Neural Configuration Distance Function for Continuum Robot Control
<|reference_start|>Neural Configuration Distance Function for Continuum Robot Control: This paper presents a novel method for modeling the shape of a continuum robot as a Neural Configuration Euclidean Distance Function (N-CEDF). By learning separate distance fields for each link and combining them through the kinematics chain, the learned N-CEDF provides an accurate and computationally efficient representation of the robot's shape. The key advantage of a distance function representation of a continuum robot is that it enables efficient collision checking for motion planning in dynamic and cluttered environments, even with point-cloud observations. We integrate the N-CEDF into a Model Predictive Path Integral (MPPI) controller to generate safe trajectories. The proposed approach is validated for continuum robots with various links in several simulated environments with static and dynamic obstacles.<|reference_end|>
@article{long2024neural,
    title={Neural Configuration Distance Function for Continuum Robot Control},
    author={Kehan Long and Hardik Parwana and Georgios Fainekos and Bardh Hoxha
  and Hideki Okamoto and Nikolay Atanasov},
    journal={arXiv preprint arXiv:2409.13865},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13865},
    primaryClass={cs.RO cs.SY eess.SY}
}