Qingmei Dang, Penghui Yang, Huaicheng Yan, Kai Rao, and Xing Qian
Mobile robot, path planning, sampling-based, ROPE-RRT
This paper explores the challenge of front-end path planning for mobile robots navigating through intricate maze environments. Introduces ROPE-RRT, a novel path-planning algorithm that effectively identifies optimal paths in both two-dimensional and three-dimensional contexts. For two-dimensional environments characterised by polygonal obstacles, the approach utilises the visibility graph algorithm. Here, the optimal path is defined along the vertices of the obstacles, and the sampling process is restricted to these vertices, markedly decreasing the sampling workload. Extending this methodology to three-dimensional spaces with convex polyhedral obstacles, the RRT algorithm is employed to swiftly generate an initial feasible path. Subsequently, the path is optimised by applying equal forces to both ends and performing force analysis on the polyline vertices, seeking equilibrium among the forces at these points. Simulation results confirm that the proposed algorithm significantly enhances path optimality in front-end path planning scenarios, outperforming existing algorithms.
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