  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.
