動的グラフを用いた深層強化学習による適応的な情報探索経路計画
Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning
未知の3D環境で目標を効率的に発見するため、ロボット周辺に動的にグラフを構築して経路を再計画する深層強化学習手法を提案し、果樹園モニタリングのシミュレーションで有効性を示した。
著者: Apoorva Vashisth, Julius Rückin, Federico Magistri, Cyrill Stachniss, Marija Popović
分類: cs.RO, cs.LG
原文アブストラクト
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 replanning robot paths to map targets of interest in unknown 3D environments. A key aspect of our approach is a dynamically constructed graph that restricts planning actions local to the robot, allowing us to react to newly discovered static obstacles and targets of interest. For replanning, we propose a new reward function that balances between exploring the unknown environment and exploiting online-discovered targets of interest. Our experiments show that our method enables more efficient target discovery compared to state-of-the-art learning and non-learning baselines. We also showcase our approach for orchard monitoring using an unmanned aerial vehicle in a photorealistic simulator. We open-source our code and model at: https://github.com/dmar-bonn/ipp-rl-3d.
