用于实时 AUV 路径规划的混合人工势场与时空变压器
Hybrid Artificial Potential Fields and Spatio-Temporal Transformers for Real-Time AUV Path Planning
- Echahid Cheikh Larbi Tebessi University（艾哈西德·谢赫·拉尔比·泰贝西大学）
- Chadli Bendjedid University（查德利·本杰迪德大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究 AUV 在复杂水下环境的路径规划，比较 13 种算法，提出混合人工势场与时空变压器的方法。该方法性能平衡，平均路径短、碰撞率低、计算高效，优于其他算法，为实时 AUV 导航提供强大解决方案。
AI中文摘要：
自主水下航行器（AUV）在复杂、非结构化环境中运行，高效安全的路径规划对任务成功和节能至关重要。本文对 13 种路径规划算法进行了全面比较评估，包括经典图搜索方法、基于采样的方法、元启发式算法和基于学习的架构。特别强调了一种将人工势场（APF）与时空（ST）变压器相结合的混合方法。在高分辨率水下地形图的五个导航场景中进行评估，所有算法都实现了 100%任务完成，但在路径最优性、避碰和计算负载方面存在显著权衡。混合 APF + ST 变压器表现出卓越的平衡性能，平均路径长度最短（943.15 单位），碰撞率低（0.031），计算时间高效（0.96 秒），优于独立学习模型和传统方法。经典算法虽保证无碰撞路径，但路径过长和处理时间长，不适用于动态水下作业；元启发式方法引入的轨迹复杂性不适合严格的能量约束。基于这些发现，混合 APF + ST 框架被推荐为实时 AUV 导航的主要方法，为资源受限的水下系统提供了一种将反应式避障与全局路径最优性相协调的强大解决方案。
英文摘要：
Autonomous Underwater Vehicles (AUVs) operate in complex, unstructured environments where efficient and safe path planning is critical for mission success and energy conservation. This paper presents a comprehensive comparative evaluation of thirteen path planning algorithms, ranging from classical graph-search methods (A*, Dijkstra) and sampling-based approaches (RRT*) to metaheuristics (PSO, GA, ACO, BCO) and learning-based architectures. Special emphasis is placed on a proposed hybrid approach combining Artificial Potential Fields (APF) with a Spatio-Temporal (ST) Transformer. Evaluated across five navigation scenarios on high-resolution underwater terrain maps, all algorithms achieved 100% task completion; however, significant trade-offs emerged in path optimality, collision avoidance, and computational load. The Hybrid APF + ST-Transformer demonstrated superior balanced performance, achieving the shortest average path length (943.15 units), a low collision rate (0.031), and efficient computation time (0.96 s), outperforming standalone learning models, which required fallback mechanisms and classical methods that incurred higher latency. While classical algorithms guaranteed collision-free paths, their excessive path lengths and processing times render them less suitable for dynamic underwater operations. Conversely, metaheuristic approaches introduced trajectory complexity unsuitable for strict energy constraints. Based on these findings, the Hybrid APF + ST framework is recommended as a principal approach for real-time AUV navigation, offering a robust solution that harmonizes reactive obstacle avoidance with global path optimality in resource-constrained underwater systems.
