Hybrid approach to implement multi‐robotic navigation system using neural network, fuzzy logic, and bio‐inspired optimization methodologies.

Mobile robots have been increasingly popular in a variety of industries in recent years due to their ability to move in variable situations and perform routine jobs effectively. Path planning, without a dispute, performs a crucial part in multi‐robot navigation, making it one of the very foremost investigated issues in robotics. In recent times, meta‐heuristic strategies have been intensively investigated to tackle path planning issues in the similar way that optimizing issues were handled, or to design the optimal path for such multi‐robotics to travel from the initial point to such goal. The fundamental purpose of portable multi‐robot guidance is to navigate a mobile robot across a crowded area from initial point to target position while maintaining a safe route and creating optimum length for the path. Various strategies for robot navigational path planning were investigated by scientists in this field. This work seeks to discuss bio‐inspired methods that are exploited to optimize hybrid neuro‐fuzzy analysis which is the combination of neural network and fuzzy logic is optimized using the particle swarm optimization technique in real‐time scenarios. Several optimization approaches of bio‐inspired techniques are explained briefly. Its simulation findings, which are displayed for two simulated scenarios reveal that hybridization increases multi‐robot navigation accuracy in terms of navigation duration and length of the path. [ABSTRACT FROM AUTHOR]