Path planning of PRM based on artificial potential field in radiation environments

Abstract

The radiation environment in nuclear facility work sites is complex. To ensure the safety of personnel working in these environments, it is essential to develop an optimal path planning method based on the criterion of personnel radiation dose. In this paper an improved probabilistic roadmap algorithm (APF-PRM) is proposed based on the combination of artificial potential fields (APF) and probabilistic roadmap algorithm (PRM) for path planning in complex static radiation environments. The entire two-dimensional dose field model of the radiation environment is constructed using bilinear interpolation based on simulation data from Geant4. Using this dose field model and the personnel radiation dose calculation model, the new algorithm is integrated with the A* algorithm to perform path planning in the radiation environment. To assess the performance of the APF-PRM algorithm, a comparison with other algorithms is made in four cases. In the first case, the computation speed of APF-PRM algorithm is significantly faster than that of the conventional A* algorithm in the same environment. In case 2, a reduction of 16.25% in cumulative personnel radiation dose is observed when compared with the traditional PRM algorithm, and the results from multiple repetitions of the experiment display higher stability. In case 3 a comparison between the APF-PRM algorithm and the Voronoi diagram is made, revealing a 36.92% reduction in cumulative dose, along with a 16.49% decrease in path length. In case 4, the four algorithms, A*, APF-PRM, PRM, and Voronoi, are comprehensively compared under the same circumstances, and the parameters of the APF-PRM algorithm that meets the requirements of minimum dose and optimal path are found.