Origin of Transient Temperature Gradients

Since from takeoff to cruise altitude, atmospheric temperature usually strongly decreases, thermal gradients may therefore appear inside the WSN node hardware. These gradients can be converted into voltage via a thermoelectric generator based on the Seebeck effect. However, sooner or later, the node will be back in an isothermal state and no more energy will be available. Therefore, to both increase the gradient value and duration, we chose to use water, therefore benefiting from its high specific heat (4.2 J/g°C -to be compared with that of Si, 0.7 J/g°C). Water melting point (0°C) being situated within the aircraft temperature operating range, the system also benefits from energy exchanges related to water latent heat of fusion (330 J/g), with the added advantage of water being maintained at a constant temperature during the freezing or melting phases, hence increasing temperature gradient values.

Referring to the International Standard Atmosphere (ISA) defined by the International Civil Aviation Organization (ICAO), at an en-route flight altitude of 12000 m, atmospheric temperature is about -60°C. On the other hand, airfoils exposed to sunlight when aircraft is parked heat up, and specific wing areas may reach temperatures over 100°C.

To roughly assess the upper limit for the thermal energy that could be captured, let us consider a sealed tank containing 1 g of water. As already mentioned, water exhibits a specific heat C of around 4.2 J/g.°C, and an enthalpy of fusion H f of 330J/g. Stating a capsule temperature at takeoff of 15°C, and a cruise temperature of -60°C, the temperature variation ΔT is 75°C, and the energy W T associated with temperature variation ΔT is given by:

W T = ΔT x C + H f = 645 J/g (1)
This value may be doubled as temperature variations take place both during climbing and descent that is a total of 1290 J. This value may be slightly higher if takeoff temperature and therefore initial temperature of the sensor node is higher. Unfortunately, it can conversely be very limited if this temperature is below icing temperature.

However, this is a thermal energy and it has to be converted into electrical energy. Thermoelectric generators using the Seebeck effect may achieve this step. Such generators are current sources converting temperature difference between a cold and a hot surface into electrical energy. Unfortunately, for temperature differences of a few tens of degrees, the yield of such components may be as low as 1% or less, drastically reducing the energy really available for WSN node activity. On the other hand, commercial products are fortunately available [START_REF] Technology | [END_REF][START_REF]Micropelt[END_REF] in sizes compatible with our application.

Experimental Apparatus

To assess the feasibility of the proposed method, we chose to operate a 2.47 mm x 2.47 mm Micropelt GmBH [START_REF]Micropelt[END_REF] thin film thermoelectric generator (MPG-D602 type), implemented into a custom-made test module. The thickness of the generator is 500 µm, it incorporates 450 couples of n-type and p-type thermoelectric material.

We designed a test module to accommodate the above thermoelectric generator. Briefly, the thermoelectric generator is compressively loaded between a small tank filled with 12 ml of water and a 9 cm x 9 cm, 2 mm thick Al plate simulating the aircraft structure. Putting the generator under a compressive load helps maximize heat transfer. To electrically isolate the surfaces of the generator, 0.25 mm thick Al2O3 insulating wafers are inserted in between the thermoelectric generator and the surfaces to which it is mounted. To prevent a significant loss of energy by thermal bypass, an efficient thermal insulation is implemented to force most of the thermal energy to pass through the thermoelectric generator.

To simulate the aircraft temperature conditions, we tested the scavenging module into an environmental test system TP04200A ThermoStream® from Temptronic corporation. This equipment is designed for testing electronic components from -85°C to +225°C. For this purpose it can generate a stream of dry air with controlled temperature.

To model the evolution of the temperature during a flight, we chose the standard atmosphere model of ICAO, that is a temperature of 15°C (59°F) and a pressure of 1013.25 hPa (14.7 psi) at sea level, and a decreasing rate of -6.5°C/km until the limit temperature -56.5°C. However, during a typical flight, there is no linear evolution of temperature vs. time because a decrease of the air density vs. altitude degrades the engine performance during the ascent, and directly impacts the speed of ascent. For the descent, the constraints of engine performance do not apply any more and the plane goes down linearly, only limited by the descent speed standards according to the altitude.

EXPERIMENTAL RESULTS

We set the ThermoStream® so that it followed the temperature corresponding to a typical one-hour flight of a commercial airliner. In Fig. 1, the airflow and water tank temperatures are plotted vs. time.

Fig. 1: Outside air and water temperatures vs. time for a short range flight (experimental)..

The airflow temperature initially decreases rapidly, and thermal energy is withdrawn from the water tank by conduction, mainly through the thermoelectric generator. The liquid water temperature consequently decreases until it drops below the freezing point (0°C) and then rebounds and as expected holds steady while water crystallizes. At this transition point between liquid and solid states, extra energy (enthalpy of fusion) has to be withdrawn before the water is completely frozen and ice temperature decreases again until steady state is established. During steady state a small temperature difference between ice and airflow is maintained due to non-ideal thermal isolation between water tank and its surrounding environment at room temperature.

When airflow temperature rises back to the initial 15°C starting point, ice temperature increases up to the transition point between solid and liquid where its temperature remains constant until the extra energy required for melting is added by conduction through the generator. During the above cycle, thermoelectric generator output open voltage, internal resistance and generated power were monitored. To evaluate the amount of scavenged energy, it was necessary to continuously measure the power generated by the thermoelectric generator during the cycle. The temperature gradient and the corresponding output voltage are plotted in Fig. 2. Output power and cumulated energy are plotted in Fig. 3.

For all variables excepted energy, a first peak occurs between 10 and 15 min after take off, when the temperature difference between water and airflow is high: the aircraft structure is reaching its final steady state low temperature while water icing is still not completed thus maintaining water temperature at 0°C. Another peak takes place during the descent, and again it corresponds to the plane structure being at its final temperature while water is still frozen. However, as the final temperature is here closer to zero than the in route temperature, the thermal gradient is smaller and so is the output power of the thermoelectric generator. The first peak during ascent is consequently the absolute maximum of the power characteristic: the WSN node can therefore benefit, from the beginning of the flight, of the essential of the energy to be captured during the total duration of the flight.

In the context of scavenging, assessing the captured energy makes more sense than considering the generated instantaneous power. In that respect, for the above reference flight, it can be seen from Fig. 3 that roughly 34 J can be captured from the thermal environment. Considering that the water tank capacity is 10 ml, corresponding to 10 g of water, equation [START_REF] Mitchell | Energy Harvesting Applications and architectures at Boeing commercial airplanes[END_REF] gives a thermal energy of 2 x 6450 J. The efficiency of our module is therefore roughly 0.3 %, broadly in agreement with the expected yield of the thermoelectric generator given the obtained thermal gradients.

On the other hand, the thermoelectric generator internal resistance remains relatively constant (189 Ω) during the thermal cycle, while the open circuit output voltage may be as high as 4 V. The DC nature of the generator output, its relatively high amplitude together with its low output resistance, favor the efficiency of the energy transfer from the generator to the storage element, the only issue being the change of polarity of the output during the flight (see Fig. 2).

STORAGE OF SCAVENGED ENERGY

To get rid of the cost (in terms of energy loss) of energy storage, immediate use of captured environmental energy by the WSN node management system would be preferable, as a fraction of the scavenged energy is likely to be lost through parasitic resistors and self-discharge during storage. Unfortunately, it is very likely that the WSN node will not by-pass the energy-storage device, even for immediate usage, but will use it as an energy buffer. Additionally, energy long-term storage may be desired to budget for future energy consumption when scavenging efficiency would be low. A powerconditioning circuit sitting between the power source and the load is therefore required. In our application context, the use of a battery being unlikely, electrical double-layer capacitors, also named super-capacitors or ultra-capacitors, may be the support of transient storage.

As it can be seen from Fig. 2, the output voltage is alternatively positive and negative depending upon flight phase. Consequently, to efficiently transfer the captured electrical energy to the super-capacitors, we designed a dedicated self-biased voltage rectifier / booster circuit, using MOS-based active diodes to reduce losses (Fig. 4).

SPICE simulation results, based on a 0.35 µm CMOS technology show that the active diode threshold voltages can be as low as 10 mV, the charging of the supercapacitors starts 2 min after take off (time required for self-bias activation), and the capacitors voltage value of 1V required to start the DC-DC regulator (not shown in fig. 4) operation is reached after 9 min (Fig. 5).

CONCLUSION

Aircraft structural health monitoring by WSN is a key tool both to reduce inspection costs, and to increase safety. For such a self-powered network, energy is a critical issue, as it is for most WSN, and hardware design must use energy as a starting point. However, considering the aeronautical application presented above, it has been shown that energy scavenging from the transient thermal gradients taking place during the flight may be of great interest. The benefits first comes from the amount of captured electrical energy which favorably compares with alternative harvesting or scavenging techniques, secondly in the fact that most of the scavenged energy is available at the beginning of the flight, and finally in the easy use of the generator electrical output.

However, this method needs to be compared or combined with other solutions for energy harvesting (capture of energy from aircraft structure mechanical vibrations, from acoustic power, and from aircraft static electricity dischargers) or other energy scavenging methods such as the use of atmospheric pressure variations.

Voltage (volts)

Fig. 2 :

 2 Fig. 2: Thermal gradient and output voltage vs. time for the flight of Fig. 1 (experimental).

Fig. 3 :

 3 Fig. 3: Power and energy vs. time for the flight of Fig.1 (experimental).

Fig. 5 :

 5 Fig. 5: Input and output voltages vs. time of the voltage booster for the flight of Fig.1 (open loadsimulation).

ACKNOWLEDGMENTS

The authors would like to thank Fondation de Recherche pour l'Aéronautique et l'Espace for financial support and Airbus-Toulouse, especially Jean Pierre Daniel, for technical support.