Enhanced Autonomous Mobile Robot Navigation Using a Hybrid BFO/PSO Algorithm for Dynamic Obstacle Avoidance

Over the past three decades, there has been considerable focus on determining path planning for mobile robots, aiming to find safe and efficient routes between starting points and destinations. Exploring various navigational techniques holds promise for broadening the scope of applications for autonomous mobile robots. This research introduces a novel navigation strategy by combining the Particle Swarm Optimization (PSO) method with the Bacteria Foraging Optimization Algorithm (BFO). Inspired by the foraging behavior of bacteria, particularly E. coli, this bio-inspired approach is utilized to optimize routes for mobile robots, harnessing the advantages of PSO. The primary objective is to establish routes that are both feasible and secure. The proposed model involves a robot that mimics bacteria behavior to identify the most optimal route within environments cluttered with obstacles, connecting an initial point to a designated destination. This approach is implemented and evaluated across various scenarios, demonstrating the effectiveness and potential of the proposed method.