Dynamic collision avoidance for multiple robotic manipulators based on a non-cooperative multi-agent game

TL;DR

This paper introduces a real-time, non-cooperative game-based motion control algorithm for multiple robotic manipulators that ensures collision avoidance and scalability in shared workspaces.

Contribution

It presents a novel non-linear model predictive control approach formulated as a non-cooperative game, enabling dynamic collision avoidance among multiple robots.

Findings

Algorithm is real-time capable in simulation.

Scales to multiple robots in shared workspace.

Effectively prevents deadlocks and collisions.

Abstract

A flexible operation of multiple robotic manipulators in a shared workspace requires an online trajectory planning with static and dynamic collision avoidance. In this work, we propose a real-time capable motion control algorithm, based on non-linear model predictive control, which accounts for static and dynamic collision avoidance. The proposed algorithm is formulated as a non-cooperative game, where each robot is considered as an agent. Each agent optimizes its own motion and accounts for the predicted movement of surrounding agents. We propose a novel approach to formulate the dynamic collision constraints. Additionally, we account for deadlocks that might occur in a setup of multiple robotic manipulators. We validate our algorithm on a pick and place scenario for four collaborative robots operating in a common workspace in the simulation environment Gazebo. The robots are controlled by the Robot Operating System (ROS). We demonstrate, that our approach is real-time capable and, due to the distributed nature of the approach, easily scales to an arbitrary number of robot manipulators in a shared workspace.