A new multiple robot path planning algorithm: dynamic distributed particle swarm optimization

Asma Ayari,Sadok Bouamama

doi:10.1186/s40638-017-0062-6

Abstract

Multiple robot systems have become a major study concern in the field of robotic research. Their control becomes unreliable and even infeasible if the number of robots increases. In this paper, a new dynamic distributed particle swarm optimization (D2PSO) algorithm is proposed for trajectory path planning of multiple robots in order to find collision-free optimal path for each robot in the environment. The proposed approach consists in calculating two local optima detectors, LODpBest and LODgBest. Particles which are unable to improve their personal best and global best for predefined number of successive iterations would be replaced with restructured ones. Stagnation and local optima problems would be avoided by adding diversity to the population, without losing the fast convergence characteristic of PSO. Experiments with multiple robots are provided and proved effectiveness of such approach compared with the distributed PSO.

Highlights

The concept of multiple robot systems (MRS) began in the 1990s, in particular in works regrouping mobile robots, gathering objects [1] and robot colonies [2, 3]
This paper proposed a novel approach to determine the optimal trajectory of the path for distributed multiple robots system using dynamic distributed particle swarm optimization (D2PSO), where each robot is considered to be a mobile, autonomous and physically independent agent
The problem of multiple robot motion planning focuses on computation of paths of different robots such that each robot has an optimal path, and so the overall path of all the robots combined is optimal

Summary

Introduction

The concept of multiple robot systems (MRS) began in the 1990s, in particular in works regrouping mobile robots, gathering objects [1] and robot colonies [2, 3]. Multiple robot systems are well known by the synchronization process and having better spatial distribution capability as compared to a single robot. This coordination addresses the problem of how teams of autonomous mobile robots can share the same workspace while avoiding interference with each other, collision with static obstacles and/or while achieving group motion objectives. There are two basic approaches to solve the problem of multiple robot path planning: centralized and distributed.

Discussion

Conclusion