Minimum dose walking path planning in a nuclear radiation environment based on a modified A* algorithm

An additional cost function for the case with turning points is introduced to the A* algorithm, which can reduce unnecessary turning points. Therefore, the modified A* algorithm consists of three parts: the actual cost function, the estimated cost function, and the turning point cost function. The three functions are normalized so that they can be compared without dimensionality to obtain the optimal path planning in radiation environments. Based on the Geant4 Monte Carlo program, the radiation field model is established and the dose distribution data is obtained. We assign weights to the functions to smooth out the three different cost functions and avoid scenarios in which one function is much greater than the other two. In a small-scale simulation experiment, the path generated by the modified algorithm is consistent with that calculated by the traditional A* method, but the number of turning points is significantly reduced. The modified algorithm takes less time and the cumulative dose on the path is slightly lower. Modified A*, traditional A* and PRM algorithms are used for path planning and comparison in the complex radiation environment of multiple γ sources. The PRM algorithm takes the least computation time to search the path, but the cumulative exposure dose is random. For the path planning of multi-task operation in a radiation field, the cumulative radiation dose of the path planned by the modified A* algorithm is equivalent to that of the traditional A* algorithm, but the modified A* algorithm significantly reduces the number of turning points, the calculation time is significantly shortened, and the execution efficiency is improved. The modified A* algorithm can be used as a reference for path planning for workers in radiation workplaces.