An Adaptive Particle Swarm Optimization Algorithm for Distributed Search and Collective Cleanup in Complex Environment

Abstract EN

Distributed coordination is critical for a multirobot system in collective cleanup task under a dynamic environment.

In traditional methods, robots easily drop into premature convergence.

In this paper, we propose a Swarm Intelligence based algorithm to reduce the expectation time for searching targets and removing.

We modify the traditional PSO algorithm with a random factor to tackle premature convergence problem, and it can achieve a significant improvement in multi-robot system.

It performs well even in a obstacle environment.

The proposed method has been implemented on self-developed simulator for searching task.

The simulation results demonstrate the feasibility, robustness, and scalability of our proposed method compared to previous methods.