Deep Reinforcement Learning With Dynamic Graphs for Adaptive Informative Path Planning

Autonomousrobots 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....

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 9; no. 9; pp. 7747 - 7754
Main Authors: Vashisth, Apoorva, Ruckin, Julius, Magistri, Federico, Stachniss, Cyrill, Popovic, Marija
Format: Journal Article
Language:English
Published: IEEE 01.09.2024
Subjects:
Agricultural robots
Agriculture
Autonomous aerial vehicles
Forestry
Motion and path planning
Motion planning
Path planning
Reinforcement learning
Robot sensing systems
robotics and automation in agriculture and forestry
Three-dimensional displays
Vehicle dynamics
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary:Autonomousrobots 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.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2024.3421188