Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative
  Path Planning
Apoorva Vashisth Julius Rückin Federico Magistri Cyrill Stachniss Marija Popović
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 quickly
react to newly discovered obstacles and targets of interest. For replanning, we
propose a new reward function that balances between exploring the unknown
environment and exploiting online-collected data about the targets of interest.
Our experiments show that our method enables more efficient target detection
compared to state-of-the-art learning and non-learning baselines. We also show
the applicability of our approach for orchard monitoring using an unmanned
aerial vehicle in a photorealistic simulator.