用智能规划追踪漂移目标，提升水面机器人监测效率
An Informative Planning Framework for Target Tracking and Active Mapping in Dynamic Environments with ASVs
- 构建时空预测网络，预判漂浮目标未来位置分布
- 新规划目标使追踪准确率显著优于仅优化熵的旧方法
- 适合搜救、污染监测等动态水域任务，实测验证有效
移动机器人平台正被广泛用于环境监测等信息采集任务。在动态环境中高效追踪目标对搜救和污染物清理等应用至关重要。本文研究因风浪和洋流等扰动而漂移的浮游目标主动建图问题，该问题挑战在于需同时预测地图的空间与时间变化。我们提出一个集成框架，结合动态占用网格建图与信息性规划方法，实现自主水面车辆对自由漂移目标的主动追踪与建图。核心是基于时空预测网络，实时预测目标位置的概率分布；并设计新的规划目标，利用这些预测结果优化路径决策。仿真结果显示，相较仅以熵减为优化目标的方法，本方案显著提升追踪性能。最后，通过实地测试验证了该方法在真实监测场景中的有效性。
原文摘要 · Abstract (English)
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.
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