Minimizing State Exploration While Searching Graphs with Unknown Obstacles

We address the challenge of finding a shortest path in a graph with unknown obstacles where the exploration cost to detect whether a state is free or blocked is very high (e.g., due to sensor activation for obstacle detection). The main objective is to solve the problem while minimizing the number of explorations. To achieve this, we propose MXA^∗, a novel heuristic search algorithm based on A^∗. The key innovation in MXA^∗ lies in modifying the heuristic calculation to avoid obstacles that have already been revealed. Furthermore, this paper makes a noteworthy contribution by introducing the concept of a dynamic heuristic. In contrast to the conventional static heuristic, a dynamic heuristic leverages information that emerges during the search process and adapts its estimations accordingly. By employing a dynamic heuristic, we suggest enhancements to MXA^∗ based on real-time information obtained from both the open and closed lists. We demonstrate empirically that MXA^∗ finds the shortest path while significantly reducing the number of explored states compared to traditional A^∗. The code is available at https://github.com/bernuly1/MXA-Star.