Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning

Autonomous robots are often employed for data collection due to their efficiency and low labour costs. A key task in robotic data acquisition is planning paths through an initially unknown environment to collect observations given platform-specific resource constraints, such as limited battery life. Adaptive online path planning in 3D environments is challenging due to the large set of valid actions and the presence of unknown occlusions. To address these issues, we propose a novel deep reinforcement learning approach for adaptively …

acquisition autonomous autonomous robots battery collection constraints costs cs.lg cs.ro data data collection dynamic efficiency environment graphs key labour life low path planning platform reinforcement reinforcement learning robotic robots through