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.


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    Title :

    Learning Autonomous Docking Operation of Fully Actuated Autonomous Surface Vessel from Expert data


    Contributors:

    Published in:

    Publication date :

    2024-09-23


    Size :

    1033228 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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