Complete coverage path planning for omnidirectional self-reconfigurable cleaning robot using aGBNN

Complete coverage path planning (CCPP) is essential for autonomous cleaning robots, particularly in complex and variable environments where traditional, fixed-footprint designs may fall short. This paper presents adaptive Glasius bio-inspired neural network (aGBNN) approach to CCPP, specifically tailored for an omnidirectional self-reconfigurable cleaning robot (OSCR). The aGBNN method dynamically generates a complete coverage path by leveraging the ability to change sweeping footprint of the robot (SFR) assisted by reconfiguring brushes design. The sweeping is carried out both longitudinally and laterally, thereby complementing the omnidirectional locomotion with cleaning. Unlike conventional CCPP algorithms that assume a fixed robot footprint, the proposed aGBNN adapts in real-time to spatial and moving obstacles, significantly enhancing coverage efficiency. Experimental and simulation results demonstrate the advantage of the aGBNN approach, in terms of path length, and total time to complete area coverage compared to state-of-the-art methods.