Hybrid Boustrophedon and Direction-Biased Region Transitions for Mobile Robot Coverage Path Planning: A Region-Based Multi-Cost Framework

Achieving efficient Coverage Path Planning (CPP) in indoor and semi-structured settings necessitates both organized area segmentation and dependable transitions between coverage zones. This research introduces an improved region-guided CPP framework that incorporates rectangular region expansion, Boustrophedon-based coverage within regions, and an obstacle-aware planner for transitioning between regions. In contrast to conventional methods that depend solely on A*-based routing, the suggested transition module utilizes a multi-weighted cost model that integrates Euclidean distance, obstacle density, and heading changes to create smoother, more context-sensitive links between regions. The approach is assessed on five representative grid maps inspired by the layouts of building corridors and greenhouse-like strip structures. Performance indicators—including intra-region coverage distance, inter-region transition cost, overall path distance, coverage ratio, and computation duration—illustrate the method’s efficiency. Experimental findings indicate consistent coverage rates ranging from 96% to 99%, with total computation times between 312 and 844 ms. When compared to traditional global Boustrophedon and spiral scanning methods, the proposed system attains noticeably shorter transition paths and enhanced navigation efficiency, particularly in narrow corridors and cluttered environments. In summary, the framework provides a modular, computationally efficient, and obstacle-aware solution that is well-suited for autonomous mobile robot coverage path planning tasks.