Friction modeling is essential for joint dynamic identification and control. Joint friction is composed of a viscous and a dry friction force. According to Coulomb law, dry friction depends linearly on the load in the transmission. However, in robotics field, a constant dry friction is frequently used to simplify modeling, identification and control. That is not accurate enough for joints with large payload or inertial and gravity variations and actuated with transmissions as speed reducer, screw-nut or worm gear. A new joint friction model taking dynamic and external forces into account is proposed in this paper. A new identification process is proposed, merging all the joint data collected while the mechanism is tracking exciting trajectories and with different payloads, to get a global LS estimation in one step. An experimental validation is carried out with a prismatic joint composed of a Star high precision ball screw drive positioning unit.

I. INTRODUCTION

HE usual identification method, based on the inverse dynamic model (IDM) and least squares (LS) method, has been successfully applied to identify inertial and friction parameters of a lot of prototypes and industrial robots [START_REF] Atkeson | Estimation of Inertial Parameters of Manipulator Loads and Links[END_REF]- [START_REF] Kawasaki | Terminal-link parameter estimation and trajectory control of robotic manipulators[END_REF]. The kinematic Coulomb friction at non zero velocity q ɺ is widely approximated by ( )

C
F sign q ɺ , where C F is a constant parameter. The identification consists in moving the robot without any load (or external force) or with constant given payloads [START_REF] Khalil | Identification of the payload inertial parameters of industrial manipulators[END_REF]. However, according to the Coulomb law, C F is a linear function of the contact reaction in the mechanism. It depends on the dynamic and the external forces applied through the joint drive chain. Thus, the usual identified IDM is no more accurate for joints with varying load, particularly with transmissions as industrial speed reducer, screw-nut or worm gear because their efficiency significantly varies with the transmitted force. This dependence on load has been often observed in transmission elements [START_REF] Gogoussis | Determining the effects of Coulomb friction on the dynamics of bearings and transmissions in robot[END_REF]- [START_REF] Garrec | ABLE, an Innovative Transparent Exoskeleton for the Upper-Limb[END_REF] through direct measurement procedures. Moreover, the mechanism efficiency often depends on the sense of power transfer leading to two distinct sets of friction parameters. This paper presents a new method to identify automatically the dry friction model where C F is a linear function of the applied force, with an asymmetrical behavior.

An experimental validation is carried out on a ball screw drive prismatic joint.

II. EXPERIMENTAL SETUP AND DYNAMIC MODEL

The EMPS is a high-precision linear Electro-Mechanical Positioning System. Its main components are a Maxon DC motor which is current controlled by a four quadrant PWM amplifier, a Star high-precision low-friction ball screw drive positioning unit, and an incremental encoder. The backlash free ball screw drive is the gear converting the rotary motion of the motor to the linear carriage joint displacement. The EMPS is a standard configuration of a drive system for prismatic joint of robots, machine tools, haptic device… It is connected to a dSPACE digital control system for easy control and data acquisition using Matlab and Simulink software [20], [21]. In order to make easy variation of the gravity load, the EMPS can be fixed in vertical position alternatively with the z joint positive linear displacement in the gravity direction or in its opposite.

The inverse dynamic model is given by:

( ) ( )
( ) a C V off out C V off I m q mg F sign q F q F sign q F q τ τ τ τ = + - + + + = + + + ɺɺ ɺ ɺ ɺ ɺ (1)
where:

• q , q ɺ and q ɺɺ are respectively the generalized joint position, velocity and acceleration,

• τ is the drive force,

• out τ is the output force (the load force) of the drive chain,

• I a is the inertia moment of all rotary elements in the drive chain (rotor of motor and encoder, ball, coupling units), • m is the mass of all translation moving elements (screw, carriage, payload),

• F V is the viscous friction coefficient,

Dynamic Modeling and Identification of Joint Drive with Load-Dependent Friction Model

P. Hamon (1) , M. Gautier (2) , P. Garrec (1) , and A. Janot (3) (1) CEA, LIST, Interactive Robotics Laboratory, 18 route du Panorama, Fontenay-aux-Roses, F-92265, France. (2) University of Nantes/IRCCyN, 1 rue de la Noë, BP 92 101, Nantes Cedex 03, F-44321, France.

(3) HAPTION S.A., Atelier Relais de Soulgé, Route de Laval, Soulgé-sur-Ouette, F-53210, France.

T z

• F C is the Coulomb friction force,

• off τ takes into account the amplifier offset and a dissymmetric Coulomb friction force, • g is the projection of the gravity acceleration on the z prismatic joint axis. • g = 9.81, when z axis stays in vertical position, oriented positive to the earth, • g = -9.81, when z axis stays in vertical position, oriented positive to the sky. All variables and parameters are given in SI units on the joint linear displacement side (carriage side).

III. USUAL IDENTIFICATION MODEL AND IDENTIFICATION METHOD

A. Modeling

The choice of the modified Denavit and Hartenberg frame attached to the link allows to obtain a dynamic model linear in relation to a set of standard dynamic parameters χ [START_REF] Gautier | Identification of robot dynamics[END_REF], [START_REF] Khalil | Modeling, identification and control of robots[END_REF]. Most of the methods to identify the joint dynamic parameters use an identification model linear in relation to the parameters, and solve the system using least squares techniques (LS) [START_REF] Atkeson | Estimation of Inertial Parameters of Manipulator Loads and Links[END_REF]- [START_REF] Gautier | Numerical calculation of the base inertial parameters[END_REF]. With the usual friction model, F C is a constant (Fig. 2.a) and the inverse dynamic model ( 1) is linear in relation to the dynamic parameters:

[ ] ( ) ( ) a C V off I m m F q g sign q q 1 q,q F τ τ +         = - =         D χ ɺɺ ɺ ɺ ɺ (2)
where [ ] ( ) ( ) q,q q g sign q q 1 = -D ɺ ɺɺ ɺ ɺ is the regressor of the linear relation, and

T a C V off I m m F F τ   = +   χ is the ( x )
b 1 vector of the b base dynamic parameters.

B. Identification

We consider off-line identification of the dynamic parameters χ, given measured or estimated off-line data for τ, q, q, q ɺ ɺɺ , collected while the mechanism is tracking some planned trajectories: the inverse dynamic model ( 2) is sampled to get an over-determined linear system such that (3). The velocities and accelerations are calculated using well tuned band pass filtering of the joint position [START_REF] Gautier | Identification of robot dynamics[END_REF].

( )

( ) q,q,q τ = + Y W χ ρ ɺ ɺɺ (3)
with ( ) τ Y the measurements vector, W the observation matrix and ρ the vector of errors.

The LS solution χ minimizes the 2-norm of the vector of errors ρ . W is a ( x ) r b full rank and well conditioned matrix where x e r N n = , with e N the number of samples on the trajectories. The LS solution χ is given by: ( )

-1 T T ˆ+ = = χ W W W Y W Y (4)
It is calculated using the QR factorization of W . Standard deviations i χ σ are estimated using classical and simple results from statistics. The matrix W is supposed to be deterministic, and ρ , a zero-mean additive independent noise, with a standard deviation such as:

E(

)

T 2 ρ σ = = ρρ r C ρρ I ( 5
)
where E is the expectation operator and r I , the ( x ) r r identity matrix. An unbiased estimation of ρ σ is:

( ) 2 2 ˆr b ρ σ = - - Y Wχ (6)
The covariance matrix of the standard deviation is calculated as follows:

T T -1 E ( )( ) ( ) 2 ˆˆˆˆˆρ σ   = - - =   χχ C χ χ χ χ W W (7) i 2 ˆˆˆii χ σ = χχ C is the i th diagonal coefficient of ˆχ χ C . The relative standard deviation ri % χ
σ is given by:

ri i ˆˆi % 100 χ χ σ σ χ = (8)
However, experimental data are corrupted by noise and error modeling and W is not deterministic. This problem can be solved by filtering the measurement vector Y and the columns of the observation matrix W as in [START_REF] Gautier | Dynamic identification of robots with power model[END_REF], [START_REF] Gautier | Extended Kalman filtering and weighted least squares dynamic identification of robot[END_REF].

IV. NEW FRICTION MODELING AND IDENTIFICATION

In this section, we introduce a friction model dependent on the load and the sign of the power.

A. Load-Dependent Friction Model

The Coulomb friction is still written ( )

C F sign q ɺ but C F
depends linearly on the absolute value of the load (Fig. 2.b), [START_REF] Gogoussis | Determining the effects of Coulomb friction on the dynamics of bearings and transmissions in robot[END_REF]- [START_REF] Garrec | ABLE, an Innovative Transparent Exoskeleton for the Upper-Limb[END_REF].

Then the inverse dynamic model becomes:

( ) ( ) out out V off sign q F q τ τ α τ β τ = + + + + ɺ ɺ (9)
where the parameters α and β are constants to be identified. These new parameters depend on the mechanical structure of the reducers used to actuate the joint.

The inverse dynamic model can be written as follows:

( ) ( )

out out V off sign q sign q F q τ τ α τ β τ = + + + + ɺ ɺ ɺ (10)
And with ( )

out out out sign τ τ τ = , one obtains: ( ) ( ) ( ) out out out V off sign sign q sign q F q τ τ ατ τ β τ = + + + + ɺ ɺ ɺ (11)
Thus, the IDM depends on the signs of out τ and q ɺ . With ( )

( ) ( )
out out sign sign q sign q τ τ = ɺ ɺ , one defines 4 quadrants which can be grouped two by two (Fig. 3.a), depending on the sign of the output power denoted out out P q τ = ɺ . In the quadrants 1 and 3, out P is positive and the actuator has a motor behavior. In the quadrants 2 and 4, out P is negative and the actuator has a generator behavior which may save the power to the power supply, assuming a 4 quadrants amplifier. This model is valid for symmetrical friction. Generally, the friction is asymmetrical and α and β can take different values depending on the quadrant where the joint runs.

B. Power Sign-Dependent Friction Model

We present here 3 ways of modeling.

• 4 models (4x1Q) for 4 different quadrants In the general case, the friction parameters α , β , and off τ depend on the signs of out τ and q ɺ in the frame ( q ɺ , out τ ), Fig.

out 1 out V 1 out 2 out V 2 out 3 out V 3 out 4 out V 4 0 q 0 1 F q 0 q 0 1 F q 0 q 0 1 F q 0 q 0 1 F q τ τ α τ β τ τ α τ β τ τ α τ β τ τ α τ β > > ⇒ = + + +   > < ⇒ = - + -   < < ⇒ = + + -   < > ⇒ = - + +  ɺ ɺ ɺ ɺ ɺ ɺ ɺ ɺ (12)
• 2 models (2x2Q) for quadrants identical two by two In some cases, the friction is symmetrical with respect to the velocity and the 4 models can be simplified to 2 models.

out m out V m off m out g out V g off g P 0 1 F q sign q P 0 1 F q sign q τ α τ β τ τ α τ β τ > ⇒ = + + + +    < ⇒ = - + + +   ɺ ɺ ɺ ɺ (13)
• 1 model (1x4Q) for 4 identical quadrants In the case of symmetrical friction with respect to out τ and q ɺ , the models simplify to 1 model for 4 quadrants.

( ) ( )

out V out out off F q sign P sign q τ τ ατ β τ = + + + + ɺ ɺ (14)
Each modeling takes the load-dependency of friction into account. Starting from the (4x1Q) models, a simplification procedure based on the identification results leads to the simplest model. However, when the joint torque amplitude is low, the friction model should be more complex because of Coulomb friction resulting from internal preload and hysteresis. Then, one cannot use the quadrants. To simplify and connect the quadrants models, a relevant approximation consists in extending them in this area which can be considered as uncertain for the experimental identification. This simplification is illustrated in a (τ , out τ ) graph (Fig. 3.b). It should be noticed that within this area, the mechanism is no longer transmitting power which is totally dissipated in friction losses.

C. Friction identification method

The friction models depend on the sign of out τ which is unknown. To overcome this problem, the samples of τ measurements are selected outside of the uncertain area (Fig. 3.b) in order to get the same sign for out τ and τ . This allows to get the sign of out P with:

( ) ( ) ( ) ( )
out out sign P sign q sign q sign P

τ τ = = = ɺ ɺ (15)
The (1x4Q) modeling can be written:

( ) ( ) out V out off F q sign P sign q τ τ ατ β τ = + + + + ɺ ɺ (16)
For the (2x2Q) modeling, 2 variables are introduced, P + and P -defined by: ( )

1 sign P P 2 + + = , 0 1 P P + > ⇔ = , 0 0 P P + < ⇔ = (17)
( )

1 sign P P P 2 - + - = = (18) b) q ɺ β -β out τ increases Friction a) q ɺ F c -F c
0 q 0 < ɺ 0 q 0 < ɺ 0 q 0 > ɺ 0 q0 > ɺ
Then [START_REF] Gautier | Numerical calculation of the base inertial parameters[END_REF]

For the (4x1Q) modeling, 4 similar variables have to be defined to obtain one model only.

As out τ is linear in relation to parameters, so is τ . Thus, for each friction model, one can write the IDM linear in relation to parameters and use the LS technique.

V. EXPERIMENTAL IDENTIFICATION

The identification process has been performed for the four different cases: first with the usual model where C F is constant, then with the 3 friction models depending on the load. We have observed that the modeling with four identical quadrants (1x4Q) was insufficient because the friction is asymmetrical here. Moreover, the modeling with four different quadrants (4x1Q) was not indispensable as the parameters are very close for the two motor quadrants on one hand and for the two generator quadrants on the other hand. That is the reason why only the modeling with quadrants identical two by two (2x2Q) is detailed here and compared with the usual model. The methods for the others are very similar.

A. Data Acquisition

The identification of dynamic parameters is carried out with and without payloads: five different additional masses can be fixed on the carriage. To excite properly the friction parameters to be identified, trapezoidal velocities trajectories were used. The sample acquisition frequency for joint position and current reference (drive force) is 5 KHz.

The estimation of q ɺ and q ɺɺ are carried out with pass band filtering of q consisting of a low pass Butterworth filter and a central derivative algorithm. The Matlab function filtfilt can be used. This is a zero-phase forward and reverse digital filtering. We calculate the drive force using the relation:

G v τ τ τ = (20)
where v τ is the current reference of the amplifier current loop, and G τ is the gain of the joint drive chain, which is taken as a constant in the frequency range of the robot because of the large bandwidth (700 Hz) of the current loop [START_REF] Restrepo | Calibration of drive chain of robot joints[END_REF].

In order to cancel high frequency ripple in v τ (and τ ) , the vector Y and the columns of the observation matrix W are both low pass filtered and decimated. This parallel filtering procedure is carried out with the Matlab decimate function [START_REF] Swevers | Optimal Robot excitation and Identification[END_REF], [START_REF] Kawasaki | Terminal-link parameter estimation and trajectory control of robotic manipulators[END_REF].

B. Identification

To identify the load-dependent friction, measurements with known payloads must be used and one needs the relation:

0 a m m m = + (21)
where:

• 0 m is the unknown mass of the translational elements, with the carriage free of additional mass,

• a m is one of the 6 additional masses, fixed on the carriage, with accurate weighed values: 0 kg, 1.05 kg, 3.0266 kg, 4.7882 kg, 9.9162 kg, and 14.704 kg.

Hence for the samples ( ) k τ with the additional mass ( )

a k m , (1) becomes: ( ) ( ) ( ) ( )
( k ) a 0 0 a k C V off I m q m g m q g F sign q F q τ τ = + - + -+ + + ɺɺ ɺɺ ɺ ɺ (22)
At a first step, we proceed separately for each (k) experiment to get 6 different usual identifications. Keeping only the samples at average constant velocities without acceleration, the load corresponds to the gravity effect. One uses usual LS method as described in III.B. The variation of C F as a function of the 6 payload values is given in Fig. 6 (see the asterisk*), in order to show the linear dependency on load.

At a second step, to identify the usual model with all samples, one distinguishes the weighed mass aw m and the mass ae m estimated by the identification. Thus, the usual model is written:

( ) ( ) ( ) ( ) ( ) ( ) ( ) ae k k a 0 0 aw k aw k C V off m I m q m g m q g ... m ... F sign q F q τ τ = + - + -+ + + ɺɺ ɺɺ ɺ ɺ (23)
Then, the sampled measurements, for k from 1 to 6, are concatenated using the m aw(k) corresponding to each experiment (k), to get the linear system:

usual usual usual usual = + Y W χ ρ ( 24
)
with the measurements vector, the observation matrix, and the vector of base parameters below:

T T T usual (1) (6)
...

  = =   Y τ τ τ (25) usual ( ) ( ) aw sign = - -     W q g m q g q q 1 ɺɺ ɺɺ ɺ ɺ (26) T usual ae a 0 0 C V off aw m I m m F F m τ   = +     χ ( 27
)
At a third step, the proposed model is identified with:

new new new new = + Y W χ ρ (28)
with the measurements vector, the observation matrix, and the vector of base parameters defined as follows:

new usual = = Y Y τ ( 29) new ( ) ( ) ( ) ( )
aw aw sign sign

+ + + + + - - - - -  = - -   - - 
W P q P g P m q g P q P ... P q P g P m q g P q P q ɺɺ ɺɺ ɺ ɺɺ ɺɺ ɺ ɺ

(30) ( )( ) ( ) ( ) ( )( ) ( ) ( ) new T m a 0 m 0 m m off m g a 0 g 0 g g off g V 1 I m 1 m 1 ... 1 I m 1 m 1 F α α α β τ α α α β τ  = + + + +   - + - -  χ (31)
Here + P and - P are diagonal matrices, with: ( ) ( )

i i i ,i i ,i 1 sign 1 sign , 2 2 + - + - = = P P P P ( 32
)
The two models are compared using exactly the same identification method with the same measurements.

C. Results

The values identified with usual IDM and OLS regressions are given in Table I and those with the new IDM in Table II. For each model, Fig. 4 and Fig. 5 present a direct validation comparing the actual τ with its predicted value Ŵ χ . In Fig. 6, it can be seen that the variation of the usual C F is the mean between motor and generator values, except for the 2 first masses because the load is to low and the joint works only in motor mode (see the uncertain area Fig. Table III shows that the new model does not improve the residual if the load variation is too small. Indeed, in this case (additional mass less than 3 kg), the joint runs mostly in motor behaviour, and the usual model is then sufficient. For additional mass greater than 5 kg, the load variation is large enough to justify the use of the proposed model: one observes a decrease of 30% in the relative norm of errors.

VI. DISCUSSION

The new IDM with a load-dependent joint friction model brings a substantial improvement for joint whose load can vary significantly. Robots carrying important masses or with large variation of inertial and gravity forces are considered. Moreover, the identification process is the same for both usual and new models, because the new model remains linear in relation to the parameters.

The main difficulty is to distinguish the different behaviours, motor and generator, but a solution has been proposed along this paper. However, the measurements have to be more exciting than usual. Each test has to be done with different loads to highlight the effect on the friction variations. So, this identification protocol is more timeconsuming and the setting up must be adapted for the measurements with additional masses. Moreover, for robots with small load variation, the joint actuates only in one quadrant: the parameters m α and g α of the new model are not excited enough and the results are not better than with the usual. Then, this one will be accurate enough for low load variation. has to consider the load variation rate and the type of the transmission to choose the appropriate model, before starting modeling and identification.

VII. CONCLUSION

This paper has presented a new model of friction depending on load and the process to automatically identify the parameters. This technique was successfully applied on a 1 degree of freedom (dof) prismatic joint robot. From these experiments, it comes that the proposed model is accurate and useful for robots with large load variation.

Future works concern the use of this IDM to find friction parameters for a multi dof robot. The application of the model for different types of transmissions will be also studied to determine the minimum load variation rate where the new IDM is needed. The 3 load-dependent models will be examined as well. A comparison of the method with nonlinear identification will be considered. Once a loaddependent model will be identified for multi-DOF robots, it will be used for torques monitoring and collision detection.

Fig. 1 .

 1 Fig. 1. EMPS prototype to be identified.

Fig. 2 .

 2 Fig. 2. a) Usual friction model with constant FC. b) Parametric effect of the load on friction model.

Fig. 3 .

 3 Fig. 3. a) Four quadrants frame ( q ɺ , out τ ) for motor or generator behavior. b) Asymmetrical friction for a given velocity 0 q ɺ and definition of the

Fig. 4 .Fig. 5 .Fig. 6 .

 456 Fig. 4. Direct validation performed with usual IDM

  The parameters α , β and off τ

			have only two different
	values, m α , m β , off m τ	for the motor quadrants 1 and 3 and
	g α , g β , off g τ for the generator quadrants 2 and 4.
	(	)	( )
	(	)	( )

  can be written in one model:

	τ	=	( P 1 +	+	) m out α τ	+	( P 1 -	-	) α τ g	out	+	...
			... P +	( β	m	( ) sign q ɺ	+	τ	off m	)	+	P -	( β	g	( ) sign q ɺ	+	τ	off g	)	+	V F q ɺ

  Moreover, Table III presents the relative norm of errors ρ Y for the two models and for two sets of experiments.


Mathieu Hatt

Dimitris Visvikis

Catherine Cheze Le Rest

Autocontouring versus manual contouring

Regarding Autocontouring and Manual Contouring: Which Is the Better Method for Target Delineation Using 18F-FDG PET/CT in Non-Small Cell Lung Cancer? By K. Wu et al

M Hatt, D Visvikis, C Cheze Le Rest

To the editor: We read with interest the study of Wu et al [START_REF] Wu | Autocontouring and Manual Contouring: Which Is the Better Method for Target Delineation Using 18F-FDG PET/CT in Non-Small Cell Lung Cancer?[END_REF] regarding autocontouring methodologies for target delineation in PET/CT for NSCLC. 17 NSCLC tumors were delineated with both automated and manual approaches, using either combined PET/CT or CT and PET independently. As expected, the manual contouring of the PET uptake provided a better correlation with the maximum diameter of the primary tumor compared to auto-contouring using a fixed threshold at 50% of the tumor maximum uptake. We believe that this result is largely associated to various shortcomings of using fixed threshold approaches, a point that needs to be clearly emphasized.

The authors have previously demonstrated that the best correlation between the maximum tumor diameters derived from histo-pathological examination in comparison to the image derived ones was obtained using a 50% fixed threshold (2). This conclusion was reached by comparison to the results obtained using other fixed threshold values (from 20% to 55%), with a modest correlation of 0.77 and non-statistically significant differences with the other fixed threshold values tested. Most significantly the use of a 50% fixed threshold led to differences larger than 1cm in half of the tumors considered. Such differences in terms of the maximum tumor diameter will most certainly lead to larger differences in the overall 3D volume. Considering similar comparisons based on 3D NSCLC tumor volumes determined by histo-pathology, other authors have demonstrated that an "optimal" threshold value cannot be determined, showing considerable variability ranging from 20% to 42% (31%±11%) of the maximum, while CT-based volumes significantly overestimated the pathologic volume (3).

It is therefore important to emphasize that a fixed threshold (irrespective of its absolute value) is not an adequate methodology to address the delineation of elevated uptake signal in PET, due to its binary, deterministic nature and lack of robustness versus varying contrast and noise conditions [START_REF] Hatt | A fuzzy Bayesian locally adaptive segmentation approach for volume determination in PET[END_REF][START_REF] Nestle | Comparison of Different Methods for Delineation of 18F-FDG PET-Positive Tissue for Target Volume Definition in Radiotherapy of Patients with Non-Small Cell Lung Cancer[END_REF]. In order to account for these widely documented literature findings concerning tumor target delineation incorporating PET uptake information, fixed thresholding should be avoided and at the very least, methodologies considering target-to-background ratios such as adaptive thresholding [START_REF] Nestle | Comparison of Different Methods for Delineation of 18F-FDG PET-Positive Tissue for Target Volume Definition in Radiotherapy of Patients with Non-Small Cell Lung Cancer[END_REF][START_REF] Daisne | Tri-dimensional automatic segmentation of PET volumes based on measured source-to-background ratios: influence of reconstruction algorithms[END_REF] should be favored. Eventually, the wider availability of automatic segmentation approaches (7-10), some of which allow accounting for the presence of heterogeneous tumor uptake distributions [START_REF] Hatt | Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications[END_REF], may allow improving the accuracy and reproducibility associated with adaptive thresholding [START_REF] Hatt | Reproducibility of 18F-FDG and 18F-FLT PET tumor volume measurements[END_REF] for functional tumor volume determination.

Considering all these facts we do agree with the authors that manual contouring should be preferred to autocontouring at 50% threshold for functional tumor volume delineation. On the other hand, one should consider that manual delineation of the PET uptake is not the ideal approach either for multiple reasons. Most importantly it represents a long process particularly when it has to be performed in 3D and it is inherently of very low reproducibility [START_REF] Hatt | Reproducibility of 18F-FDG and 18F-FLT PET tumor volume measurements[END_REF].

We therefore recommend that future studies investigating this issue include the use of advanced image segmentation approaches (4-10) which have demonstrated improved performance in comparison to fixed threshold, and may therefore lead to alternative / complementary conclusions regarding the role of manual contouring. Irrespective of the performance of a segmentation algorithm, operator intervention will always be necessary in order to appropriately identify the functional uptake of interest and avoid the inclusion of non-tumor specific activity uptake.


Abstract-Usually, the joint transmission friction model for robots is composed of a viscous friction force and of a constant dry sliding friction force. However, according to the Coulomb law, the dry friction force depends linearly on the load driven by the transmission. It follows that this effect must be taken into account for robots working with large variation of the payload or inertial and gravity forces, and actuated with transmissions as speed reducer, screw-nut or worm gear. This paper proposes a new inverse dynamic identification model for n degrees of freedom (dof) serial robot, where the dry sliding friction force is a linear function of both the dynamic and the external forces, with a velocity-dependent coefficient. A new identification procedure groups all the joint data collected while the robot is tracking planned trajectories with different payloads to get a global least squares estimation of inertial and new friction parameters. An experimental validation is carried out with a joint of an industrial robot.

I. INTRODUCTION

HE usual identification method is based on the inverse dynamic model (IDM) which is linear in relation to the dynamic parameters, and uses least squares (LS) technique. This procedure has been successfully applied to identify inertial and friction parameters of a lot of prototypes and industrial robots [START_REF] Atkeson | Estimation of Inertial Parameters of Manipulator Loads and Links[END_REF]- [START_REF] Kawasaki | Terminal-link parameter estimation and trajectory control of robotic manipulators[END_REF]. An approximation of the kinematic Coulomb friction, ( ) C F sign q ɺ , is widely used to model friction force at non zero velocity q ɺ , assuming that the friction force F C is a constant parameter. It is identified by moving the robot without any load (or external force) or with constant given payloads [START_REF] Khalil | Identification of the payload inertial parameters of industrial manipulators[END_REF]. However, the Coulomb law suggests that F C depends on the transmission force driven in the mechanism. It depends on the dynamic and on the external forces applied through the joint drive chain. Consequently for robots with varying load, the identified IDIM is no more accurate when the transmission uses industrial speed reducer, screw-nut or worm gear because their efficiency significantly varies with the transmitted force. The significant dependence on load has been often observed for transmission elements [START_REF] Gogoussis | Coulomb friction effects on the dynamics of bearings and transmissions in precision robot mechanisms[END_REF]- [START_REF] Garrec | ABLE, an Innovative Transparent Exoskeleton for the Upper-Limb[END_REF] through direct measurement procedures. Moreover, the mechanism efficiency depends on the sense of power transfer leading to two distinct sets of friction parameters. In addition, when the robot moves at very low velocity, as for teleoperation, one observes a velocity-dependency of the dry friction.

This paper presents a new inverse dynamic identification model for n degrees of freedom (dof) serial robot, where the dry sliding friction force C F is a linear function of both the dynamic and the external forces, with asymmetrical behavior depending on the signs of joint force and velocity, and a variation depending on the velocity amplitude. A new identification procedure is proposed. All the joint position and joint force data collected in several experiments, while the robot is tracking planned trajectories with different payloads, are concatenated to calculate a global least squares estimation of both the inertial and the new friction parameters.

An experimental validation is carried out on the third joint of an industrial robot: Stäubli RX130L [25]. Both models are compared.

II. USUAL INVERSE DYNAMIC MODELING AND IDENTIFICATION

A. Modeling

In the following, all mechanical variables are given in SI units in the joint space. All forces, positions, velocities and accelerations have a conventional positive sign in the same direction. That defines a motor convention for the mechanical behavior.

The dynamic model of a rigid robot composed of n moving links is written as follows [START_REF] Khalil | Modeling, identification and control of robots[END_REF]:

dyn in f ext = + + τ τ τ τ (1)
where:

• dyn τ is the (nx1) vector of dynamic forces due to the inertial, centrifugal, Coriolis, and gravitational effects:

( ) ( ) ( ) dyn , = + + ɺɺ ɺ ɺ τ M q q C q q q Q q (2)
where q, ɺ q and ɺɺ q are respectively the (nx1) vectors of generalized joint positions, velocities and accelerations, M(q) is the (nxn) robot inertia matrix, ( , ) ɺ C q q is the (nxn) matrix of centrifugal and Coriolis effects, Q(q) is the (nx1) vector of gravitational forces.

• in τ is the (nx1) input torque vector on the motor side of the drive chain:

Dynamic Identification of Robots with a Dry Friction Model Depending on Load and Velocity

P. Hamon (1) , M. Gautier (2) , and P. Garrec (1) (1) CEA, LIST, Interactive Robotics Laboratory, 18 route du Panorama, BP6, Fontenay-aux-Roses, F-92265, France. (2) University of Nantes/IRCCyN, 1 rue de la Noë, BP 92101, Nantes Cedex 03, F-44321, France.

T ( )

0 in f f f = - τ g v v (3)
where f v is the (nx1) vector of current references of the current amplifiers, 0 f v is a (nx1) vector of amplifiers offsets, g f is the (nxn) matrix of the drive gains,

f i t = g NG K (4)
N is the (nxn) gear ratios matrix of the joint drive chains ( m = ɺ ɺ q Nq with m ɺ q the (nx1) velocities vector on the motor side), G i is the (nxn) static gains diagonal matrix of the current amplifiers, K t is the (nxn) diagonal matrix of the electromagnetic motor torque constants [START_REF] Restrepo | Calibration of drive chain of robot joints[END_REF].

• f τ is the (nx1) vector of the loss force due to frictions.

Usually, it is approximated with a viscous friction and a dry friction:

( )

f V C Coff = - - - ɺ ɺ τ F q F sign q F (5)
where F V is the (nxn) diagonal matrix of viscous parameters, F C is the (nxn) diagonal matrix of dry friction parameters, and sign(.) denotes the sign function, F Coff is a (nx1) vector of asymmetrical Coulomb friction force between positive and negative velocities. This friction model is linear to F V and F C (Fig. 1.a).

• ext τ is the (nx1) external forces vector in the joint space.

Thus (1) becomes:

( ) ( ) ( )

0 dyn ext f f V C Coff f f out V C off - = - - - + ⇔ = - - - ɺ ɺ ɺ ɺ τ τ g v F q F sign q F g v τ τ F q F sign q τ (6)
where out dyn ext = τ τ τ is the output force (the load force) of the drive chain,

0 off Coff f f = + τ F g v is an offset force that
regroups the amplifier offset and the asymmetrical Coulomb friction coefficient.

f f = τ g v (7)
is the motor force, without offset, and defined by v f which is the current reference calculated by the numerical control and stored for the identification. Then (1) can be rewritten as the inverse dynamic model (IDM) which calculates the motor torque vector τ as a function of the generalized coordinates:

( ) ( ) ( ) ( ) ( ) C V off ext out C V off , = + + + + + - = + + + ɺɺ ɺ ɺ ɺ ɺ ɺ ɺ
τ M q q C q q q Q q F sign q F q τ τ τ F sign q F q τ (8)

B. Identification

The choice of the modified Denavit and Hartenberg frames attached to each link allows to obtain a dynamic model linear in relation to a set of standard dynamic parameters St χ [START_REF] Gautier | Identification of robot dynamics[END_REF], [START_REF] Khalil | Modeling, identification and control of robots[END_REF]:

( )

, , = ɺ ɺɺ τ D q q q χ [START_REF] Khalil | Identification of the payload inertial parameters of industrial manipulators[END_REF] where ( )

St

, , ɺ ɺɺ D q q q is the regressor and St χ is the vector of the standard parameters which are the coefficients XX j , XY j , XZ j , YY j , YZ j , ZZ j of the inertia tensor of link j denoted j J j , the mass of the link j called m j , the first moments vector of link j around the origin of frame j denoted j M j = [MX j MY j MZ j ] T , the friction coefficients F Vj , F Cj , the actuator inertia called Ia j , and the offset τ off j . The velocities and accelerations are calculated using well tuned band pass filtering of the joint position [START_REF] Gautier | Dynamic identification of robots with power model[END_REF].

The base parameters are the minimum number of parameters from which the dynamic model can be calculated. They are obtained by eliminating and by regrouping some standard inertial parameters [START_REF] Gautier | Direct calculation of minimum set of inertial parameters of serial robots[END_REF], [START_REF] Gautier | Numerical calculation of the base inertial parameters[END_REF]. The minimal inverse dynamic model can be written as: (

) , , = ɺ ɺɺ τ D q q q χ [START_REF] Kawasaki | Terminal-link parameter estimation and trajectory control of robotic manipulators[END_REF] where ( ) , , ɺ ɺɺ D q q q is the minimal regressor and χ is the vector of the base parameters.

The inverse dynamic model ( 10) is sampled while the robot is tracking a trajectory to get an over-determined linear system such that [START_REF] Gautier | Identification of robot dynamics[END_REF]: [START_REF] Khalil | Modeling, identification and control of robots[END_REF] with Y(τ) the measurements vector, W the observation matrix and ρ the vector of errors.

( ) ( ) , , = + ɺ ɺɺ Y τ W q q q χ ρ
The LS solution χ minimizes the 2-norm of the vector of errors ρ. W is a (r×b) full rank and well conditioned matrix where e r N x n = , with N e the number of samples on the trajectories. The LS solution χ is given by:

( )

1 T T ˆ- + = = χ W W W Y W Y (12)
It is calculated using the QR factorization of W. Standard deviations i χ σ are estimated using classical and simple results from statistics. The matrix W is supposed to be deterministic, and ρ, a zero-mean additive independent noise, with a standard deviation such as:

( ) T E 2 r ρρ ρ σ = = C ρρ I ( 13
)
where E is the expectation operator and I r , the (r×r) identity matrix. An unbiased estimation of σ ρ is:

( ) 2 2 ˆr b ρ σ = - - Y Wχ ( 14
)
The covariance matrix of the standard deviation is calculated as follows:

T 2 T 1 χχ ρ E ( )( ) σ ( ) ˆˆˆˆ-  = - - =   C χ χ χ χ W W ( 15
) i 2 ˆˆˆii C χ χχ σ = is the i th diagonal coefficient of ˆχχ C . The relative standard deviation ri % χ
σ is given by:

ri i ˆˆi % 100 χ χ σ σ χ = (16)
However, experimental data are corrupted by noise and error modeling and W is not deterministic. This problem can be solved by filtering the measurement vector Y and the columns of the observation matrix W as described in [START_REF] Gautier | Dynamic identification of robots with power model[END_REF], [START_REF] Gautier | Extended Kalman filtering and weighted least squares dynamic identification of robot[END_REF].

III. NEW DRY FRICTION MODEL AND IDENTIFICATION

In this section, we introduce a dry friction model dependent on the load, that is out τ , and on the velocity ɺ q .

A. Load-Dependent Friction Model

The Coulomb friction is still written ( ) C sign ɺ F q , with F C a (nxn) diagonal matrix. But here, for each link j,

( ) C j , j

F

(the (j,j) th element of the matrix C F ) depends linearly on the absolute value of the load of joint j which is out j τ (Fig. 1.b), [START_REF] Gogoussis | Coulomb friction effects on the dynamics of bearings and transmissions in precision robot mechanisms[END_REF]- [START_REF] Garrec | ABLE, an Innovative Transparent Exoskeleton for the Upper-Limb[END_REF]. As one can see in II.B, out j τ is a function of , , ɺ ɺɺ q q q and is linear in relation to base parameters.

Then the inverse dynamic model for each link j becomes:

( ) ( ) ( ) j out j j out j j j V j , j j off j sign q q τ τ α τ β τ = + + + + ɺ ɺ F ( 17
)
where j α and j β are parameters to be identified. These new parameters depend on the mechanical structure of the reducers used to actuate the robot.

For ease of understanding, the subscript j is omitted for all variables in the following to simplify the notation. The inverse dynamic model can be written as follows:

( ) ( ) out out V off sign q sign q F q τ τ α τ β τ = + + + + ɺ ɺ ɺ (18)
And with ( )

out out out sign τ τ τ = and ( ) ( ) ( ) ( ) out out out sign sign q sign q sign P τ τ = = ɺ ɺ , one obtains: ( ) ( ) out out out V off sign P sign q F q τ τ ατ β τ = + + + + ɺ ɺ (19)
Thus, the IDM depends on the signs of the output power out out P q τ = ɺ . One defines 4 quadrants in the frame ( ) out q,τ ɺ , which can be grouped two by two (Fig. 2.a). In the quadrants 1 and 3, out P is positive and the actuator has a motor behavior. In the quadrants 2 and 4, out P is negative and the actuator has a generator behavior which may save the power to the power supply, assuming a 4 quadrants amplifier.

B. Dry Friction Model Depending on the Power Sign

In the model [START_REF] Garrec | ABLE, an Innovative Transparent Exoskeleton for the Upper-Limb[END_REF]

out m out m V off out g out g V off P 0 1 sign q F q P 0 1 sign q F q τ α τ β τ τ α τ β τ > ⇒ = + + + +    < ⇒ = - + + +   ɺ ɺ ɺ ɺ (20)
The model ( 20) is illustrated in Fig. 2.b for a constant velocity 0 q ɺ . This model is not valid anymore for very low forces in the stiction area ( q 0 = ɺ ): one approximates the friction as the limit of model [START_REF] Olsson | Friction Models and Friction Compensation[END_REF] in the rectangle ( ) b) Asymmetrical friction for a given velocity 0 q ɺ and the stiction area.

; ( ) ( ) m V 0 off g V 0 off g F q F q 1 β τ β τ α + + + - - ɺ ɺ .

C. Dry Friction Model Depending on the Velocity

For a robot moving at low velocities, one observes a dry friction variation, functions of the velocity, which is similar to the Stribeck model (Fig. 1.c), [START_REF] Olsson | Friction Models and Friction Compensation[END_REF], [START_REF] Armstrong-Hélouvry | Control of machines with friction[END_REF], [START_REF] Swevers | An Integrated Friction Model Structure with Improved Presliding Behavior for Accurate Friction Compensation[END_REF].

if and ( ) if and ( ) if

out st out st out out st q 0 F F F sign q 0 F F q q 0 τ τ τ τ = <   = = ≥   ≠  ɺ ɺ ɺ ɺ (21)
with: ( )

( ) ( ) ( ) s q / q sl st sl F q F F F e sign q - = + - ɺ ɺ ɺ ɺ ( 22
)
where S q ɺ is a velocity constant, st F is the dry friction in stiction and sl F is the dry friction in sliding mode.

To combine the variation due to the load [START_REF] Pelchen | Modeling and simulation the efficiency of gearboxes and of planetary gearboxes[END_REF] with the one due to velocity [START_REF] Swevers | An Integrated Friction Model Structure with Improved Presliding Behavior for Accurate Friction Compensation[END_REF], one takes:

0 q 0 < ɺ 0 q 0 < ɺ 0 q 0 > ɺ 0 q0 > ɺ b) q ɺ β -β out τ increases a) q ɺ Fc -Fc f Coff ( F ) τ - + c) q ɺ out τ increases f Coff ( F ) τ - + f Coff ( F ) τ - + ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) S S S
q q out out out q q q q out out F q e sign q e sign P e sign q α τ β γ τ δ α τ β = -, the inverse dynamic model becomes:

( ) ( ) ( ) ( ) S S
S S out q q q q m out m out m m V off out q q q q g out g out g g V off P 0 a b e c sign q d e sign q F q P 0 a b e c sign q d e sign q F q τ τ τ τ

τ τ τ τ - - - - > ⇒   = + + + + +   < ⇒   = - + + + +  ɺ ɺ ɺ ɺ ɺ ɺ ɺ ɺ i ɺ ɺ ɺ i ɺ ɺ ɺ (25)

D. Friction Identification Method

In order to keep a IDM linear in relation to the parameters, one decides on an a priori value of S q ɺ . This constant represents the exponential transitional behavior between stiction and sliding, that is about 3 S q * ɺ , more or less 5%.

Measurements show that the amplitude of this transitional behavior is close to 10% of the nominal velocity 1.2 rad/s, that is 0.04 rad/s S q = ɺ . A final adjustment to 0.03 rad/s S q = ɺ at the moment of the identification gives a minimal residual ρ a little lower. This value for S q ɺ is close to the value given in [START_REF] Swevers | An Integrated Friction Model Structure with Improved Presliding Behavior for Accurate Friction Compensation[END_REF] for a Kuka IR 161 robot. Furthermore, the model (25) depends on the sign of out P which is unknown. To overcome this problem, the samples of τ measurements are selected outside of the stiction area ( q 0 = ɺ -Fig. 2.b) in order to get the same sign for out τ and τ . allows to get the sign of out P with:

( ) ( ) ( ) ( )
out out sign P sign q sign q sign P τ τ

= = = ɺ ɺ (26)
One can then write the IDM linear in relation to parameters and use the LS technique. To have only one expression instead of two in (25), 3 variables are introduced, P + , P -, and E xp , defined by: ( )

1 sign P P 2 + + = , 0 1 P P + > ⇔ = , 0 0 P P + < ⇔ = (27) ( ) 1 sign P P P 2 - + - = = (28) S q q xp E e - = ɺ ɺ (29)
The inverse dynamic model is then written:

( ) ( ) ( ) ( ) ( ) ( ) m m xp out g g xp out m m xp g g xp V off P a b E P a b E ... ... P c d E sign q P c d E sign q F q τ τ τ τ + - + - = + + - + + + + + + ɺ ɺ ɺ (30)
As out τ is linear in relation to parameters, so is τ .

IV. EXPERIMENTAL SETUP AND IDENTIFICATION

A. Study case: Stäubli RX130L Robot

The Stäubli RX130L robot is an industrial robot with 6 rotational joints. The joint 3 has been chosen for this study because unlike the joint 1, it has large gravity variation, and no compensation gravity spring contrary to the joint 2. The links 1 and 2 are lined up and locked in a vertical position. The arm 3 is composed of the links 4, 5 and 6 brought into line with the link 3 and locked (Fig. 3), with a total mass of about 30 kg and a length of 1.33 m. The maximum velocity is 1.2 rad/s and the maximum acceptable load at the extremity is 10 kg.

The inverse dynamic model of joint 3 is written:

( ) ( ) ( ) 3 3 3 3 3 3 3 C3 3 V3 3 off 3
J q MX gcos q MY gsin q F sign q F q τ τ

= + + + + + ɺɺ ɺ ɺ (31)
where:

• 3 3 3 J Ia ZZ = +
is the inertia moment 3 Ia of the drive chain plus the inertia moment ZZ 3 of the arm,

• 2 m/s g 9.81 = is the gravity acceleration.

All variables and parameters are given in SI units on the joint space. In the following, the subscript 3 is omitted to simplify the notation.

B. Data Acquisition

The identification of dynamic parameters is carried out with and without payloads: two different additional masses can be fixed to the arm extremity. To excite properly the friction parameters to be identified, sinusoidal and trapezoidal velocities trajectories were used.

The estimation of q ɺ and q ɺɺ are carried out with pass band filtering of q consisting of a low pass Butterworth filter and a central derivative algorithm. The Matlab function filtfilt can be used [23]. The motor torque is calculated using the current reference [START_REF] Gautier | Dynamic identification of robots with power model[END_REF]. In order to cancel high frequency ripple in τ , the vector Y and the columns of the observation matrix W are both low pass filtered and decimated. This parallel filtering procedure is carried out with the Matlab decimate function [START_REF] Swevers | Optimal Robot excitation and Identification[END_REF], [START_REF] Kawasaki | Terminal-link parameter estimation and trajectory control of robotic manipulators[END_REF].

C. Identification

To identify the load-dependent friction, measurements with known payloads are used. Gravity and inertial forces due to the additional mass fixed to the robot extremity have to be added in the IDM. Let R m be the frame set at the center of gravity G m of the additional masse M a , and parallel to the frame ( )

3 3 3 3 R x , y ,z
linked to the arm (Fig. 3). One gives the inertia matrix I Ga of the additional mass which is a disk with a radius r and a thickness l:

( ) ( ) ( ) m m 2 a 2 2 Ga a 2 2 a G ,R M r 2 0 0 I 0 M r 4 l 12 0 0 0 M r 4 l 12     = +     +   (32)
The vector of translation between R 3 and R m is

T a T L 0 0 =   
 and as the 2 frames are parallel, one can apply the Huygens theorem:

( ) ( ) 2 a 2 2 a a 2 2 2 a a a M r 2 0 0 J 0 M r 4 l 12 0 0 0 M r 4 l 12 M L     = +     + +   (33)
As the terms r² and e² are negligible, compared with

2 a
L , one keeps only the term

2 a a M L q ɺɺ .
For the gravity, considering the vector of translation T, one has:

( )

a a M L g cos q .
Thus, for the samples ( ) k τ with an additional mass

( ) a k M , (31) becomes: ( ) ( ) ( ) ( ) ( ) ( ) ( )
2 k a k a a k a C V off
Jq MXg cos q MYg sin q M L q ... ...M L g cos q F sign q F q

τ τ = + + + + + + + ɺɺ ɺɺ ɺ ɺ (34)
where:

• ( ) a k M
is one of the additional masses, fixed to robot extremity, with accurate weighed values: 0 kg, 3.4584 kg and 6.970 kg,

• a L is the length from the joint 3 to the additional mass position (measured distance): 1.277 m At a first step, to identify the usual model with all samples, one distinguishes the weighed mass aw M and the mass ae M estimated by the identification. Thus, the usual model is:

( ) ( ) ( ) ( ) ( ) ( ) ( ( )) ( ) k ae k aw k a a C V off aw k
Jq MXg cos q MYg sin q ... M ... M L L q g cos q F sign q F q M τ τ

= + + + + + + + ɺɺ ɺɺ ɺ ɺ (35)
Then, the sampled measurements, for k from 1 to 3, are concatenated using the ( ) aw k M corresponding to each experiment (k), to get the linear system:

usual usual usual usual = + Y W χ ρ ( 36
)
with the measurements vector, the observation matrix, and the vector of base parameters below:

T T T T usual (1) (2) (3)
  = =   Y τ τ τ τ (37) ( ) ( ) ( ( )) ( ) usual aw a a sign = +     ɺɺ ɺɺ ɺ ɺ W
q gcos q gsin q M L Lq gcos q q q 1 (38)

T ae usual C V off aw M J MX MY F F M τ   =     χ ( 39
)
At a second step, the proposed model is identified with:

new new new new = + Y W χ ρ ( 40
)
with the measurements vector, the observation matrix, and the vector of base parameters defined as follows:

new usual = = Y Y τ ( 41) ( ) ( ) ( ) ( ) ( ( )) ( ( )) ( ) ( ) ( ) (
+ + + + + + + + - - - - - - -  =  + - - -
ɺɺ ɺɺ ɺɺ ɺɺ ɺɺ ɺɺ W P q P E q P gcos q P E gcos q P gsin q P E gsin q P M L L q gcos q P E M L L q+ gcos q P q P E q P gcos q P E gcos q P gsin q P E gsin q P M L ( L q gcos q P E M L L q gcos q P sign q P E sign q P sign q P E sign q q 1 ( Jq MXgcos q MYgsin q M L L q M gcos q

τ = + + + + ɺɺ ɺɺ
in the inverse dynamic model (30).

Here + P , - P , and xp E are diagonal matrices, with:

( ) ( ) ( ) i S q q i i i ,i i ,i xp i ,i 1 sign 1 sign , , e 2 2 - + - + - = = = ɺ ɺ P P P P Ε ( 44
)
The two models are compared using exactly the same identification method with the same measurements.

D. Results

The significant values identified with usual IDM and OLS regressions are given in Table I and those with the new IDM in Table II (the parameters with a large relative deviation are not significant and have been eliminated). For each model, Fig. 4 and Fig. 5

V. DISCUSSION

The parameters of the new model are identifiable (low standard deviation) and so significant. The identification process does not change as the new model is still linear in relation to the parameters. The originality is that the global identification groups all measurements, with all payloads, in only one LS process. The main difficulty is to distinguish the different behaviours, motor and generator, but a solution has been proposed along this paper. One can also note that the measurements have to be more exciting than usual: each test has to be done with different loads and low velocities to highlight the effect on the friction variations. So, this identification protocol is more time-consuming and the setting up must be adapted for the measurements with additional masses.

The figures of direct validation show an improvement of the estimated torque by the new model, which is confirmed by the Table III. Indeed, one observes a decrease of 34% in the relative norm of errors. The improvement is mostly important for the low velocities where the errors are divided by two, thanks to the new model (decrease of 46%). At high velocity, the friction term with the exponential function approaches zero, and the new model is equivalent to the usual.

Moreover, the Table IV shows that the model is especially interesting for robots carrying some payloads. However, for a robot without payload but with high gravity variation, as the third joint of the RX130L, one obtains still a decrease of 19% of the errors.

Finally, this new model can be easily applied to a multi dof robot, using (30) for each joint j. This model is important for example in teleoperation, where the robots work at reduced velocity and can carry payloads or perform tasks with the effector subjected to external forces.

VI. CONCLUSION

This paper has presented a new dry friction model, with load-and velocity-dependency, and its identification method. The inverse dynamic model and the identification of its parameters have been successfully validated on a rotational joint of an industrial robot. As a result, one observes a significant improvement comparing to the usual model, for joints with large load variations, and especially at low velocity. Robots carrying important masses or with large inertial or gravity variations are concerned. In addition, this technique can be applied to multi dof robots.

Future works concern the application of this model to the multi dof robot and for different types of transmission. Then, the model will be used for torques monitoring and collision detection.

Fig. 1 .

 1 Fig. 1. a) Usual friction model with constant dry friction + viscous friction. b) Model with load-dependent dry friction + viscous c) Model with load-and velocity-dependent dry friction + viscous friction.

Fig. 2 .

 2 Fig. 2. a) Four quadrants frame ( ) out q,τ ɺ for motor or generator behavior.

  dependence on power sign, one has 2 sets of parameters α , β , γ , δ for the 2 behaviors: motor and generator

Fig. 3 .

 3 Fig. 3. RX130L drawing: joints 1, 2, 4, 5 and 6 locked in position.

  J a MX b MX a MY b MY a b ... ... a J b J a MX b MX a MY b MY a b ... ... c d c d F τ

Fig. 4 .Fig. 5 .

 45 Fig. 4. Direct validation performed with usual IDM.

  , α and β do not depend on the output power sign. But, generally they take different values: m

			α
	and m β for the motor quadrants, and g α and g β for the
	generator quadrants.
	(	)	( )
	(	)	( )

  )

	new	xp	...
	...	xp	xp	...
	...	a a a	xp a a a	...
	...	xp	...
	...	xp	xp	...
	...	a

  present a direct validation comparing the actual τ with its predicted value Ŵ χ . Moreover, TableIIIpresents the relative norm of errors ρ Y for the two models and for several sets of experiments: all measurements (all velocities), with low velocities (0 to 10% of the maximum velocity) or high velocities (35% to 100% of the maximum velocity). Finally, Table IV compares the relative norms of errors for the two models, with two different identifications: the first one is carried out with all measurements, that is with variation of the payload fixed to arm extremity, and the second one is carried out with only the samples obtained without payload.As one can see in all figures and tables, the new dynamic model improves the residual.

TABLE IV RELATIVE

 IV NORM OF ERRORS FOR 2 IDENTIFICATIONS

				500
				400
				300
				200
			τ (N.m)	100
			Input torque	-200 -100 0
				-300
				-400	Measurement
					Estimation
				-500	Error
				0	2	4	6	8	10	12	14
								Samples	x 10 4
	Identification carried out	Usual model New model
	With payload variations	0.0733	0.0484
	Without payload	0.0742	0.0598

ACKNOWLEDGMENT

The authors thank the AREVA society [24] for the availability of the Stäubli RX130L robot.


est destinée au dépôt

Toxicity assessment of nanoparticles: impact of physico-chemical properties and adsorption artefacts on biological responses

Mélanie Pailleux, Jérémie Pourchez, Philippe Grosseau, Delphine Boudard,

Michèle Cottier

To cite this version:

Mélanie Pailleux, Jérémie Pourchez, Philippe Grosseau, Delphine Boudard, Michèle Cottier. Toxicity assessment of nanoparticles: impact of physico-chemical properties and adsorption artefacts on biological responses. Bulletin du Cancer, 2010, 97, pp.S18. emse-00583184

Toxicity assessment of nanoparticles: impact of physico-chemical properties and adsorption artefacts on biological responses

Introduction: In medicine, the uses of nanoparticles (NP) offer new perspectives in imaging (Wagner, 2006), drug delivery (Salmaso, 2004) or radiotherapy (Hainfeld, 2004). However, questions about potential toxic and deleterious effects of nano-structured materials have been raised (Hoet, 2004;Maynard, 2006). Our objective is to develop standardized methods to assess NP toxicity on alveolar macrophages. However, the measurement of biomolecules released in the cell culture supernatants can be modified by their adsorption on the NP. Thus, this adsorption mechanism must be fully understood to avoid misinterpretation of data.

Materials and methods: TNF-α adsorption was evaluated by ELISA kits (R&D Systems®) on aluminum NP. Transmission electron microscopy and X-ray diffraction measurements showed that these nanoparticles had a crystallite size of 10nm. Laser granulometry emphasized agglomerates of 20µm up to 100nm. The specific surface area was evaluated to 157m².g -1 by adsorption of N 2 and analysis through BET method.

Results: The concentration of biomolecules in cell culture supernatants results of two antagonistic mechanisms: their release by the macrophages and their adsorption on NP surface.

We demonstrate a significant adsorption of TNF-α on aluminum NP. Indeed, if 75pg of TNF is put in contact with 12 µg of aluminum NP for 24hours, we measure only 57 pg in supernatants (i.e. 24% wt of TNF adsorbed on aluminum NP).

Conclusions: We have developed a specific protocol in order to evaluate the organo-mineral interactions in terms of affinity and quantity of biomolecules adsorbed to the aluminum NP at thermodynamic equilibrium. The original results generated lead to quantify the pro-inflammatory, cytotoxic and pro-oxidant effects of the NP used, in the absence of any experimental artefacts due to adsorption of biomolecules to NP surface.

Prospects: The physico-chemical characteristics of NP (chemical composition, crystallographic structure, properties of surface, particles shape ...) have an important influence on their biological activity. We will highlight the correlation between the level of toxicity induced by NP and their physico-chemical characteristics taking into account adsorption artefacts on biological responses.

Keywords: nanoparticles; toxicity; cytokine adsorption

L'archive ouverte pluridisciplinaire HAL,


P Chedmail

D Chablat

C Le Roy

To appear in Journal on Industrial, Electronics

Keywords: Cooperative systems, Ergonomics, Manipulator motion-planning, Robot vision systems

This paper presents a new method, based on a multi-agent system and on a digital mock-up technology, to assess an efficient path planner for a manikin or a robot for access and visibility task taking into account ergonomic constraints or joint and mechanical limits. In order to solve this problem, the human operator is integrated in the process optimization to contribute to a global perception of the environment. This operator cooperates, in real-time, with several automatic local elementary agents. The result of this work validates solutions through the digital mock-up; it can be applied to simulate maintenability and mountability tasks.

I. INTRODUCTION

n an industrial environment, the access to a sharable and global view of the enterprise project, product, and/or service appears to be a key factor of success. It improves the triptych delay-quality-cost but also the communication between the different partners and their implication in the project. For these reasons, the Manuscript received January 9, 2002. This work has been experimented in the framework of the European projects CEDIX and ENHANCE, acronyms for "Concurrent Engineering and Design Integrating X-Methodologies" and ''ENHanced AeroNautical Concurrent Engineering''.

A distributed Approach for Access and Visibility

Task with a Manikin and a Robot in a Virtual Reality Environment P. Chedmail, D. Chablat, and Ch. Le Roy I 2/12 digital mock-up (DMU) and its functions are deeply investigated by industrials. Based on computer technology and virtual reality, the DMU consists in a platform of visualization and simulation that can cover different processes and areas during the product lifecycle such as product design, industrialization, production, maintenance, recycling and/or customer support (Figure 1). The digital model enables the earlier identification of possible issues and a better understanding of the processes even, and maybe above all, for actors who are not specialists. Thus, a digital model allows deciding before expensive physical prototypes have been built. Even if evident progresses were noticed and applied in the domain of DMUs, significant progresses are still awaited for a placement in an industrial context. As a matter of fact, the digital model offers a way to explore areas such as maintenance or ergonomics of the product that were traditionally ignored at the beginning phases of a project; new processes must consequently be developed.

Through the integration of a manikin or a robot in a virtual environment, the suitability of a product, its shape and functions can be assessed. In the same time, it becomes possible to settle the process for assembling with a robot the different components of the product. Moreover, when simulating a task that should be performed by an operator with a virtual manikin model, feasibility, access and visibility can be checked. The conditions of the performances in terms of efforts, constraints and comfort can also be analyzed. Modifications on the process, on the product or on the task itself may follow but also a better and earlier training of the operators to enhance their performances in the real environment. Moreover, such a use of the DMU leads to a better conformance to health and safety standards, to a maximization of human comfort and safety and an optimization of the robot abilities.

With virtual reality tools such as 3D manipulators (Figure 2), it is possible to manipulate the object as easily as in a real to manipulate the object as easily as in a real environment. Some drawbacks are the difficulty to manipulate the object with as ease as in a real environment, due to the lack of kinematics constraints and the automatic collision avoidance. As a matter of fact, interference detection between parts is often displayed through color changes of parts in collision but collision is not avoided.

Another approach consists in integrating automatic functionality into the virtual environment in order to ease the user's task. Many research topics in the framework of robotics dealing with the definition of collision-free trajectories for solid objects are also valid in the DMU. Some methodologies need a global perception of the environment, like (i) visibility graphs proposed by Lozano-Pérez and Wesley [START_REF] Lozano-Pérez | An algorithm for planning collision-free paths among polyhedral obstacles[END_REF], (ii) geodesic graphs proposed by Tournassoud [START_REF] Tournassoud | Géométrie et intelligence artificielle pour les robots[END_REF], or (iii) Voronoï's diagrams [START_REF] Boissonnat | Perception et planification[END_REF]. However, these techniques are very CPU consuming but lead to a solution if it exists. Some other methodologies consider the moves of the object only in its close or local environment. The success of these methods is not guaranteed due to the existence of local minima. A specific method was proposed by Khatib [START_REF] Khatib | Real time obstacle avoidance for manipulators and mobile robots[END_REF] and enhanced by Barraquand and Latombe [START_REF] Barraquand | Robot Motion Planning: A Distributed Representation Approach[END_REF]. In this method, Khatib's potentials method is coupled with an optimization method that minimizes the distance to the target and avoids collisions. All these techniques are limited, either by the computation cost, or the existence of local minima as explained by Namgung [START_REF] Namgung | Two Dimensional Collision-Free Path Planning Using Linear Parametric Curve[END_REF]. For these reasons a designer, is required in order to validate one of the different paths found or to avoid local minima. The accessibility and the optimum placement of an operator to perform a task is also a matter of path planning that we propose to solve with DMU. In order to shorten time for a trajectory search, to avoid local minima and to suppress tiresome on-line manipulation, we intend to settle for a mixed approach of the above presented methodologies. Thus, we use local algorithm abilities and global view ability of a human operator, with the same approach as [START_REF] Chedmail | A distributed approach for accessibility and maintainability check with a manikin[END_REF]. Among the local algorithms, we present these ones contributing to a better visibility of the task, in term of access but also in term of comfort.

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II. PATH PLANNING AND MULTI-AGENT ARCHITECTURE

The above chapter points out the local abilities of several path planners. Furthermore, human global vision can lead to a coherent partition of the path planning issue. We intend to manage simultaneously these local and global abilities by building an interaction between human and algorithms in order to have an efficient path planner [START_REF] Chedmail | Integrated design and artificial reality: accessibility and path planning[END_REF] for a manikin or a robot with respect of ergonomic constraints or joints and mechanical limits of the robot.

A. History

Several studies about co-operation between algorithm processes and human operators have shown the great potential of co-operation between agents. First concepts were proposed by Ferber [START_REF] Ferber | Les systèmes multi-agents, vers une intelligence collective[END_REF]. These studies led to the creation of a "Concurrent Engineering" methodology based on network principles, interacting with cells or modules that represent skills, rules or workgroups. Such studies can be linked to work done by Arcand and Pelletier [START_REF] Arcand | Cognition Based Multi-Agent Architecture[END_REF] for the design of a cognition based multi-agent architecture. This work presents a multi-agent architecture with human and society behavior. It uses cognitive psychology results within a cooperative human and computer system.

All these studies show the important potential of multi-agent systems (MAS). Consequently, we built a manikin "positioner", based on MAS, that combines human interactive integration and algorithms.

B. Choice of the multi-agent architecture

Several workgroups have established rules for the definition of the agents and their interactions, even for dynamic architectures according to the environment evolution [START_REF] Ferber | Les systèmes multi-agents, vers une intelligence collective[END_REF][START_REF] Jennings | Roadmap of agent research and development[END_REF]. From these analyses, we keep the following points for an elementary agent definition. An elementary agent:  is able to act in a common environment,  is driven by a set of tendencies (goal, satisfaction function, etc.),  has its own resources,  can see locally its environment,  has a partial representation of the environment,

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 has some skills and offers some services,  has behavior in order to satisfy its goal, taking into account its resources and abilities, according to its environment analysis and to the information it receives.

The points above show that direct communications between agents are not considered. In fact, our architecture implies that each agent acts on its set of variables from the environment according to its goal. Our Multi Agent System (MAS) will be a black board based architecture.

C. Path planning and MAS

The method used in automatic path planners is schematized Figure 3a. A human global vision can lead to a coherent partition of the main trajectory as suggested in [START_REF] Hwang | Human Computer Cooperation in Interactive Motion Planning[END_REF]. Consequently, another method is the integration of an operator to manage the evolution of the variables, taking into account his or her global perception of the environment (Figure 3b). To enhance path planning, a coupled approach using multi-agent and distributed principles as it is defined in [START_REF] Chedmail | Integrated design and artificial reality: accessibility and path planning[END_REF] can be build; this approach manages simultaneously the two, local and global, abilities as suggested Figure 3c. The virtual site enables graphic visualization of the database for the human operator, and communicates positions of the virtual objects to external processes. As a matter of fact, this last scheme is clearly correlated with the "blackboard" based MAS architecture. This principle is described in [START_REF] Ferber | Les systèmes multi-agents, vers une intelligence collective[END_REF][START_REF] Ferber | Problématique des univers multi-agents intelligents[END_REF][START_REF] Jennings | Roadmap of agent research and development[END_REF]. A schematic presentation is presented on Figure 4. The only medium between agents is the common database of the virtual reality environment. The human operator can be considered as an elementary agent for the system, co-operating with some other elementary agents that are simple algorithms.

D. Considered approach

The approach we retained is the one proposed in [START_REF] Chedmail | A distributed approach for accessibility and maintainability check with a manikin[END_REF] whose purpose was to validate new CAD/CAM 5/12 solutions based on a distributed approach using a virtual reality environment. This method has successfully shown its advantage by demonstrating in a realistic time the assembly task of several components with a manikin. Such problem was previously solved by using real and physical mock-ups. We kept the same architecture and developed some elementary agents for the manikin (Figure 5). In fact, each agent can be recursively divided in elementary agents. Each agent i acts with a specific time sampling which is pre-defined by a specific rate of activity i. When acting, the agent sends a contribution, normalized by a value  i  to the environment and/or the manipulated object (the manikin in our study). In Figure 6, we represent the Collision agent with a rate of activity equal to 1, the Attraction agent has a rate of 3 and Operator and Manikin agents a rate of 9. This periodicity of the agent actions is a characteristic of the architecture: it expresses a priority between each of the goals of the agents. To supervise each agent activity, we use an event programming method where the main process collects agent contributions and updates the database [START_REF] Chedmail | A distributed approach for accessibility and maintainability check with a manikin[END_REF]. The normalization of the actions of the agents (the values i) induces that the actions are relative and not absolute.

E. Examples

The former method is illustrated with two different examples. The first one (Figure 7) uses the MAS for testing the ability of a manikin for mounting an oxygen bottle inside an airplane cockpit through a trap. During the path planning process, the operator has acted in order to drive the oxygen bottle toward the middle of the trap. The other agents have acted in order to avoid collisions and to attract the oxygen bottle toward the final location. The real time duration is approximately 30s. The number of degrees of freedom is equal to 23.

The second example (Figure 8) is related to the automatic manipulation of a robot which base is attracted toward a wall. The joints are managed by the agents in order to avoid a collision and to solve the associated inverse kinematic model.

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III. VISIBILITY AND MAINTAINABILITY CHECK WITH MULTI-AGENT SYSTEM IN VIRTUAL REALITY

A. Introduction

For the visibility check, we have focused our attention on the trunk and the head configurations of a manikin (resp. the end-effector and the film camera orientation of a robot). The joint between the head and the trunk is characterized by three rotations  b ,  b and  b whose range limits are defined by ergonomic constraints (Figure 9) (resp. the joint limits of the robot). These data can be found using the results of ergonomic research [START_REF] Riffard | Accessibilité d'un opérateur humain en environnement très contraint -Placement optimal et postures[END_REF]. To solve the problem of visibility, we define a cone C whose vertex is centered between the two eyes (resp. the center of the film camera) and whose base is located in the plane orthogonal to u, centered on the target (Figure 10). The cone width  c is variable.

Thus, additionally to the position and orientation variables of all parts in the cluttered environment (including the manikin itself), we consider in particular:  Three degrees of freedom for the manikin (resp. the robot) to move it in the x-y plane: x m = (x m , y m ,  m ,) T . It is also possible to take into account a degree of freedom z m if we want to give to the manikin (resp. the robot) the capacity to clear an obstacle.  Three degrees of freedom for the head joint (resp. wrist joints) to manage the manikin (resp. robot) vision: q b = ( b ,  b ,  b ) t with their corresponding joint constraints. The normalized contributions from the agents are defined with two fixed parameters:  pos for translating moves and  or for rotating moves.

x m z m y m  m  b R b  b R s R m t u C  b visual axis s y target T S  c q 1
x m y m z m q 2 q 3 q 4 q 5 q 6

Figure 10. Manikin skeleton and robot kinematics; visibility cone and target definition.

B. Agents ensuring visibility access and comfort for manikin, and visibility access for robot

We present below all the elementary agents used in our system to solve the access and visibility task.  Attraction agent for the manikin (resp. the robot) The goal of the attraction agent is to enable the manikin (resp. the robot) to reach the target with the best trunk posture (resp. the best base placement of the robot), that is:  To orient the projection of y m on the floor plane collinear to the projection of u on the same plane by rotation of  m (Figure 10),  To position x m and y m, coordinates of the manikin (resp. the robot) in the environment floor, as close as possible to the target position (Figure 10), (and for the robot.  q 1 up to q n using the inverse kinematic model. This last agent acts in order to keep the robot posture, as much as possible, in the same aspect, or posture, or configuration as defined in [START_REF] Ph | Ability of a robot to travel through Cartesian free workspace in an environment with obstacles[END_REF].)

This attraction agent only considers the target and does not take care of the environment. This agent is similar to the attraction force introduced by Khatib [START_REF] Khatib | Real time obstacle avoidance for manipulators and mobile robots[END_REF], and gives the required contributions x att , y att , and  att according to the attraction toward a target referenced as above. These contributions, which act on the manikin (resp. the robot) leading member position and orientation (in our case the trunk (resp. the base of the robot and its kinematics)), are normalized according to  pos and  or .

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 Repulsion agent between manikin (resp. robot) and the cluttered environment

This repulsion agent acts in order to avoid the collisions between the manikin (resp. the robot) and the cluttered environment, which may be static or mobile.

Several possibilities can be used in order to build a collision criterion. The intersection between two parts A and B in collision, as shown by Figure 11a, can be quantified in several ways. We can consider either the volume V of collision, or the surface of collision, or the depth D max of collision (Figure 11b). The main drawback of these approaches comes out from the difficulty to determine these values. Moreover, 3D topological operations are not easy because many of the virtual reality softwares use polyhedral surfaces to define 3D objects. To determine D move , the distance to avoid the collision (Figure 11b), we have to store former positions of the mobile (manikin or robot), so this quantification does not use only the database at a given instant but uses former information. This solution cannot be kept with our blackboard architecture that only provides global environment status at an instant. Another quantification of the collision is possible with the use of the collision line between the two parts. With this collision line, we can determine the maximum surface S or the maximum length of the collision line l =  l i (Figure 11c). By the end, we compute the gradient of the collision criterion according to the Cartesian environment frame using a finite difference approximation.

From the gradient vector of the collision length

 Head orientation agent

The goal of the head orientation agent is to rotate the head of the manikin (resp. of the film camera) in order to observe the target. It ensures the optimum configuration that maximizes visual comfort (resp. the visibility of the target). Finding the optimum configuration consists in minimizing efforts on the joint coupling the head with the trunk and minimizing ocular efforts (resp. mechanical efforts or isotropy of the configuration). We simplify the problem by considering that the manikin has a monocular vision, defined by a cone whose principal axis, called vision axis, is along y s and whose vertex is the center of manikin eyes (in that case, the manikin vision is similar to that of a film camera on a robot). If the target belongs to the vision axis, ocular efforts are considered null. Our purpose consists in orienting y s such as it becomes collinear to u by rotation of  b and  b (Figure 10), subject to joint limits. A joint limit average for an adult is given in Figure 9. In the case of a film camera, the corresponding values will be the optical characteristics of the film camera.

The algorithm of this agent is similar to the attraction agent algorithm presented there above; contributions  head and  head, after normalization, are applied to the joint coupling the head to the manikin trunk (resp. the wrist joints of the robot).

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 Visibility agent

The visibility agent ensures that the target is visible, that is, no interference occurs between the segment ST linking the center of manikin eyes (resp. of the film camera) and the target, and the cluttered environment.

The repulsion algorithm is exactly the same as the one presented there above:

 we determine the collision line length,  if non equal to zero, normalized contributions are determined from x vis , y vis , and  vis computed by the visibility agent according to the gradient vector of the collision length,  contributions are applied to the manikin trunk (resp. the base of the robot).

It is to notice that some contributions may also be applied to the head orientation (resp. the film camera orientation). This is due to the fact that by turning the head, collisions between the simplified cone with the environment may also occur.

The use of a simplified cone offers the advantage of combining an ergonomic criterion with the repulsion effect. As a matter of fact, when the vision axis y s is inside the cone C (Figure 10), we widen the cone, respecting a maximum limit. If not, we decrease its vertex angle, also with respect of a minimum limit that corresponds to the initial condition when starting this visibility agent. The maximum limit may be expressed according to the target size or/and to the type of task to perform: proximal or distant visual checking, global or specific area to control.  Operator agent on the manikin (resp. on the robot) One of the aims of the study is to integrate a human operator within the MAS in order to operate in realtime. The operator has a global view of the cluttered environment displayed by means of the virtual reality software. Her or his action must be simple and efficient. For that purpose, we use a Logitech SpaceMouse device (see /12) that allows us to manipulate a body with six degrees of freedom.

The action of the operator agent only considers the move of the leading object, which is in our case the manikin trunk (resp. the base of the robot). Parameters come out from position x op and y op and orientation  op returned from the SpaceMouse. These contributions are normalized, in the same way as with the attraction or repulsion agents.

IV. RESULTS AND CONCLUSIONS

This method has been tested to check the visual accessibility of specific elements under a trap of an aircraft. The digital model is presented in Figure 12 and the list of elementary agents is depicted in the master agent window in Figure 13. In this example, the repulsion agent for the manikin (Repulsion), the visibility agent (Visual) and the head orientation agent (Cone) have a specific rate of activity equal to 1, meaning that their actions have priority but it is possible for the operator to change in real-time this activity rate. Since the action of each agent is independent from the other elementary agents, it is possible to inactivate some of them (Pause/Work buttons). The values of  pos and  or , which are used to normalize the agent contributions, can also be modified in real-time (Position and Orientation buttons) in order to adapt the contribution to the scale of the environment or to the task to perform.

Our experience shows that the contribution of the human operator is important in the optimization process. Indeed, if the automatic agent process fails (which can be the case when the cone used in the visibility agent is in collision with the environment), the human operator can: 10/12  give to the MAS intermediate targets that will lead to a valid solution;  move the manikin to a place where the MAS process could find a solution. On the other hand, the MAS allow the human operator to act more quickly and more easily with the DMU. The elementary agents guarantee a good physical and visual comfort and enable to quantify and qualify it, which would be a hard task for the human operator, even with sophisticated virtual reality devices. For instance, we can evaluate the rotations of the head and see how they are dispersed from a neutral configuration, inducing little effort. Moreover, the MAS permits a very good local collision detection and avoidance without any effort of the human operator.

The advantage of the MAS is to enable the combination of independent elementary agents to solve complex tasks. Thus, the agents participating in the visibility task can be coupled with agents enabling accessibility and maintainability as proposed by Chedmail [START_REF] Chedmail | A distributed approach for accessibility and maintainability check with a manikin[END_REF]. The purpose of further works consists in a global coupling of manikin manipulations taking into account visual and ergonomic constraints and the manipulation of moving objects as robots. The result of this work is currently implemented in an industrial context with Snecma Motors [START_REF] Maillé | Multi-Agent Approach and Emergence of a Virtual Human Behaviour in a VR Environment[END_REF].

Figure 1 .

 1 Figure 1. Manufacturing simulation.Figure2. SpaceMouse (LogitechTM).

Figure 2 .

 2 Figure 1. Manufacturing simulation.Figure2. SpaceMouse (LogitechTM).

Figure 3 .

 3 Figure 3. Co-operation principles.

Figure 4 .

 4 Figure 4. Blackboard principle with co-operating agents.

Figure 5 .Figure 6 .

 56 Figure 5. Co-operating agents and path planning activity[START_REF] Chedmail | A distributed approach for accessibility and maintainability check with a manikin[END_REF].

Figure 7 .Figure 8 .

 78 Figure 7. Trajectory path planning of a manikin using the MAS

Figure 9 .

 9 Figure 9. Example of joint limits and visibility capacity of a manikin and of a film camera.

  Two intersecting parts. (b) Volume in collision.(c) Intersection line.

Figure 11 .

 11 Figure 11. Collision criteria.

  rep , y rep , and  rep are computed by the repulsion agent. These contributions, acting on the manikin trunk position and orientation, are normalized according to  pos and  or .

Figure 12 .

 12 Figure 12. Digital model of a trap of an aircraft.

Figure 13 .

 13 Figure 13. Master agent window.


Mélanie Pailleux

Jérémie Pourchez

Delphine Boudard

Philippe Grosseau

Michèle Cottier

Study of the toxicity of inhaled ultrafine engineered powder: example of boehmite nanoparticles

Study of the toxicity of inhaled ultrafine engineered powder: example of boehmite nanoparticles

Mélanie PAILLEUX 1,2 , Jérémie POURCHEZ 1,3 , Delphine BOUDARD 1,4,5,6 , Philippe GROSSEAU 2 , Michèle COTTIER 1,4,5,6 . Boehmite nanoparticles (NP) can be used as a vector for vaccines, replacing conventional adjuvants but also in industry as abrasives, catalysts, substrates for electronic circuits, refractory materials. Thus, the boehmite NP constitutes a good model of study taking into account its various applications: in the industry as well as in the nanomedecine field. This important use of boehmite NP makes particular interest of the evaluation of its potential toxicity. Some toxicological works have been yet performed, the health risk induced by boehmite NP is still not completely characterized. Nevertheless, some authors have shown from In vitro experiments that the boehmite can cause an inflammation characterized by production of IL-8 and a certain cytotoxicity associated with a LDH release. As a result, the boehmite NP must be considered as potential risk factors for health. This work aims to develop a multidisciplinary approach to highlight the correlation between the toxicity of alumina engineered NP and their physico-chemical characteristics. Accuracy of measurements depends on cell production after contact with particles, but also depends on the ability of biomolecules to get adsorbed on the NP (Val, 2009). Therefore, mechanisms of biomolecules adsorption on NP must be fully understood to avoid misinterpretation of data.

Transmission electron microscopy and X-ray diffraction measurements showed that these NP had a crystallite size of 10nm. In vitro biological effects were evaluated on macrophages (RAW 264.7 cell line) after a 24 hours exposure to alumina NP. Inflammatory response was evaluated using TNF-α production in the cell culture supernatant. Biomolecules-NP interactions were evaluated on acellular medium measuring both affinity and amount of adsorbed TNF-α on alumina NP after 24 hours (ELISA kit, R&D Systems ® ).

Results showed an important inflammatory response (e.g. a TNF-α production about 400pg/mL for a dose of 120µg/10 cells) as compared to positive (quartz, 2000pg/mL) toxicity control. Moreover, we 6 demonstrated a significant adsorption of TNF-α on alumina NP. Indeed, 75pg of TNF-α were added to 12 µg of NP in cell culture medium. The mass measured after 24 hours incubation was only 65 pg, so 2.88pg of adsorbed TNF-α meaning that 10% of artifacts.

Thus adsorption artefacts must be taken into account when evaluating biological responses in order to reliably compare NP with different physico-chemical characteristics.

Val S, 2009. Carbon black and titanium dioxide nanoparticles induce pro-inflammatory responses in bronchial epithelial cells: Need for multiparametric evaluation due to adsorption artifacts. Inhalation Toxicology, 21, 115-122.