Online optimal motion generation with guaranteed safety in shared workspace

Pu Zheng (1, 2) , Pierre-Brice Wieber (3) , Olivier Aycard (1, 2)

1 LIG - Laboratoire d'Informatique de Grenoble
2 Marvin - Artificial Intelligence and Robotics
3 Inria Grenoble - Rhône-Alpes

With new, safer manipulator robots, the probability of serious injury due to collisions with humans remains low (5%), even at speeds as high as 2 m.s -1. Collisions would better be avoided nevertheless, because they disrupt the tasks of both the robot and the human. We propose in this paper to equip robots with exteroceptive sensors and online motion generation so that the robot is able to perceive and react to the motion of the human in order to reduce the occurrence of collisions. It’s impossible to guarantee that no collision will ever take place in a partially unknown dynamic environment such as a shared workspace, but we can guarantee instead that, if a collision takes place, the robot is at rest at the time of collision, so that it doesn’t inject its own kinetic energy in the collision. To do so, we adapt a Model Predictive Control scheme which has been demonstrated previously with two industrial manipulator robots avoiding collisions while sharing their workspace. The proposed control scheme is validated in simulation.

Domaines: Automatique / Robotique

Pu Zheng, Pierre-Brice Wieber, Olivier Aycard. Online optimal motion generation with guaranteed safety in shared workspace. ICRA 2020 - International Conference on Robotics and Automation, May 2020, Paris, France. pp.1-6. ⟨hal-02496057⟩