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 ( a GBNN) approach to CCPP, specifically tailored for an omnidirectional self-reconfigurable cleaning robot (OSCR). The a GBNN 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 a GBNN adapts in real-time to spatial and moving obstacles, significantly enhancing coverage efficiency. Experimental and simulation results demonstrate the advantage of the a GBNN approach, in terms of path length, and total time to complete area coverage compared to state-of-the-art methods. Note to Practitioners—Efficient cleaning of large and complex spaces is increasingly essential across industries such as healthcare, hospitality, and manufacturing, where maintaining cleanliness over vast areas or around fixed obstacles is challenging. Traditional cleaning robots are often limited by fixed brush configurations, restricting their adaptability to clean sideways and navigate tight space while cleaning. a GBNN dynamically adjusts the cleaning path based on the OSCR’s configuration, reducing path length and cleaning time. Simulation and experimental results demonstrate that the OSCR, using the a GBNN, significantly outperforms conventional fixed-footprint robots and existing CCPP methods. We compare our work against algorithms, namely, the original works of Glasius bio-inspired neural network (GBNN) without adaptive nature to handle reconfiguration, Depth First Search (DFS), Predator-Prey Coverage Planner (PPCPP), and ε∗ . It is found that a GBNN achieves more efficient area coverage with up to 21.9% fewer steps, 16.3% less distance traveled, and 17.9% less time required. For industry professionals and practitioners, this means that integrating the OSCR into cleaning operations can lead to improvements in efficiency and cost-effectiveness. However, the implementation requires sophisticated control systems to manage the robot’s reconfiguration. Future research will focus on enhancing the robot’s adaptability and control systems to better handle real-world conditions.