Towards a Probabilistic Roadmap for Multi-robot Coordination

Zhi Yan (1) , Nicolas Jouandeau (1) , Arab Ali Cherif

1 LIASD - Laboratoire d'Informatique Avancée de Saint-Denis

In this paper, we discuss the problem of multi-robot coordination and propose an approach for coordinated multi-robot motion planning by using a probabilistic roadmap (PRM) based on adaptive cross sampling (ACS). The proposed approach, called ACS-PRM, is a sampling-based method and consists of three steps including C-space sampling, roadmap building and motion planning. In contrast to previous approaches, our approach is designed to plan separate kinematic paths for multiple robots to minimize the problem of congestion and collision in an effective way so as to improve the system efficiency. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate the total planning time can be obviously reduced by our ACS-PRM approach compared with previous approaches.

Mots clés: Multi-robot system; motion planning; multi-robot coordination; sampling-based approach

Domaines: Intelligence artificielle [cs.AI]

Zhi Yan, Nicolas Jouandeau, Arab Ali Cherif. Towards a Probabilistic Roadmap for Multi-robot Coordination. International Conference on Artificial Intelligence, Jul 2012, Las Vegas, United States. ⟨hal-02316444⟩