Autonomous surface vehicles offer an efficient solution for environmental cleanup as well as search and rescue operations in open waters. Targets in these settings drift continuously, so efficient search must balance exploration of unobserved regions with tracking of known targets. However, most target tracking and pursuit scenarios consider simple guidance behaviours and short-term predictions for decision-making. In this letter, we address the problem of search and capture of multiple drifting targets, such as litter, in dynamic environments, using a hybrid planning framework. A key aspect of our strategy is a spatiotemporal informative planning method based on model predictive path integral (MPPI) control, a sampling-based model predictive control approach. The planner directly generates kinematic-level commands by optimising continuous trajectories over long horizons. A multi-objective cost balances search and tracking objectives while ensuring safe, feasible trajectories. In the interception stage, we switch to a pure pursuit guidance controller for the physical capture of moving targets. Experiments show that our planner outperforms the chosen planning baselines. Finally, we validate our approach in field trials with an ASV.

翻译：自主水面艇为开阔水域的环境清理及搜索救援任务提供了高效解决方案。此类场景中的目标持续漂移，因此高效搜索须平衡未观测区域的探索与已知目标的追踪。然而，现有目标追踪与追捕场景多采用简单制导律及短期预测进行决策。本文提出一种混合规划框架，解决动态环境中多个漂移目标（如漂浮垃圾）的搜索与捕获问题。该策略的核心是基于模型预测路径积分（MPPI）控制的时空信息规划方法——一种基于采样的模型预测控制方法。该规划器通过优化长时域连续轨迹直接生成运动学级指令，并采用多目标代价函数平衡搜索与追踪目标，同时确保轨迹的安全性与可行性。在拦截阶段，我们切换至纯追捕制导控制器以物理捕获运动目标。实验表明，本规划器优于所选基准规划方法。最后，通过自主水面艇现场试验验证了本方法的有效性。