比较三种路径规划方法，提升水下机器人探测效率。
Comparison of Innovative Strategies for the Coverage Problem: Path Planning, Search Optimization, and Applications in Underwater Robotics
- 采用TSP、MST和最优控制三种路径规划策略。
- 最优控制在时间受限时表现最好，但计算成本高。
- 适合需快速决策或资源受限的水下探测任务。
在水下机器人等应用中，覆盖问题要求自主航行器系统性探索指定区域，同时最小化重复路径并避开障碍物。本文研究了覆盖路径规划策略，以提升水下滑翔机的探测效率，特别是在最大化探测放射性源概率的同时确保安全航行。评估了三种路径规划方法：旅行商问题（TSP）、最小生成树（MST）和最优控制问题（OCP）。在MATLAB中进行仿真，对比处理时间、未覆盖区域、路径长度和遍历时间。结果表明，当遍历时间受限制时，最优控制（OCP）更优，但计算开销显著更高；而基于MST的方法虽更快，但解的质量较低。研究为根据任务优先级选择算法提供了依据，平衡效率与计算可行性。
原文摘要 · Abstract (English)
In many applications, including underwater robotics, the coverage problem requires an autonomous vehicle to systematically explore a defined area while minimizing redundancy and avoiding obstacles. This paper investigates coverage path planning strategies to enhance the efficiency of underwater gliders, particularly in maximizing the probability of detecting a radioactive source while ensuring safe navigation. We evaluate three path-planning approaches: the Traveling Salesman Problem (TSP), Minimum Spanning Tree (MST), and Optimal Control Problem (OCP). Simulations were conducted in MATLAB, comparing processing time, uncovered areas, path length, and traversal time. Results indicate that OCP is preferable when traversal time is constrained, although it incurs significantly higher computational costs. Conversely, MST-based approaches provide faster but less optimal solutions. These findings offer insights into selecting appropriate algorithms based on mission priorities, balancing efficiency and computational feasibility.
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