Path planning for substation inspection robots faces significant challenges in environments characterized by densely distributed equipment and narrow passages, which severely degrade the sampling efficiency and convergence speed of conventional algorithms. To address these limitations, this paper proposes an enhanced Rapidly-exploring Random Tree* (RRT*) framework that explicitly exploits the structured spatial layout of substations. Specifically, the proposed approach overcomes existing bottlenecks by introducing a Dynamic Constrained Sampling Space (DCSS) model to restrict exploration within task-relevant regions and minimize invalid samples; implementing a spatial adaptive adjustment strategy to dynamically guide tree expansion around large-scale equipment and prevent local stagnation; and integrating a multi-level dynamic rewiring mechanism to eliminate redundant intermediate nodes and enhance path structural continuity. Comprehensive simulations conducted in representative layout scenarios demonstrate the superiority of the proposed framework. The results indicate that this method provides a highly efficient and reliable navigation solution for intelligent substation inspection.

Shihan Fang. Layout-Aware Path Planning for Substation Inspection Robots Using Enhanced RRT*. Academic Journal of Engineering and Technology Science (2026), Vol. 9, Issue 2: 95-102. https://doi.org/10.25236/AJETS.2026.090213.

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