| 2020 |
Fusion of Multiple Motion Capture Systems for Musculoskeletal Analysis
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An accurate and convenient method of measuring human movements is essential for human motion analysis. In recent days, several types of motion capture system became available. Since each measurement technology has both merits and demerits, their suitable choice depends on each application or varying situations. Therefore, it is important that the motion analysis software should flexibly select and be connected to several measurement systems simultaneously according to target applications. This paper presents a software designed to manage different motion capture systems and to perform the musculoskeletal analysis. It allow us not only to get data from different systems but also give us the capability of synchronizing/merging their data.
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| 2019 |
Musculoskeletal Estimation Using Inertial Measurement Units and Single Video Image
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We address the problem of estimating the physical burden of a human body. This translates to monitor and estimate muscle tension and joint reaction forces of a musculoskeletal model in real-time. The system should minimize the discomfort generating by any sensors that needs to be fixed on the user. Our system combines a 3D pose estimation from vision and IMU sensors. We aim to minimize the number of IMU fixed to the subject while compensating the remaining lack of information with vision.
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| 2019 |
Real-time musculoskeletal visualization of muscle tension and joint reaction forces
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This paper presents a novel software that visualizes the physical burden of human body during movements. Its main objective is to support factory workers by monitoring the risk of physical health problems like low back pains. To achieve the goal, the software utilizes wearable sensors like IMUs to realize the measurement at a work-site. Several physical information like joint angles, joint torques, muscle tensions, joint reaction forces can be obtained by real-time musculoskeletal computation. The musculoskeletal information can be plotted and recorded by the visualization interface which is integrated to an ergonomic assessment software DhaibaWorks.
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| 2018 |
Analysis of a simple model for post-impact dynamics active compliance in humanoids falls with nonlinear optimization
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We analyse a mass-spring-damper model as an active compliance steering controller to adaptively comply with post-impact dynamics in humanoid falls. We use it as a one degree of freedom virtual link that can be attached between a point at impact and a given limb point (e.g. torso or waist of the humanoid). By mapping position and torque limits of the robot joints into corresponding position and force limits in the virtual link, we formulate a nonlinear optimization problem to find its admissible stiffness and damping that prevents violating the constraints before reaching a steady state rest. The nonlinear constraints are analytically derived using symbolic computation and then numerically solved with off-the-shelf nonlinear optimization solver. The virtual model trajectories are then mapped back on the full body of the humanoid robot and illustrated on the HRP-4 robot in simulation.
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| 2017 |
Post-Impact Adaptive Compliance for Humanoid Falls Using Predictive Control of a Reduced Model
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We consider control of a humanoid robot in active compliance just after the impact consecutive to a fall. The goal of this post-impact braking is to absorb undesired linear momentum accumulated during the fall, using a limited supply of time and actuation power. The gist of our method is an optimal distribution of undesired momentum between the robot's hand and foot contact points, followed by the parallel resolution of Linear Model Predictive Control (LMPC) at each contact. This distribution is made possible thanks to \emph{torque-limited friction polytopes}, an extension of friction cones that takes actuation limits into account. Individual LMPC results are finally combined back into a feasible CoM trajectory sent to the robot's whole-body controller. We validate the solution in full-body dynamics simulation of an HRP-4 humanoid falling on a wall. Humanoids 2017.
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| 2017 |
QP-based Adaptive-Gains Compliance Control in Humanoid Falls
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We address the problem of humanoid falling with a decoupled strategy consisting of a pre-impact and a post- impact stage. In the pre-impact stage, geometrical reasoning allows the robot to choose appropriate impact points in the surrounding environment and to adopt a posture to reach them while avoiding impact-singularities and preparing for the post-impact. The surrounding environment can be unstructured and may contain cluttered obstacles. The post-impact stage uses a quadratic program controller that adapts on-line the joint proportional-derivative (PD) gains to make the robot compliant –to absorb impact and post-impact dynamics, which lowers possible damage risks. This is done by a new approach incor- porating the stiffness and damping gains directly as decision variables in the QP along with the usually-considered variables of joint accelerations and contact forces. Constraints of the QP prevent the motors from reaching their torque limits during the fall. Several experiments on the humanoid robot HRP-4 in a full-dynamics simulator are presented and discussed. ICRA 2017.
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| 2015 |
Falls control using posture reshaping and active compliance
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We address the problem of humanoid falls when they are unavoidable. We propose a control strategy that combines two behaviors: i) closed-loop posture reshaping – during the falling phase, which allows best impact absorption from a predefined taxonomy, coupled with ii) an active compliance through instant PD gains reduction, instead of shutting-off the actuators or instead of high-gains control with additional implements as previously proposed by other works. We perform several simulations to assess our strategy and made experimental trials on the HRP-4 humanoid robot. Humanoids 2015.
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| 2014-2017 |
(Thesis) Humanoid fall control by postural reshaping and adaptive compliance
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This thesis deals with the problem of humanoid falling with a decoupled strategy consisting of a pre-impact and a post-impact stages. In the pre-impact stage, geometrical reasoning allows the robot to choose appropriate impact points in the surrounding environment and to adopt a posture to reach them while avoiding impact singularities and preparing for the post-impact. The post-impact stage uses a quadratic program controller (QP) that adapts on-line the joint proportional-derivative (PD) gains to make the robot compliant, i.e. to absorb post-impact dynamics, which lowers possible damage risks. We propose a new approach incorporating the stiffness and damping gains directly as decision variables in the QP along with the usually-considered variables that are the joint accelerations and contact forces. Finally, to overcome the limitations of the local QP approach, we combine the method with a Model Predictive Controller (MPC) allowing for a preview on a time-horizon based on a reduced model. Several experiments on the real humanoid robot HRP-4 or in a full-dynamics simulator are presented and discussed to illustrate and validate each part of the thesis.
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| 2014 - 2013 |
Master 2 in robotics (ENSAM Paris)
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Master in robotics « Advanced Systems and Robotics » in cohabilitation with UPMC, ENSTA and ENS Cachan
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| 2013 - 2008 |
Engineering degree « Design and Mechanical Engineering » (INSA Lyon)
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Mechanical & Design deparment. Integrated preparatory class – section « ASINSA » which is linked with Asian culture.
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| 2012 - 2011 |
Exchange year (神戸大学)
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Done at the mechanical department of Kobe university.
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