A Dynamic Nuclear Radiation Environment Path Planning Method Based on an Improved Ant Colony Algorithm

Minimum cumulative dose path planning is an important radiation protection measure to reduce the radiation exposure of robots in nuclear emergencies. However, when an emergency or accident occurs, the distribution of radiation doses in the environment changes dynamically, making the cumulative radiation dose of paths planned by traditional methods nonoptimal. This study proposes a Dijkstra-improved ant colony optimization algorithm (DIACO) to address this issue, combined with a segmented search method to achieve path planning in a dynamic radiation environment.

This method transforms the minimal cumulative radiation dose path obtained by the Dijkstra algorithm into an increment of the initial pheromone distribution for the ant colony optimization (ACO) algorithm, improves the heuristic factor of the ACO algorithm, and incorporates the maximum-minimum ant system to enhance the algorithm’s convergence speed.

Experimental results show that the proposed DIACO algorithm reduces the cumulative radiation dose of the obtained path by approximately 21.08%, the travel distance to the target by about 33.87%, and the number of turns by about 85.1% compared to the traditional ACO algorithm.