Path planning in radioactive environment of nuclear facilities based on modified A-star algorithm and search neighborhood optimization

An improved A-star algorithm is proposed in this paper for path planning in the radioactive environment of nuclear facilities. The heuristic function is defined as the estimated dose from the current node to the end point. A dynamic weighting scheme is introduced to balance the actual cost and the estimated cost, and it is applied to the actual cost function. This modification improves the efficiency of path search while maintaining a low cumulative dose for staff. The effect of the number of search neighborhoods (3-8) on path planning is then studied. The results show that the cumulative dose of the modified algorithm is comparable to the traditional A-star algorithm, but the average execution point is reduced by 53.06% and the computation time is shortened by 24.23%. Compared with the Probabilistic Roadmap Method (PRM), PRM has the shortest computation time due to its random sampling characteristics, but the cumulative dose and path length are higher than the two A-star algorithms. For models of varying complexity, optimizing the number of search neighborhoods further enhances search efficiency without affecting path planning results. This improvement significantly enhances the application efficiency of the A-star algorithm in the radioactive environment of nuclear facilities, providing a more effective solution for path planning in complex radioactive environments.