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Nonlinear Model Predictive Control for Human-Robot Handover with Application to the Aerial Case

Abstract : In this article, we consider the problem of delivering an object to a human coworker by means of an aerial robot. To this aim, we present an ergonomics-aware Nonlinear Model Predictive Control (NMPC) designed to autonomously perform the handover. The method is general enough to be applied to any mobile robot with a minimal adaptation of the robot model. Our formulation lets the NMPC steer the robot toward a handover location optimizing the human coworker ergonomics metrics, which includes the predicted joint torques of the human. The motion task is expressed in a frame relative to the human, whose motion model is included in the equations of the NMPC. This allows the controller to reactively adapt to the human movements by predicting her future poses over the horizon. The control framework also accounts for the problem of maintaining visibility on the human coworker, while respecting both the actuation and state limits. A safety barrier is also embedded in the controller to avoid any risk of collision with the human partner. Realistic simulations are used to validate the feasibility of the approach and the source code of the implementation is released open-source.
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Contributor : Gianluca Corsini Connect in order to contact the contributor
Submitted on : Thursday, July 7, 2022 - 4:27:45 PM
Last modification on : Tuesday, October 25, 2022 - 11:58:11 AM
Long-term archiving on: : Saturday, October 8, 2022 - 7:06:07 PM


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  • HAL Id : hal-03716664, version 1


Gianluca Corsini, Martin Jacquet, Hemjyoti Das, Amr Afifi, Daniel Sidobre, et al.. Nonlinear Model Predictive Control for Human-Robot Handover with Application to the Aerial Case. The 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Oct 2022, Kyoto, Japan. ⟨hal-03716664⟩



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