An Informative Planning Framework for Target Tracking and Active Mapping in Dynamic Environments with ASVs

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 propose an informative path planning framework to map an arbitrary number of moving targets with initially unknown positions in dynamic environments. A key component of our approach is a spatiotemporal prediction network that predicts target position distributions over time. We propose an adaptive planning objective for target tracking that leverages these predictions. Simulation experiments show that our proposed 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 using an autonomous surface vehicle, showcasing its ability to track targets in real-world monitoring scenarios.