Path planning for inspection and maintenance robots in nuclear power plants often suffers from limited adaptability, high computational cost, and unstable convergence in obstacle-dense confined environments. To address these issues, this paper proposes an improved Bi-RRT–APF path optimization framework for complex industrial scenarios. The method integrates (1) a hybrid sampling strategy combining random, goal-biased, and potential-field-guided sampling to enhance global exploration and convergence efficiency; (2) a potential-field-guided perturbation and stagnation detection mechanism to improve escape capability from local minima; and (3) a dynamic target switching and constrained segmented connection strategy to improve path feasibility and safety. A digital twin-based simulation platform is further developed to validate the engineering applicability of the proposed approach. Simulation results demonstrate significant quantitative improvements over baseline methods. Compared with conventional RRT and Bi-RRT, the proposed method reduces iteration count by 65.3% and 43.8%, respectively, and decreases computation time by 76.1% and 48.4%, respectively, while increasing the success rate to 95% (from 82% and 93%) and improving path smoothness (reduced from 5.3 and 3.3 to 2.9). Compared with advanced variants (Quad-RRT and KB-RRT*), the method further reduces computation time by 25.2% and 10.3% and iteration count by 29.3% and 8.4%, respectively. These results indicate that the proposed method achieves a balanced improvement in efficiency, robustness, and path quality. This work provides an efficient and reliable solution for autonomous path planning of robots in complex nuclear power plant environments.
Wu et al. (Mon,) studied this question.