Repeated exploration of a water surface to detect objects of interest and their subsequent monitoring is important in search-and-rescue or ocean clean-up operations. Since the location of any detected object is dynamic, we propose to address the combined surface exploration and monitoring of the detected objects by modeling spatio-temporal reward states and coordinating a team of vehicles to collect the rewards. The model characterizes the dynamics of the water surface and enables the planner to predict future system states. The state reward value relevant to the particular water surface cell increases over time and is nullified by being in a sensor range of a vehicle. Thus, the proposed multi-vehicle planning approach is to minimize the collective value of the dynamic model reward states. The purpose is to address vehicles' motion constraints by using model predictive control on receding horizon, thus fully exploiting the utilized vehicles' motion capabilities. Based on the evaluation results, the approach indicates improvement in a solution to the kinematic orienteering problem and the team orienteering problem in the monitoring task compared to the existing solutions. The proposed approach has been experimentally verified, supporting its feasibility in real-world monitoring tasks.

翻译：重复探索水面以检测感兴趣的目标并随后对其进行监测，在搜索救援或海洋清理行动中至关重要。由于任何已检测目标的位置都是动态的，我们提出通过建模时空奖励状态并协调车辆团队收集奖励来解决组合式水面探索与已检测目标监测问题。该模型刻画了水面的动态特性，使规划器能够预测未来系统状态。特定水面单元的奖励状态值随时间增加，并在车辆传感器范围内被归零。因此，所提出的多车规划方法旨在最小化动态模型奖励状态的累积值。其目的是通过使用滚动时域模型预测控制来处理车辆的运动约束，从而充分利用所用车辆的运动能力。基于评估结果，该方法在监测任务中相对于现有解决方案，在运动学定向问题与团队定向问题的求解上表现出改进。所提方法已通过实验验证，支持其在真实世界监测任务中的可行性。