Swarm Robotics for Search and Rescue Operations in Disaster Zones Using Particle Swarm Optimization (PSO) Algorithms

Abstract

Swarm robotics has taken a specific dare for search and rescue operations planning in disaster affected areas because of the ability to enhance efficiency and increased coverage area with flexibility of operation in disaster area due to number of robots. This study focuses on the use of PSO heuristics for enhancing coordinated navigation of swarm robotic groups in search and rescue scenarios. A swarm intelligence algorithm known as PSO is applied for improving the path planning, target recognition, and resource management in dynamic live disaster situations modeled from bird and fish flocking behavior. The idea of using PSO is that, within the robots in the swarm, efficiency increases as these ability or skill variables change to respond to current conditions, or limit energy usage and enhance the accuracy of identifying victims/hazards. The paper seeks to understand how PSO can be used to adapt the robot paths, compute for real time reassignment of tasks and support the communications within the swarm in a reliable manner. In any disaster area as in a collapsed building/area, the swarm robotics is evaluated based on some simulation models and case studies in power driven by PSO algorithms. The study shows that swarm robotics has an application in changing the way search and rescue missions are conducted to reduce response times, make the environments risking human lives safer, and increase the number of mission successful rescues.