Abstract
This paper addresses the problem of cooperative motion of a swarm mobile robotic system with the purpose of searching a target in a complicated environment. The solution is inspired from Particle Swarm Optimization (PSO) and combined with multibody system dynamics which also includes the consideration of robots' physical properties like mass, inertia, force, acceleration, etc. The entire robot swarm is mainly guided by this physical PSO and an independent obstacle avoidance module is active when robots encounter any conflicts during missions. This paper considers an artificial swarm mobile robot system to perform searching tasks and each member of the system may interact with its neighbors or the environment by limited local communication ability. Several groups of simulations are set up for the verification of the strategy and the results show that this method creates the desired behavior well. The simulation experiments also investigate the feature of fault tolerance of this strategy. Finally, a framework for the future application on real robots is briefly discussed.
ACKNOWLEDGMENTS
The authors want to thank the Chinese Scholarship Council (CSC) for supporting Mr. Qirong Tang to study in Germany as a doctoral student. Part of this work is supported by the Cluster of Excellence Simulation Technology (SimTech) in Stuttgart, Germany, which is also greatly acknowledged. All such help and support are highly appreciated.
Notes
Totally successful: 82 runs. Additionally, No robots detach from swarm: 63 runs. One robot detach from swarm: 17 runs. Two robots detach from swarm: 15 runs. Three robots detach from swarm: 5 runs. Successfully find target, and without robot detach from swarm: 50 runs. Successfully find target, but with one robot detach from swarm: 17 runs. Successfully find target, but with two robots detach from swarm: 10 runs. Successfully find target, but with three robots detach from swarm: 5 runs.