Addition of a peristaltic wave improves multi-legged locomotion performance on complex terrains
<|reference_start|>Addition of a peristaltic wave improves multi-legged locomotion performance on complex terrains: Characterized by their elongate bodies and relatively simple legs, multi-legged robots have the potential to locomote through complex terrains for applications such as search-and-rescue and terrain inspection. Prior work has developed effective and reliable locomotion strategies for multi-legged robots by propagating the two waves of lateral body undulation and leg stepping, which we will refer to as the two-wave template. However, these robots have limited capability to climb over obstacles with sizes comparable to their heights. We hypothesize that such limitations stem from the two-wave template that we used to prescribe the multi-legged locomotion. Seeking effective alternative waves for obstacle-climbing, we designed a five-segment robot with static (non-actuated) legs, where each cable-driven joint has a rotational degree-of-freedom (DoF) in the sagittal plane (vertical wave) and a linear DoF (peristaltic wave). We tested robot locomotion performance on a flat terrain and a rugose terrain. While the benefit of peristalsis on flat-ground locomotion is marginal, the inclusion of a peristaltic wave substantially improves the locomotion performance in rugose terrains: it not only enables obstacle-climbing capabilities with obstacles having a similar height as the robot, but it also significantly improves the traversing capabilities of the robot in such terrains. Our results demonstrate an alternative actuation mechanism for multi-legged robots, paving the way towards all-terrain multi-legged robots.<|reference_end|>
@article{iaschi2024addition,
    title={Addition of a peristaltic wave improves multi-legged locomotion
  performance on complex terrains},
    author={Massimiliano Iaschi and Baxi Chong and Tianyu Wang and Jianfeng Lin
  and Juntao He and Daniel Soto and Zhaochen Xu and Daniel I Goldman},
    journal={arXiv preprint arXiv:2410.01046},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01046},
    primaryClass={cs.RO}
}