Research on Full Coverage Path Planning Based on Reinforcement Learning in Nuclear Environment

摘要

In this paper, we study the path-planning problem of emergency fire control robots in the nuclear environment. Given the high risk of the atomic environment, the irregularity of spatial shape, and the complex distribution of obstacles, a robot path planning method is proposed based on the combination of [Formula: see text]-learning and BCD raster map decomposition method. It realizes the automatic elimination control of the nuclear-contaminated environment and reduces the exposure risk of manual intervention operation. First, [Formula: see text]-learning, a reinforcement learning model, is used to establish the optimal path between the start and end points of the operation area. Second, the BCD raster map decomposition method is used to realize the global division of the operation area. Then, an improved partition merging method based on the [Formula: see text]-learning optimal path is proposed to complete the job sub-region merging and cover path planning. Finally, the simulation experiment proves that the technique can quickly and stably achieve the global path coverage of the unique operating environment of the nuclear domain.