With the increasing complexity of substation inspection tasks, achieving efficient and safe path planning for Unmanned Aerial Vehicles in densely populated and structurally complex three-dimensional (3D) environments remains a critical challenge. To address this problem, this paper proposes an improved path planning algorithm—Random Geometric Graph (RGG)-guided Rapidly-exploring Random Tree (R-RRT)—based on the classical Rapidly-exploring Random Tree (RRT) framework. First, a refined 3D occupancy grid map is constructed from Light Detection and Ranging point cloud data through ground filtering, noise removal, coordinate transformation, and obstacle inflation using spherical structuring elements. During the planning stage, a dynamic goal-biasing strategy is introduced to adaptively adjust the sampling direction, the sampling distribution is optimized using a pre-generated RGG, and collision detection is accelerated via a K-Dimensional Tree structure. After initial trajectory generation, redundant nodes are eliminated via greedy pruning, and a curvature-minimizing gradient-based optimization method is applied to smooth the trajectory. Experimental results conducted in a simulated substation environment demonstrate that, compared with mainstream path planning algorithms, the proposed R-RRT achieves superior performance in terms of path length, planning time, and trajectory smoothness. Comprehensive analysis shows that the proposed method significantly enhances trajectory quality, planning efficiency, and operational safety, validating its applicability and advantages for high-precision 3D path planning in complex substation inspection scenarios.
Yu et al. (Thu,) studied this question.