Research on path planning of lunar exploration robot based on A* algorithm

Abstract

This paper presents an advanced implementation of the A* algorithm for path planning in lunar exploration, tailored to address the complexities of navigating the Moon's rugged terrain. The algorithm is enhanced through environmental mapping, which transforms detailed lunar topographic maps into grid-based maps suitable for A* pathfinding. The methodology involves high-resolution imagery from NASA's Daily Moon Guide, processed through grayscale conversion and binarization to create navigable maps. The A* algorithm is optimized with a Manhattan distance heuristic, ensuring admissible and consistent cost estimation. The application of this method results in a 25.5% reduction in total path distance and a 33.3% decrease in turning angles compared to traditional approaches like Dijkstra’s algorithm. The improved path planning method significantly enhances the efficiency and safety of lunar rover operations, which is crucial for scientific data collection and mission success. The practical impact of this research is evident in its potential to streamline future lunar missions, justifying the high costs associated with space exploration through more effective scientific outcomes.