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Collision avoidance interaction between human and a hidden robot based on kinect and robot data fusion

Abstract : Human-Robot Interaction (HRI) is a largely ad- dressed subject today. In order to ensure co-existence and space sharing between human and robot, collision avoidance is one of the main strategies for interaction between them without contact. It is thus usual to use a 3D depth camera sensor (Microsof Kinect V2) which may involve issues related to occluded robot in the camera view. While several works overcame this issue by applying infinite depth principle or increasing the number of cameras, in the current work we developed and applied an original new approach that combines data of one 3D depth sensor (Kinect) and proprioceptive robot sensors. This method uses the principle of limited safety contour around the obstacle to dynamically estimate the robot-obstacle distance, and then generate the repulsive force that controls the robot. For validation, our approach is applied in real time to avoid collisions between dynamical obstacles (humans or objects) and the end- effector of a real 7-dof Kuka LBR iiwa collaborative robot. Our method is experimentally compared with existing methods based on infinite depth principle when the robot is hidden by the obstacle with respect to the camera view. Results showed smoother behavior and more stability of the robot using our method. Extensive experiments of our method, using several strategies based on distancing and its combination with dodging were done. Results have shown a reactive and efficient collision avoidance, by ensuring a minimum obstacle-robot distance (of ≈ 240mm), even when the robot is in an occluded zone in the Kinect camera view.
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Submitted on : Thursday, February 11, 2021 - 5:23:42 PM
Last modification on : Friday, February 12, 2021 - 1:50:40 PM
Long-term archiving on: : Wednesday, May 12, 2021 - 7:11:23 PM


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Hugo Nascimento, Martin Mujica, Mourad Benoussaad. Collision avoidance interaction between human and a hidden robot based on kinect and robot data fusion. IEEE Robotics and Automation Letters, IEEE 2021, 6 (1), pp.88-94. ⟨10.1109/LRA.2020.3032104⟩. ⟨hal-03139189⟩



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