Author name cluster

Vincent Bonnet

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

IROS Conference 2023 Conference Paper

Multi-Modal Upper Limbs Human Motion Estimation from a Reduced Set of Affordable Sensors

Mohamed Adjel
Maxime Sabbah
Raphaël Dumas
Nicolas Mansard
Samer Mohammed
Bruno Watier
Vincent Bonnet

This study aims at developing a new affordable motion capture system for human upper limbs' joint kinematics estimation based on a reduced set of visual inertial measurement units coupled with a markerless skeleton tracking algorithm. The markerless skeleton tracking algorithm allows to alleviate the kinematic redundancy that is observed if only a single visual inertial measurement unit is used at the hand level but it introduces undesired outliers. A Sliding Window Inverse Kinematics Algoritm based on a biomechanical model is proposed to filter out outliers. It has the advantage to constrain the evolution of joint kinematics while being able to handle multi- modalities. The proposed system was validated with five healthy volunteers performing a popular rehabilitation pick and place task. Joint angles estimated using our method were compared with the ones obtained using a reference stereophotogrammetric system. The results showed an average root mean square error of 9. 7deg along with an average correlation of 0. 8. These results compare favorably with literature results obtained with more numerous and relatively costly sensors or more elaborated and expensive markerless systems.

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ICRA Conference 2018 Conference Paper

Human-Exoskeleton System Dynamics Identification Using Affordable Sensors

Randa Mallat
Vincent Bonnet
Weiguang Huo
Patrick Karasinski
Yacine Amirat
Mohamad Ali Khalil
Samer Mohammed

This paper presents a practical method to identify body segments inertial parameters of a human-exoskeleton system using affordable and easy-to-use sensors. First, the joints and the base kinematics are estimated based on the use of an extended Kalman filter and QR visual markers. Then, joints kinematics are used in a dynamic identification pipeline together with the ground reaction force and moments collected with an affordable Wii Balance Board. The identification process is done using an augmented regressor matrix to identify at once each segment mass, center of mass 3D position and inertia tensor elements of both human locomotor apparatus and exoskeleton. The proposed method is able to accurately estimate external force and moments, with less than 6 % of normalized RMS difference in average, and is experimentally validated with a subject wearing a full lower limb exoskeleton.

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ICRA Conference 2018 Conference Paper

Inertial Parameters Identification of a Humanoid Robot Hanged to a Fix Force Sensor

Vincent Bonnet
André Crosnier
Gentiane Venture
Maxime Gautier
Philippe Fraisse

Knowledge of the mass and inertial parameters of a humanoid robot is crucial for the development of model-based controller and motion planning in dynamics situation. Parameters are usually provided from Computer Aided Design (CAD) data and thus inaccurate specially if the robot is modified over time. In this paper, a practical method consisting of hanging a humanoid robot to a fix force sensor to perform its dynamic identification is proposed. This allows, contrary to the literature, to generate very exciting and dynamic motions to identify most of the elements of the inertia tensors in a reduced amount of time. This procedure transforms an instable floating base legged humanoid robot to a safe fix base tree structure robot which makes easier to generate optimal exciting motions. Because of a better excitation the overall trajectory lasts for less than a minute. The method was experimentally validated with a HOAP3 humanoid robot and using a 6-axis force sensor. A reduction of 3 times in average of the RMS difference between measured external reaction forces and moments and their estimates from CAD data was obtained with a single minute of optimal exciting motions.

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IROS Conference 2015 Conference Paper

Constrained dynamic parameter estimation using the Extended Kalman Filter

Vladimir Joukov
Vincent Bonnet
Gentiane Venture
Dana Kulic

In this paper we present a real-time method for identification of the dynamic parameters of a manipulator and its load using kinematic measurements and either joint torques or force and moment at the base. The parameters are estimated using the Extended Kalman Filter and constraints are imposed using Sigmoid functions to ensure the parameters remain within their physically feasible ranges, such as links having positive masses and moments of inertia. Identified parameters can be used in model based controllers. The presented approach is validated through simulation and on data collected with the Barret WAM manipulator. Using the estimated parameters instead of ones provided by the manufacturer greatly improves joint torque prediction.

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IROS Conference 2009 Conference Paper

A robotic closed-loop scheme to model human postural coordination

Vincent Bonnet
Philippe Fraisse
Nacim Ramdani
Julien Lagarde
Sofiane Ramdani
Benoît G. Bardy

This paper models recent data in the field of postural coordination showing the existence of self-organized postural states, and transition between them, underlying suprapostural tracking movements. The proposed closed-loop controller captures the complex postural behaviors observed in humans and can be used to implement efficient and simple balance control principles in humanoids.

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