Mobile robot platforms are increasingly being used to automate information gathering tasks such as environmental monitoring. Efficient target tracking in dynamic environments is critical for applications such as search and rescue and pollutant cleanups. In this letter, we study active mapping of floating targets that drift due to environmental disturbances such as wind and currents. This is a challenging problem as it involves predicting both spatial and temporal variations in the map due to changing conditions. We introduce an integrated framework combining dynamic occupancy grid mapping and an informative planning approach to actively map and track freely drifting targets with an autonomous surface vehicle. A key component of our adaptive planning approach is a spatiotemporal prediction network that predicts target position distributions over time. We further propose a planning objective for target tracking that leverages these predictions. Simulation experiments show that this planning objective improves target tracking performance compared to existing methods that consider only entropy reduction as the planning objective. Finally, we validate our approach in field tests, showcasing its ability to track targets in real-world monitoring scenarios.

翻译：移动机器人平台正日益广泛地应用于环境监测等自动化信息采集任务。在动态环境中实现高效目标跟踪对于搜救和污染物清理等应用至关重要。本文研究了受风、流等环境扰动而漂移的浮动目标的主动建图问题。由于需要预测环境条件变化导致的地图时空变化，这是一个具有挑战性的问题。我们提出了一种集成动态占据栅格建图与感知规划方法的统一框架，利用自主水面艇主动建图并跟踪自由漂移目标。该自适应规划方法的核心组件是一个时空预测网络，用于预测目标位置随时间变化的分布。我们进一步提出了一种利用这些预测信息的目标跟踪规划目标。仿真实验表明，与仅以熵减作为规划目标的现有方法相比，该规划目标显著提升了目标跟踪性能。最后，我们通过实地测试验证了该方法，展示了其在真实监测场景中跟踪目标的能力。