Adaptive A* graph routing with smoothed obstacle clearance: a rescue simulation-based system

Disasters, such as earthquakes, fires, and hurricanes, inflict significant losses on human lives and economies. These unfortunate events have driven research into best-practice rescue methods via the RoboCupRescue competition and simulation system. One crucial aspect of rescue management is to ensure police forces clear ingresses and egresses to allow evacuations and quick, free movement by other rescue personnel. To this end, existing rescue methods mainly rely on the A* path-planning method coupled with a nearest-neighbour clearance strategy. However, the A* path-planning method generally searches paths for blocked roads but may not consider the prevailing disaster conditions. The nearest-neighbour clearance strategy often results in curved clearance that takes longer than the optimal time. Hence, to improve the efficiency of police force rescue, we propose a novel framework called adaptive A* graph routing with smoothed obstacle clearance. The framework incorporates an adaptive A* graph routing approach that will select routes based on the current disaster conditions. Additionally, it uses a smoothed obstacle clearance strategy such that police forces will clear blocked roads in a particular “smoothed” direction. This framework optimises route selection and obstacle clearance for police forces, improving rescue efficiency. Experimental results demonstrate that our proposed framework achieves superior rescue outcomes compared to the current baselines.