基于改进A^*的陆空两栖机器人路径规划

Path Planning for Ground-Aerial Amphibious Robot Based on an Improved A^* Algorithm

摘要: 针对传统A*算法搜索效率低、路径拐点多以及环境适应性差等问题，本文提出一种改进A*与蜣螂优化算法(DBO)相结合的跨域规划方法。首先采用基于环境障碍分类的智能分层跨域决策方法；同时，在A*算法中引入电池能耗和运行时间等代价，动态权重调整启发函数，提高搜索效率。最后，采用基于DBO的局部路径优化，利用其搜索能力对路径进行平滑处理与优化。仿真实验结果表明，本算法在路径长度、能量消耗和计算效率等方面均表现出显著优势，为陆空两栖机器人的实际应用提供了可靠的技术支撑。

Abstract: To address the issues of low search efficiency, numerous path inflection points, and poor environmental adaptability in traditional A^* algorithms, this paper proposes a cross-domain planning method combining an improved A^* algorithm with the dung beetle optimization (DBO) algorithm. First, an intelligent hierarchical cross-domain decision-making method based on environmental obstacle classification is adopted. Simultaneously, costs such as battery energy consumption and runtime are introduced into the A^* algorithm, dynamically adjusting the heuristic function weights to enhance search efficiency. Finally, DBO-based local path optimization is employed to leverage its search capabilities for path smoothing and refinement. Simulation results demonstrate that this algorithm exhibits significant advantages in path length, energy consumption, and runtime, providing reliable technical support for the practical application of ground-aerial amphibious robots.