Distributed Particle Swarm Optimization for Multi-Robot System in Search and Rescue Operations

摘要

In this paper, we tackle the problem of finding victims in a search and rescue environment, taking into account that the terrain in a disaster is complex and dangerous for rescuers to traverse. Furthermore, time is crucial when saving lives from a disaster considering all of these challenges, a solution is proposed by using a cooperative robotics team, which speeds up the process of searching for survivors and avoids risking additional lives. This article focuses on the navigation of a swarm of robots that can avoid collisions with obstacles that can be either static or dynamic, and locate victims. The method we employed to navigate in the environment is based on a DPSO Distributed Particle Swarm Optimization where each particle swarm represents a single robot. We show the interaction between swarms, and we make use of artificial potential functions for collision avoidance and for attraction to victims. We perform several simulation experiments to test the navigation algorithm, avoiding obstacles, and finding victims. These experiments are carried out in different environments, varying the number of victims and also the size and number of obstacles. The results show how the algorithm allows the group to avoid obstacles and find possible victims, all experiments are implemented using a combination of Python with V-Rep.