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Conference Papers Year : 2024

Model predictive control under hard collision avoidance constraints for a robotic arm

Abstract

We design a method to control the motion of a manipulator robot while strictly enforcing collision avoidance in a dynamic obstacle field. We rely on model predictive control while formulating collision avoidance as a hard constraint. We express the constraint as the requirement for a signed distance function to be positive between pairs of strictly convex objects. Among various formulations, we provide a suitable definition for this signed distance and for the analytical derivatives needed by the numerical solver to enforce the constraint. The method is completely implemented on a manipulator "Panda" robot, and the efficient open-source implementation is provided along with the paper. We experimentally demonstrate the efficiency of our approach by performing dynamic tasks in an obstacle field while reacting to non-modeled perturbations.
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Dates and versions

hal-04425002 , version 1 (29-01-2024)
hal-04425002 , version 2 (13-05-2024)

Identifiers

  • HAL Id : hal-04425002 , version 2

Cite

Arthur Haffemayer, Armand Jordana, Médéric Fourmy, Krzysztof Wojciechowski, Guilhem Saurel, et al.. Model predictive control under hard collision avoidance constraints for a robotic arm. Ubiquitous Robots 2024, Korea Robotics Society, Jun 2024, New York (USA), United States. ⟨hal-04425002v2⟩
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