Research on Three-Dimensional Simulation Technology Based on an Improved RRT Algorithm

As urban power grids grow increasingly complex and underground space resources become increasingly scarce, traditional two-dimensional cable design methods face significant challenges in spatial representation accuracy and design efficiency. This study proposes an automated cable path planning method based on an improved Rapidly exploring Random Tree (RRT) algorithm. This framework first introduces an enhanced RRT algorithm (referred to as ABS-RRT) that integrates adaptive stride, target-biased sampling, and Soft Actor-Critic reinforcement learning. This algorithm automates the planning of serpentine cable laying paths in confined environments such as cable tunnels and manholes. Subsequently, through trajectory simplification and smoothing optimization, it generates final paths that are safe, smooth, and compliant with engineering specifications. Simulation validation on a typical cable tunnel project in a city’s core area demonstrates that compared to the traditional RRT algorithm, this approach reduces path planning time by over 57%, decreases path length by 8.1%, and lowers the number of nodes by 52%. These results validate the algorithm’s broad application potential in complex urban power grid projects.