Abstract
Maritime surveillance missions, such as search and rescue and environmental monitoring, rely on the efficient allocation of sensing assets over vast and geometrically complex areas. Traditional Coverage Path Planning (CPP) approaches depend on decomposition techniques that struggle with irregular coastlines, islands, and exclusion zones, or require computationally expensive re-planning for every instance. We propose a Deep Reinforcement Learning (DRL) framework to solve CPP on hexagonal grid representations of irregular maritime areas. Unlike conventional methods, we formulate the problem as a neural combinatorial optimization task where a Transformer-based pointer policy autoregressively constructs coverage tours. To overcome the instability of value estimation in long-horizon routing problems, we implement a critic-free Group-Relative Policy Optimization (GRPO) scheme. This method estimates advantages through within-instance comparisons of sampled trajectories rather than relying on a value function. Experiments on 1,000 unseen synthetic maritime environments demonstrate that a trained policy achieves a 99.0% Hamiltonian success rate, more than double the best heuristic (46.0%), while producing paths 7% shorter and with 24% fewer heading changes than the closest baseline. All three inference modes (greedy, stochastic sampling, and sampling with 2-opt refinement) operate under 50~ms per instance on a laptop GPU, confirming feasibility for real-time on-board deployment.
Abstract (translated)
海洋监视任务（如搜救和环境监测）依赖于在广阔且几何结构复杂的区域上高效分配感知资源。传统的覆盖路径规划（CPP）方法依赖于分解技术，但这些技术难以处理不规则的海岸线、岛屿和禁飞区，或需要对每个实例进行计算成本高昂的重新规划。我们提出了一种深度强化学习（DRL）框架，用于解决基于不规则海域六边形网格表示的CPP问题。与常规方法不同，我们将问题 formulate 为一个神经组合优化任务，其中基于Transformer的指针策略通过自回归方式构建覆盖路径。为了克服长时域路径规划中价值估计的不稳定性，我们 implement 了一种无评判器的组相对策略优化（GRPO）方案。该方法通过实例内采样轨迹的比较来估计优势，而非依赖价值函数。在1000个未见过的合成海洋环境上的实验表明，训练后的策略实现了99.0%的哈密顿成功率，是最优启发式算法（46.0%）的两倍以上，同时生成的路径比最近基线方法短7%，航向变更减少24%。所有三种推理模式（贪婪模式、随机采样及结合2-opt优化的采样）在笔记本电脑GPU上每实例耗时均低于50毫秒，证实了其在实时机载部署中的可行性。
URL
https://arxiv.org/abs/2603.28385
