Carlos Wilfrido Ponce Quiroga
email: carlos-wilfrido.ponce-quiroga@univ-lorraine.fr

T Raharijaona
email: thibaut.raharijaona@univ-lorraine.fr

Gabriel Abba
email: gabriel.abba@univ-lorraine.fr

Gabriel Abba Offline

C W Ponce-Quiroga

Offline approach towards obstacle avoidance in collaborative robots

Keywords: obstacle avoidance, collaborative robots, collision-free path planning, ROS, MoveIt!, Dubins' curves variation

Performance improvement in Human-Robot Collaboration is one of the objectives in collaborative robots. This improvement can be achieved through the correct obstacle avoidance planification of collision-free paths, focusing on terms of path continuity, final motion times and average speed of the end-effector. We propose an offline path planning approach considering an obstacle given an initial linear trajectory from pose A to pose B. By using ROS, MoveIt! and Rviz we generate three trajectories varying the sagitta of our equations for evaluation and determining the best performing trajectory. Results in simulations are compared with the actual motions using the UR10e cobot.

Introduction

The combination of the human capabilities and dexterity in conjunction with the machine efficiency has been a dream for the past several decades. Conventional automation technology has arrived at a bottle-neck, where this collaboration is the potential solution for the industrial dilemma which improvement can be achieved through the correct obstacle avoidance planification of collision-free paths, focusing on terms of path continuity, final motion times and average speed of the end-effector. The general collaborative robot obstacle avoidance path planning refers to plan a feasible path of the manipulator end-effector, from a starting pose towards a target pose, while avoiding collision with any obstacle during this operation. Human decision-making capabilities combined with the robots' precision and efficiency, may indeed enable a remarkable level of performance, however, this collaboration must into consideration the security for human beings [START_REF] Krüger | Cooperation of human and machines in assembly lines[END_REF], [START_REF] Tan | Human-robot collaboration in cellular manufacturing: Design and development[END_REF].

In the past decade, collaborative robots are used for many industrial automation applications. Because of that, the standard ISO/TS15066:2016 was submitted into revision and confirmed its validity in 2019

[3]: it announces the specifications for the safety requirements of collaborative robots and the workspace environment. Different collision avoidance strategies to assure the safety of the operators. In [START_REF] Ebert | Safe human-robot-coexistence: emergencystop using a high-speed vision-chip[END_REF], an emergency stop is activated when an operator entered a hazard zone. However, productivity can be severely reduced due to potential frequent stops.

In [START_REF] Schmidt | Depth camera based collision avoidance via active robot control[END_REF], four control solutions are proposed for collision avoidance: i) speed reduction and warning, ii) calling a stop interruption, iii) moving away from the oncoming operator, and iiii) changing the path to the target at run-time.

The level of difficulty to resolve the obstacle avoidance problem depends on different factors such as the environment, the type of robot, the task to execute, in order for the robot to move safely around the obstacles [START_REF] Gill | A parallel collision-avoidance algorithm for robot manipulators[END_REF]. This planning is performed in two different cases: i) off-line planning, where a complete description of the workspace is known in advance (this is a global approach, finding the solutions before implementing them); and ii) on-line planning, where the robot makes decisions given the current state of the environment and moves toward the target, however, the path found may not correspond to the most efficient or optimal one. The problematic addressed here is to find suitable trajectories for obstacle avoidance in collaborative robots, such that have good performances in terms of overall speed along the trajectory and total time for them to accomplish the task while assuring path smoothness.

Path planning

Path planning is a subproblem of the general motion planning problem that consists in finding collision-free paths between an initial and final configuration. While trajectory planning is an additional step where the series of robot configurations are specified as a function of time. The trajectory planning should as well take into consideration constraints such as limits on joint velocities, accelerations or torques [START_REF] Lynch | Modern robotics: mechanics, planning, and control[END_REF].

Path continuity

Continuity notion is fundamental in the definition of a path. The continuity problem refers to the geometric continuity of a path in terms of curvature continuity and tangential continuity. There are two types of continuities [START_REF] Ravankar | Path Smoothing Techniques in Robot Navigation: State-of-the-Art, Current and Future Challenges[END_REF]: 1) Geometric continuity and 2) Parametric continuity. Geometric continuity guarantees that the endpoints of segments of the path meet, and tangent vector's directions are equal. Parametric continuity guarantees that the endpoints of segments of the path meet, and that both, the tangent vector's direction and magnitudes are equal.

As cited by Ravankar [START_REF] Ravankar | Path Smoothing Techniques in Robot Navigation: State-of-the-Art, Current and Future Challenges[END_REF], parametric continuity mean smoothness both of the curve and of its parameterization. Geometric continuity simply means the smoothness of the track that the robot traverses. Dubins curves [START_REF] Dubins | On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents[END_REF] were originally proposed in 1957 to describe a continuously differentiable path ( continuity type). The initial interest was to obtain a path of minimal length for a given particle.

Since then, these types of curves have been used in applications in mobile robotics and UAV's for obstacle avoidance purposes [START_REF] Lin | Path planning using 3D Dubins Curve for Unmanned Aerial Vehicles[END_REF]- [START_REF] Yang | 2D Dubins Path in Environments with Obstacle[END_REF].

Arcs path planning

The approach proposed in this work consists in the use of a variation of the Dubins curve. Dubins curves applications mainly consist of the computation of the smallest path length, constrained by the maximum curvature possible by the mobile system (which can be a negligible restriction for manipulators robots). For manipulators there is not restriction of specific maximum curvature of a path, therefore the final path is composed by several arc with different arc radiuses.

To simplify the calculation, we will consider a single particle obstacle as first approach.

Considering a plane XZ with origin in O as in Figure 3.1, we can compute these equations given four main parameters: h, r, R2, R3 (equations are shown in Table 3. To pass from the 2D case to a 3D case, we take a finite n series of waypoints from the 2D and then each one of the points is rotated towards the new coordinate system orientation, finally the waypoints are translated to the 3D case scenario (see Figure 3.2) To achieve these, a series of equations and functions was developed in MATLAB, by computing quaternions, cross products, dot products, etc.

4

Cartesian trajectory generation

ROS, MoveIt! and Rviz

ROS is an open source robot operating system [START_REF] Quigley | ROS: an open-source Robot Operating System[END_REF]. It provides a structured communication layer that allows to send and to receive messages above the host operating systems. Many researchers all around the world have used this and it is up today one of the most popular frameworks in robotics.

MoveIt! is a motion planning framework that works alongside ROS [START_REF] Coleman | Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study[END_REF]. It has been integrated successfully with many robots, including the UR family robots. It uses OMPL motion planning plugins, OROCOS Kinematics and Dynamics Library (KDL) for inverse and forward kinematics, Fast Collision Library (FCL) for collision detection.

Rviz is a three-dimensional visualizer integrated in the core of ROS, used to visualize robots, the environment and sensor data. It can be used for simulations and real-time visualization when on-line with the robot. It is configurable and may integrate plugins: MoveIt! for example.

Universal Robots ROS Driver is open-source set of ROS libraries that provides a stable interface between the UR robots and ROS [START_REF]Universal Robots A/S[END_REF]. It uses the Real-Time Data Exchange (RTDE) and it allows factory calibration of the robot inside ROS. It provides all the necessary configuration files for a transparent interaction and to integrate the use of the MoveIt! framework.

Nodes and modules interaction

The main node of MoveIt is called move_group. This node uses all the individual elements together to provide the interface of a set of ROS services and actions. This node can be configured through the ROS Param Server, getting as well the URDF and SRDF configuration files that describe the environment and the robot itself. Figure 4.1 depicts the basic architecture and interaction between the principal nodes when using MoveIt with ROS, in addition to our Obstacle Avoidance Planner. The move_group node interacts with the robot to get current state information (joint positions, etc.) and to talk to the robot controllers through the JointTrajectoryAction interface. The Obstacle Avoidance Planner with its configurable parameters its done initially. The waypoints information of our precomputed path is given to the move_group_interface in order to validate the proposed path. This is achieved through the execution of several services (Inverse Kinematics, Planning Scene, Plan Validity, etc.) to finally determine the corresponding trajectory, considering all the environment and robot constraints present in the configuration files.

The move_group can authorize or not the execution of a trajectory. In the real robot is through the JointTrajectoryAction services, taking the state information of the robot and communicating through the ROS Robot Controllers; in simulation environment this is performed through the GUI -Rviz interface, by using fake controllers, fake robot actuators and state positions.

Simulations and experimental results

The end-effector is constrained to maintain the same orientation along the different trajectories. The obstacle avoidance motion is to be done above the original linear trajectory ( -axis). The proposed poses for validation are the following (position in meters and roll-pitch-yaw in radians):

The main interest is to reduce the final time (total task duration) of the trajectory, while safely avoiding any given obstacle. Three different avoiding trajectories are generated and simulated to compare the final times results and their overall performance in the real robot.

For the path planner algorithm, the coordinate of the obstacle is introduced as input: = .

We vary the sagitta by a factor of , to obtain three different obstacle avoidance trajectories: Similarly, Table 5.1 presents the total distance traveled by the end-effector, the total time taken to complete the trajectory, and its average speed for each one of the four trajectory cases. Comparing the average speed among the trajectories gives a first insight of the overall performance of the different trajectories. From these values, we can conclude that the case (d) has the best performance between the obstacle avoidance trajectories, in terms of time and average speed; however, case (d) decreased 44.7% (0.158 m/s) in speed with respect from the original linear trajectory (a) (0.285 m/s). In terms of time, case (d) took 91% (5.590 s) longer to finish with respect to the case (a) (2.921 s). The MoveIt! real results are shown in the right of Table 5.1. It can be seen that the results are quite similar between simulations and real case scenarios. There are just some minor improvements: in terms of time the real obstacle avoidance trajectories perform between 0.01s and 0.05s faster which can be negligible (it represents less than 1% of difference). The best resulting obstacle avoidance trajectory results to be for the case (d), both in simulation and real scenario. The motion performances of all the trajectories are quite similar. Figure 5.3 shows the resulting joint positions, joint velocities and the reflected end-effector linear speed. We can remark that the joint positions present undesired oscillations, which are reflected in the end-effector linear speed. This comes from the Cartesian trajectory generation by using MoveIt!, which lacks from the possibility to directly adjust a desired end-effector speed, which is essential to meet the security requirements of the ISO/TS as it can be seen that it surpasses the 0.25m/s authorized collaboration mode speed.

Conclusion and discussion

We successfully generated feasible trajectories with a proposed offline approach that focus on terms of path continuity, final time and average speed of the end-effector. The collision-free path based on Dubins curve variation, has the advantage of low computation cost as it is uses radius, sagittas, tangents with good continuity in terms of curvature and tangential vectors, resulting in a C 1 continuous path that effectively avoids a given obstacle.

The obstacle particle model allows to examine in detail the performance of the trajectory, the advantage lays in the simplicity, yet scalability, that this approach can allow. The simulation framework is easily transferred to a real case scenario. The recent development of the ROS framework in conjunction with MoveIt! is of great help to push forward into the robotics research problems, however this may come with some disadvantages. MoveIt! with ROS and the UR10e, allows to seemingly setup all the necessary tools without much problem for simulation and real implementation, thanks to all the documentation and the robotics community. The main setback of this proposition (when using MoveIt!) is that we can-not control the Cartesian speed of the end-effector trajectory, only the path without any time parametrization. The results validate the viability of the obstacle avoidance planner, and some key conclusions are extracted, in terms of the expected performances in the real case scenario. These results were verified and performed under real condition with the UR10e collaborative robot. Further analysis and computations are to be proposed, to address the problematics or disadvantages encountered so far in this scope.

Figure 2

 2 Figure 2 Parametric continuity paths. (a) Discontinuous segments. (b) C 0 continuity. (c) C 1 continuity. (d) C 2 continuity.Figure 2 Sample path using principle of Dubins curves

Figure 2

 2 Figure 2 Parametric continuity paths. (a) Discontinuous segments. (b) C 0 continuity. (c) C 1 continuity. (d) C 2 continuity.Figure 2 Sample path using principle of Dubins curves

Figure 2 .

 2 2 depicts a sample smoothing path using Dubins curves, which is composed by the combination of circular arcs and straight segments. Points B, C and D are points that represent continuous motion, by having the same tangent vector of motion (magnitude and direction).

  Figure 3.1 Geometric diagram for equations computation Figure 3.2 Three avoiding trajectories in 2D and in 3D

Figure 4 . 1

 41 Figure 4.1 Basic architecture with ROS-MoveIt! + Obstacle Avoidance Planner

Figure 5 . 1 Figure 5 . 2

 5152 Figure 5.1 UR10e trail trajectories: (a) linear trajectory; obstacle avoidance trajectories (b), (c) and (d) Figure 5.2 Resulting end-effector positions in the Cartesian space

Figure 5 .

 5 Figure 5.3 MoveIt! real case (d): 3D position trajectory, joint positions, joint velocities, and end-effector linear speed

Table 3 .

 3

	Arc 2	Arc 1 (green)	Arc3

1 Equations used to compute the arcs path

Table 5 .

 5 1 Quantitative assessment of simulation trajectories and robot actual trajectories execution

			SIM	SIM	MoveIt! real	MoveIt! real
		Total distance	Total time	Average speed	Total time	Average speed
	Trajectory	(m)	(s)	(m/s)	(s)	(m/s)
	Case (a) Linear	0.833	2.921 [100%]	0.285 [100%]	3.0 [100%]	0.278 [100%]
	Case (b) with	0.989	6.806 (+133%)	0.145 (-49%)	6.76 (+125%)	0.146 (-47%)
	Case (c) with	0.930	6.223 (+113%)	0.149 (-48%)	6.19 (+106%)	0.150 (-46%)
	Case (d) with	0.882	5.590 (+91%)	0.158 (-45%)	5.58 (+86%)	0.158 (-43%)

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Model F Forget
email: forget@lmd.jussieu.fr

F Montmessin

S Cha

E Marcq

J Schaeffer

Y Wanherdrick

3D SIMULATIONS OF THE EARLY MARS CLIMATE WITH A GENERAL CIRCULATION

Introduction

The environmental conditions that existed on Mars during the Noachian period are subject to debate in the community. In any case, there are compelling evidence that these conditions were different than what they became later in the amazonian and possibly the Hesperian periods. Indeed, most of the old cratered terrains are disected by valley networks (thought to have been carved by flowing liquid water), whereas younger surface are almost devoid of such valleys. In addition, there are evidence that the erosion rate was much higher during the early noachian than later [1]. Flowing water is surprising on early Mars because the solar luminosity was significantly lower than today. Even with the thick atmosphere (up to several bars) that is expected to have existed on Mars at the time, simple 1D models based on a purely gaseous atmosphere predicted climate conditions too cold to allow liquid water to flow [2]. On this basis, some authors have suggested that the difference resulted from a stronger geothermalism during that period, and that a warm climate was not necessary to explain the valley network [3]. However, other authors claim that a warm, wet early climate capable of supporting rainfall and surface runoff is the most plausible scenario for explaining the entire suite of geologic features in the Martian cratered highlands [1].

To help understand this key issue in Mars science, it is important to extend the initial climate simulations that concluded that early Mars must have been cold [2,3], since these calculations were performed with very simple 1D models [4]. Such improvements have been performed by several authors, and in particular it has been suggested that the CO ¡ ice clouds that must have formed in such an atmosphere could have produced a strong "scattering" greenhouse effect sufficient to warm the planet above the freezing point of water [5]. However, there again, these simulations were performed with simple 1D models, from which it is difficult to predict the environmental conditions.

A Global Climate model for early Mars

To improve our understanding of the early Mars Climate, we have developped a 3D general circulation model similar to the one used on current Earth or Mars to study the details of the climate today. Our first objective is to answer the following questions : how is the Martian climate modified if 1) the surface pressure is increased up to several bars (our baseline: 2 bars) and 2) if the sun luminosity is decreased by 25account the heat possibly released by impacts during short periods, although it may have played a role [6] For this purpose, we have coupled the Martian General Circulation model developped at LMD [7] with a sophisticated correlated k distribution model developped at NASA Ames Research Center. It is a narrow band model which computes the radiative transfer at both solar and thermal wavelengths (from 0.3 to 250 microns). The correlated-k's for each bands are generated from a line-by-line code using the HITEMP data base from HITRAN. In addition, pressure induced absorption by CO ¡ in included using a simple parameterisation from an analytical formula produced by Moore et al. (1971). This is a major source of uncertainty.

In addition to the radiative transfer and to the parameterisation that are usually included in such a climate models (dynamical core to solve the 3D fluid dynamic equations, subgrid scale turbulent and convective mixing, surface heat balance and subsurface heat conduction, etc...), we have included parameterisations to account for the condensation, transport, gravitational sedimentation and radiative effects of the CO ¡ ice clouds that readily form in a thick CO ¡ atmosphere. model. Using the correlated k distribution model allowed us to compute the complex radiative transfer processes (scattering in the thermal infrared) that cannot usually be accounted for with the usual GCM's wide band model.

Results

Preliminary results obtained assuming a 2 bars atmosphere suggest that, even without taking into account the radiative effect of CO ¡ clouds, temperature near or above the freezing point of water may be obtained seasonally in the summer hemisphere. The diurnal amplitude of surface temperature is small, and therefore warm temperatures may last for long periods. CO ¡ ice clouds are found to form almost everywhere on the planet in the upper atmosphere above 40 km. Their radiative effect on the climate is very model dependent but, in most cases, should correspond to a warming of the surface.

Ultimately, such a model may enable us to simulate the water cycle by applying parameterisations currently used in Earth models. It will be interesting to investigate wether snow or rain could have occur on such a planet.

Third Mars Polar Science Conference (2003) 8070.pdf


Y Dong

A Bouali

N Mechbal

L Bleron

T Raharijaona

Towards wood dust collection during robotic grooving

The wood construction industry has integrated the challenges of robotics and introduced the use of five-axis machining for more than 30 years. However, the wood dust and chips are often contaminated with different chemicals which causes health risks. In this paper, we present an experimental analysis of the physical properties of chips by observing the motion behavior with a single high-speed camera. The experiments are led with an industrial gantry robot during the grooving process. The results show that we can estimate and quantify the velocity direction of wood chip emission in a range of 5m/s near the tool-material interaction. We also constructed two suction hoods and we experimentally show that chip collection is better when the tube is tilted near the tool.

I. INTRODUCTION

Wood manufacturing operations emit air pollutants and dust particles. When not properly removed, dust particles can cause problems in the machining systems: damage and immediate loss of performance [START_REF] Barański | Experimental analysis of chip removing system in circular sawing machine[END_REF]. Breathing dust particles also increases the risk of nasal cavity cancer and lung cancer among workers with cumulative exposure [START_REF] Vallières | Occupational exposure to wood dust and risk of lung cancer in two population-based case-control studies in montreal, canada[END_REF]. Thus, centralized aspiration systems have become mandatory in the wood industries. These systems efficiently remove dust particles from the traditional wood machining process, but still need improvements for Computer Numerical Control (CNC) machining [START_REF] Varga | Aerodynamic assessment of the extraction attachment of cnc processing machinery[END_REF].

Indeed, the efficiency of chip and dust extraction depends on the machining process. Dust creation in CNC drilling has been studied in [START_REF] Rogoziński | Dust creation in cnc drilling of wood composites[END_REF]. The size of the obtained particles when drilling without a proper suction system might pose a potential risk to the health of woodworkers. The results are similar when considering sawing operations [START_REF] Baranski | Experimental examination and modification of chip suction system in circular sawing machine[END_REF] and the milling process in [START_REF] Palmqvist | Emission of dust in planing and milling of wood[END_REF]. Moreover, cutting parameters impact the dust emission and the surface quality during wood machining [START_REF] Kos | Influence of woodworking machine cutting parameters on the surrounding air dustiness[END_REF], [START_REF] Ugulino | Effect of cutting parameters on dust emission and surface roughness during helical planing red oak wood[END_REF]. It has been demonstrated that the shape, the size, and the amount of emitted particles during sawing process mainly depend on the form, physical and mechanical properties of sawed wood as well as on the shape, dimensions, type of machine, sharpness of the cutting blade, and technical and technological conditions of the sawing process [START_REF] Nasir | Characterization, optimization, and acoustic emission monitoring of airborne dust emission during wood sawing[END_REF].

When considering CNC machining, it is usual to see the suction hood fixed above the spindle. The efficiency of such a technique is limited due to the large workspace and the Fig. 1: Picture of wood dust emission during the robotic grooving. We constructed an experimental setup composed of an industrial gantry robot to understand how dust particles are emitted for operational conditions such as feed speed and rotational speed. complexity of the tool path that is allowed by using a 5-axis CNC. It has been demonstrated in [START_REF] Benamar | Recycling of wood dust-laden atmospheres in workshops[END_REF] that the efficiency of the extraction system is usually limited.

Contribution: In this paper, we present an experimental setup dedicated to the observation of emitted particles during a woodworking process: robotic grooving.

The experimental setup is based on the use of a high-speed camera. The setup enables the observation of particles with diameters ranging from 0.1mm to 3mm. We succeeded in experimentally (i) determining the directions of emission of the wood chips, (ii) estimating the velocity magnitude of the particles, and (iii) building and testing a centralized suction system to improve dust collection near the tool. The suction system is tilted. While tilted, it succeeded in capturing an average of 50% of the emitted dust.

II. STATE OF THE ART A. Origin of dust and chips

The expected quantity by volume of dust and chips produced in the unit time is determined by the width and depth of cut and the feed speed. The quantity by mass (kg/s) is obtained by multiplying the volume with the wood density [START_REF] Magoss | Dust Extraction and Handling in the Wood Industry[END_REF]:

m m = b • e • H 60 • ρ m ( 1
)
where b is the width of cut, H is the depth of cut, e is the feed speed in m/min, ρ m is the wood density.

B. Dust and chip collection

The collection of dust is a very difficult task. It may impact the workroom pollution and energy costs of dust extraction. In this paper, we addressed the problem of dust collection during robotic grooving by analyzing the motion behavior of the thrown particles in the air as presented in Fig. 1. To analyze the motion behavior of a wood chip, the dynamics of the particle is modeled. As a first approximation, we can neglect the effect of gravity. The equation of motion reads [START_REF] Sitkei | Particle motion with air resistance[END_REF]:

m dv dt = c e • A • ρ 2 • v 2 (2)
Eq. ( 2) can be written such that:

dv v = B • dt (3)
where,

B = g v 2 (4)
v cr is the terminal velocity. It is determined when the force due to air resistance is identical to the weight of the body. g is the gravitational constant.

If we assume the wood chip is a spherical body with a diameter d and a volume weight γ m , the expression of v cr is:

v cr = 4dγ m 3c e ρ (5)
An integration, with the initial condition t = 0 → v = v 0 , yields the expression:

v = v 0 v 0 • B • t + 1 (6) s = 1 B • ln(v 0 • B • t + 1) ( 7
)
where s is the traveled distance, t is the elapsed time.

III. MATERIALS

A. Experimental Setup objective

We created an experimental setup to capture wood chips and dust generated during the robotic grooving. The setup included the following elements: a gantry robot, an electrospindle carrying machining tools, a high-speed camera, a lighting system with DC generator, and the wooden piece for manufacturing (see Fig 2).

B. Details of the experimental setup

1) The robot: WoodFlex32, the robot we used is a fiveaxis CNC platform. The robotic platform executes machining processes and is based on a multi-functional five-axis gantry robot. Thanks to Lot 1 engine, and the command software, we can pilot this robot easily with CNC ISO code.

2) The machining tool and the electro-spindle: A GIOR-DANO COLOMBO motor is mounted on the robot arm. It can provide a rotational speed of up to 18000rpm. A machining tool is selected to execute the grooving program, with the rotational speed up to 18000rpm and feed speed up to 10m/min. The path width was the same as the diameter of the tool.

3) The high-speed camera: To visualize wood chips and dust during manufacturing, we used a high-speed camera Photron mini UX 50, which provides a 1280*1024 full resolution and can give a frequency of 102400 frames per second with a duration of about 2 seconds. In addition, we used PFV4 software to pilot the camera and save videos. 4) The lighting system: We used two projectors COOLH dedocool as the lighting system, to light up the processing zone (see Fig 2). Each of them has a wattage of 250W and can provide 370000 Lux at 50 cm, with a supply voltage of 24V.

5) The DC generator: A COOLT3 generator powered two projectors. It can transform 240V to 24V.

6) The wooden part: The work piece part is a resinous wood. It is about 150mm long, 70mm in width, and 20mm in height. The work piece is fixed by the screw on the metallic support, to avoid the part from moving during machining.

IV. METHODS

In this section, we describe the methods and tools that will be employed, from camera calibration to the definition of the woodworking parameters. We also present a basic equation of motion for wood particles.

A. Camera calibration

The camera was positioned to have a large field of view and a deep film zone at the same time. We put the camera at 0.5m away from the machining origin.

To calibrate the camera, we used a checkerboard as the calibration reference. Each square of the checkerboard represented 10mm*10mm, so we measured and calibrated by using the software calibration function, and we obtained the following setting: 0.034mm/pixel.

We needed a high frequency to follow the wooden particles during machining, but the higher the frequency, the lower the resolution. Finally, we set the frequency at 5000fps. Consequently, we followed almost all wooden particles and, at the same time, had a good resolution.

To make the video as clear as possible, we needed a higher shutter speed, but we also needed a more powerful light. After several tests with the equipment, we set the shutter speed at 1/40000s.

B. Wood piece modeling and trajectory definition

We used the software CadWork for modeling the machining part (see Fig 3), and Lignocam to define the machining trajectory. We also used the software Lot in height mainly because it was the easiest form to find and process in the laboratory. The length of the part was 150mm, so we had enough length for a camera field of view and, at the same time, we minimized the machining time.

C. Definition of the woodworking parameters

The dust and chips are produced by different woodworking operations such as sawing, milling, molding, sanding, and grooving. In the article, we need to understand for grooving process how dust and chips are produced. We defined the woodworking parameters given in Table I and we identified that the production of dust and chips is determined by the width and depth of cut and the feed speed. We analyzed the motion of dust particles whose size is over 100µm.

Moreover, the chips thrown by the tool are subjected to a drag force consisting of frictional forces and a force due to asymmetrical pressure distribution on the body [START_REF] Magoss | Dust Extraction and Handling in the Wood Industry[END_REF]. This drag force is calculated from the equation below:

F d = c e • A • ρ 2 • v 2 (8)
where c e is the drag coefficient, A is he cross section of the body normal to the flow, ρ is the flow velocity relative to the body, v is the air density. The drag coefficient c e is determined for bodies of regular shapes (sphere, cylinder, disk) and are plotted on logarithmic coordinates as a function of Reynolds number Re. For the case study of the woodworking process, the chips have irregular shapes with a rough surface. Their drag coefficients depend on the instantaneous position and orientation. We can assume that the drag coefficients c e may vary between 0.7 and 0.9.

V. EXPERIMENTAL RESULTS

Depending on the operational characteristics such as cutting speed, depth of cut, and on the mechanical properties of wood species (density, moisture content, resin content) different dust and chips may be obtained, concerning their size distribution and physical properties [START_REF] Magoss | Dust Extraction and Handling in the Wood Industry[END_REF].

In the following experiments, we tested several operational conditions such as feed speed and rotational speed. All these factors influence the particle size distribution and the maximum particle diameter. Fig. 4: Experimental estimation of the wood chip velocity magnitude. Knowing the position of the wood chip for each frame, we estimated the instantaneous velocity magnitude versus time along thirty images. Fig. 5: Automated trajectory tracking of a wood chip. We assumed that the selected wood chip moved in the vertical plane. We manually initialized points to track and set the initial video frame. We estimated the motion of the wood chip by using Kanade-Lucas-Tomasi (KLT), feature tracking algorithm. Since the wood chip changed its shape along its trajectory, we used this algorithm for short-term tracking (ten frames). Fig. 6: Trajectory tracking of a wood chip offline. We used the images acquired at high frequency (5000fps) and manually tracked the wood chip during thirty frames.

A. Motion estimation of wood chips by using offline image processing

In the following experiments, we acquired video frames at 5000fps to track the wood chip in the plan of the camera. From Fig. 4, one can see the instantaneous velocity versus time along thirty frames. The magnitude of the velocity is about 5m/s.

In Fig. 5, we automated the trajectory tracking of a wood chip, assuming that the selected wood chip moved in the vertical plane. Firstly, we manually initialized points to track and set the initial video frame. Secondly, we estimated the motion of the wood chip by using Kanade-Lucas-Tomasi (KLT), feature tracking algorithm. In practice, the shape of a wooden ship is irregular, so the experimental determination of the velocity is not a Fig. 7: Estimated velocity vector of a single thrown wood chip simple task. Therefore, we used this algorithm for short-term tracking (ten frames). Figure 6 shows the trajectory tracking of a wood chip. The tracking is processed manually frame by frame. One can remark the straight path of the wood chip.

Figure 7 plots the estimated velocity vector of one wood chip along its trajectory. To collect most of the chips thrown by the tool, it seems to be better to place the collecting tube such that the flying chips directly get into the tube.

B. Construction of a centralized suction system for proximal aspiration and tests of two aspiration modes (vertical tilted)

The placing and construction of suction hoods depend on the tool and operation design. For the woodworking machines, the suction hoods usually are not placed in the Fig. 8: Tests of the wood dust collection near the toolmaterial interaction. We constructed two mechanical devices connected to the gantry robot to test the efficiency of the wood dust collector while tilted. A) the device is vertically oriented, B) the device is tilted on the side of the electrospindle. proper position to catch more wood particles. In this paper, we constructed a centralized suction system to experiment with the benefit of aspiration of wood particles near the toolmaterial interaction. Two mechanical devices were connected to the gantry robot to test the efficiency of the wood dust collector while tilted. Figure 8 presents both configurations of the suction hood. In Table II, we give the aspiration modes and the collected dust weights.

We found out the efficiency of vertical aspiration was 29.6% (configuration shown in Figs 8A and8B). It means that 29.6% of the total amount of wood dust emitted during grooving is collected by vertical aspiration. The efficiency of the titled aspiration revealed a better result with 54.4% (configuration shown in Figs 8C and8D). This preliminary observation showed that extracting the wood particles near the tool-material interaction improves dust capture efficiency.

Summary of experiments:

Experiments have demonstrated that (i) we can assess realistic velocities of wood chips in direction and norm during robotic machining by using a high-speed vision system, and (ii) placing the suction hood near the tool-material contact improves the dust capture efficiency.

VI. CONCLUSION AND PERSPECTIVES

Wood dust is known to be a human carcinogen. The exposure levels to hardwood dust are usually below 3mg/m 3 . However, over 0.5 million workers may be exposed to a dust level exceeding 5mg/m 3 . To improve dust capture efficiency during woodworking grooving, we analyzed the physical properties of chips by observing the motion behavior with a single high-speed camera. Moreover, a proper hood design placed near the tool seems to improve the capture efficiency. In a further study, we aim at designing a robotic hood that will enable dust capture near the tool.

Fig. 2 :

 2 Fig. 2: Experimental setup dedicated to the analysis of the wood dust motion during robotic grooving. A) Picture of the experimental setup. It is composed of a multi-functional five-axis gantry robot. B) We used a lighting system and a highspeed camera to capture the motion of the ejected wood dust.

Fig. 3 :

 3 Fig. 3: Modeling the wood piece by Cadwork 70mm in width, 20mm in height, 150mm in length

  1 engine to generate ISO code and operate the robot. The

	Machining parameters	Value	Unity
	Rotational speed	18000	rpm
	Feed speed	5	m/min
	Feed width	24	mm
	Feed depth	10	mm
	Feed per turn	0.27	mm
	Feed per tooth	0.093	mm

TABLE I :

 I Values of the woodworking parameters geometry of the wood piece was 70mm in width and 20mm

TABLE II :

 II Values of the woodworking parameters

Acknowledgements: This work was supported by IC Carnot Arts Institute and by University of Lorraine Nov2021-Fev2023.