This paper presents an approach for autonomous docking of a fully actuated autonomous surface vessel using expert demonstration data. We frame the docking problem as an imitation learning task and employ inverse reinforcement learning (IRL) to learn a reward function from expert trajectories. A two-stage neural network architecture is implemented to incorporate both environmental context from sensors and vehicle kinematics into the reward function. The learned reward is then used with a motion planner to generate docking trajectories. Experiments in simulation demonstrate the effectiveness of this approach in producing human-like docking behaviors across different environmental configurations.
Learning Autonomous Docking Operation of Fully Actuated Autonomous Surface Vessel from Expert data
OCEANS 2024 - Halifax ; 1-5
2024-09-23
1033228 byte
Conference paper
Electronic Resource
English
Learning Autonomous Docking Operation of Fully Actuated Autonomous Surface Vessel from Expert data
ArXiv | 2024
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