Minimum Dose Path Planning in Complex Radioactive Environments With Sampling-Based Algorithms

The objective of this paper is to provide a minimum dose path navigation method for occupational workers to avoid additional radiation exposure and quantitatively analyze the cost of paths in radioactive environments. A sampling-based algorithm named Bias-based T-RRT* (BT-RRT*) was proposed, which is an extension of nearly the latest sampling-based algorithm T-RRT*. It combines the exploration strength of T-RRT* that favors the exploration of low-cost regions and connects sampling points selectively with the strategy of biased sampling around the suboptimal paths to increase the convergence rate. To improve planning efficiency, a branch-and-bound strategy is also integrated to improve the efficiency of maintaining the node tree. A walking path-planning system was also developed using virtual reality. Simulation results presented in several radioactive environments show that the walking path planning method was effective in providing the minimum dose path navigation for occupational workers to avoid additional radiation exposure and to increase personnel safety.