Clarke Transform -- A Fundamental Tool for Continuum Robotics
<|reference_start|>Clarke Transform -- A Fundamental Tool for Continuum Robotics: This article introduces the Clarke transform and Clarke coordinates, which present a solution to the disengagement of an arbitrary number of coupled displacement actuation of continuum and soft robots. The Clarke transform utilizes the generalized Clarke transformation and its inverse to reduce any number of joint values to a two-dimensional space without sacrificing any significant information. This space is the manifold of the joint space and is described by two orthogonal Clarke coordinates. Application to kinematics, sampling, and control are presented. By deriving the solution to the previously unknown forward robot-dependent mapping for an arbitrary number of joints, the forward and inverse kinematics formulations are branchless, closed-form, and singular-free. Sampling is used as a proxy for gauging the performance implications for various methods and frameworks, leading to a branchless, closed-form, and vectorizable sampling method with a 100 percent success rate and the possibility to shape desired distributions. Due to the utilization of the manifold, the fairly simple constraint-informed, two-dimensional, and linear controller always provides feasible control outputs. On top of that, the relations to improved representations in continuum and soft robotics are established, where the Clarke coordinates are their generalizations.   The Clarke transform offers valuable geometric insights and paves the way for developing approaches directly on the two-dimensional manifold within the high-dimensional joint space, ensuring compliance with the constraint. While being an easy-to-construct linear map, the proposed Clarke transform is mathematically consistent, physically meaningful, as well as interpretable and contributes to the unification of frameworks across continuum and soft robots.<|reference_end|>
@article{grassmann2024clarke,
    title={Clarke Transform -- A Fundamental Tool for Continuum Robotics},
    author={Reinhard Grassmann and Anastasiia Senyk and Jessica Burgner-Kahrs},
    journal={arXiv preprint arXiv:2409.16501},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16501},
    primaryClass={cs.RO}
}