Dynamic Obstacle Avoidance Algorithm for Autonomous Mobile Robots

A mobile robot's major purpose is to get its destination by traveling over an optimum path defined various parameters such as time, distance, and the safety from any impediments in path. As a result, backbone of autonomous robot planning obstacle avoidance. Several algorithms for avoidance have been presented researchers, each with own set benefits drawbacks. This paper focuses on two parts; first part finds short smooth collision-free navigate static environment based proposed hybrid algorithms. The between Firefly Algorithm (FA) Modify Chaotic Particle Swarm Optimization (MCPSO), namely (HFACPSO), while other Genetic (GA) MCPSO, (HGACPSO). second suggests algorithm planner improving efficiency route-planning moving adjusting velocity or re-planning robot. To demonstrate effectiveness terms shortest length collision-free, well obtaining optimal near-optimal wheel velocities minimum number iterations. (FAMCPSO) provides enhancement equal (0.82%) compared firefly (FA). Moreover, (GAMCPSO) equals (0.67%) genetic (GA). All methods are simulated dynamic using MATLAB 2018b. Index Terms— Dynamic environment, Collision avoidance, Path planning, PSO, FA, GA.