A computationally efficient complete area coverage algorithm for intelligent mobile robot navigation

Complete area coverage navigation (CAC) requires a special type of robot path planning, where the robots should visit every point of the state workspace. CAC is an essential issue for cleaning robots and many other robotic applications. Real-time complete area coverage path planning is desirable for efficient performance in many applications. In this paper, a novel vertical cell-decomposition (VCD) with convex hull (VCD-CH) approach is proposed for real-time CAC navigation of autonomous mobile robots. In this model, a vertical cell-decomposition (VCD) methodology and a spanning-tree based approach with convex hull are effectively integrated to plan a complete area coverage motion for autonomous mobile robot navigation. The computational complexity of this method with minimum trajectory length planned by a cleaning robot in the complete area coverage navigation with rectangle obstacles in the Euclidean space is O(n log n). The performance analysis, computational validation and comparison studies demonstrate that the proposal model is computational efficient, complete and robust.