Optimal path planning in a real-world radioactive environment: A comparative study of A-star and Dijkstra algorithms

Abstract

Navigating complex radioactive environments while minimizing radiation exposure to workers is a critical challenge faced by the nuclear industry. Although various shortest-path algorithms and radiation dose calculation techniques have been employed for optimal path finding, most existing models are based on simulations that do not accurately represent real-world environments. To address this limitation, this study presents a path-planning experiment conducted on a naturally radioactive slag dump, Slag Dump No. 48, also known as Black Mountain, in Zambia. The experiment utilizes the Radiation Detection Backpack System (RDBS) and Geolocation Application for Radiation Monitoring (GARM) in conjunction with the Dijkstra and A-star algorithms to search for an optimal walking path on the slag dump. The distances between neighboring nodes and heuristic values, derived from gamma dose rates, are experimentally obtained from the GARM software. This research contributes to the field by: (1) performing a real-world path planning experiment on a radioactive slag dump, (2) applying RDBS for measuring gamma radiation from a naturally radioactive slag, (3) investigating the combined use of RDBS, GARM, Dijkstra, and A-star algorithms for optimal pathfinding, (4) generating heuristic values and node distances experimentally for path planning in an actual radioactive environment, and (5) comparing the performance of state-of-the-art minimum dose walking path algorithms on dose rate-based and node distance-based weighted graphs. The results of this study and the proposed future work provide valuable insights for enhancing radiation protection and optimizing path planning in radioactive environments.