Obstacle Avoidance in Collective Robotic Search Using Particle Swarm Optimization
This document has been relocated to http://scholarsmine.mst.edu/ele_comeng_facwork/1790
There were 15 downloads as of 28 Jun 2016.
Abstract
Particle Swarm Optimization (PSO) has been demonstrated to be a useful technique in target search applications such as Collective Robotic Search (CRS). A group of unmanned mobile robots are able to locate a specified target in a high risk environment with extreme efficiency when driven by an optimized PSO algorithm. This paper presents an algorithm for obstacle avoidance with the PSO approach applied to navigate robots in collective search applications. Obstacles represented by basic geometric shapes to simulate perilous ground terrain are introduced to the search area. Results are presented to show that PSO algorithm based CRS is able to locate targets avoiding hazardous pathways.
