Advance Particle Swarm Optimization-Based Navigational Controller For Mobile Robot

While the robot is inmotion, path planning should follow the three aspects: (1) acquire the knowledge from its environmental conditions. (2) determine its position in the environment and (3) decision-making and execution to achieve its highest-order goals. The present research work aims to develop an efficient particle swarm optimizationbased path planner of an autonomous mobile robot. In this approach, a fitness function has been introduced for converting the mobile robot navigation problem into multi objective optimization problem. The fitness of the swarm mainly depends on two parameters: (1) distance between each particle of the swarm and target, (2) distance between each particle of the swarm and the nearest obstacle. From the obtained fitness values of the swarm, the global best position of the particle is selected in each cycle. Thereby, the robot reaches the global best position in sequence. The effectiveness of the developed algorithm in various environments has been verified by simulation modes.