UC Riverside

Robots are utilized in activities such as mine detection, search and rescue, etc. Especially where the environment is dangerous or search time can be optimized, etc. As the cost of robots reduced and application of robotic systems grew, researchers started working on distributed robotic systems. However, creating a robust, flexible and scalable system of collaborating mobile robots that can efficiently perform the search task has been a challenging issue for roboticists. Biologists observed that animal interactions in their societies create a collective behavior that helps them in foraging, group defending and other activities. They show that each animal in its society has a simple role but the collection of these behaviors enables them to accomplish a complex task. Moreover, swarm behavior has interesting properties such as robustness, flexibility and scalability. Based on these studies, researchers have introduced new optimization methods. Roboticists utilize these optimization methods to provide important properties of swarm behavior. PSO is one of the successful optimization methods in this area. Initially, PSO performs well in exploring different search regions, however, in some cases the method doesn't exploit well in promising regions which increases the search time. In this study, mobile robots are deployed to find a target in the search space. Robots interact with each other and move around the search space using PSO algorithm. In addition, the MPSO algorithm is introduced to create an efficient balance between exploration and exploitation of the PSO algorithm. Results show that MPSO reduces search time and minimizes region revisiting.