Effect of Planning Period on MPC-based Navigation for a Biped Robot in a Crowd

Matteo Ciocca 1 Pierre-Brice Wieber 2 Thierry Fraichard 1
1 PERVASIVE - Interaction située avec les objets et environnements intelligents
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, UGA - Université Grenoble Alpes
Abstract : We control a biped robot moving in a crowd with a Model Predictive Control (MPC) scheme that generates stable walking motions, with automatic footstep placement. Most walking strategies propose to re-plan the walking motion to adapt to changing environments only once at every footstep. This is because a footstep is planted on the ground, it usually stays there at a constant position until the next footstep is initiated, what naturally constrains the capacity for the robot to react and adapt its motion in between footsteps. The objective of this paper is to measure if re-planning the walking motion more often than once at every footstep can lead to an improvement in collision avoidance when navigating in a crowd. Our result is that re-planning twice (or more) during each footstep leads to a significant reduction of the number of collisions when walking in a crowd, but depends on the density of the crowd.
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Submitted on : Monday, August 19, 2019 - 11:46:42 AM
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Matteo Ciocca, Pierre-Brice Wieber, Thierry Fraichard. Effect of Planning Period on MPC-based Navigation for a Biped Robot in a Crowd. IROS 2019 - IEEE/RSJ International Conference on Intelligent Robots and Systems, Nov 2019, Macau, China. ⟨hal-02267426⟩

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