AquaMILR: Mechanical intelligence simplifies control of undulatory robots in cluttered fluid environments
<|reference_start|>AquaMILR: Mechanical intelligence simplifies control of undulatory robots in cluttered fluid environments: While undulatory swimming of elongate limbless robots has been extensively studied in open hydrodynamic environments, less research has been focused on limbless locomotion in complex, cluttered aquatic environments. Motivated by the concept of mechanical intelligence, where controls for obstacle navigation can be offloaded to passive body mechanics in terrestrial limbless locomotion, we hypothesize that principles of mechanical intelligence can be extended to cluttered hydrodynamic regimes. To test this, we developed an untethered limbless robot capable of undulatory swimming on water surfaces, utilizing a bilateral cable-driven mechanism inspired by organismal muscle actuation morphology to achieve programmable anisotropic body compliance. We demonstrated through robophysical experiments that, similar to terrestrial locomotion, an appropriate level of body compliance can facilitate emergent swim through complex hydrodynamic environments under pure open-loop control. Moreover, we found that swimming performance depends on undulation frequency, with effective locomotion achieved only within a specific frequency range. This contrasts with highly damped terrestrial regimes, where inertial effects can often be neglected. Further, to enhance performance and address the challenges posed by nondeterministic obstacle distributions, we incorporated computational intelligence by developing a real-time body compliance tuning controller based on cable tension feedback. This controller improves the robot's robustness and overall speed in heterogeneous hydrodynamic environments.<|reference_end|>
@article{wang2024aquamilr:,
    title={AquaMILR: Mechanical intelligence simplifies control of undulatory
  robots in cluttered fluid environments},
    author={Tianyu Wang, Nishanth Mankame, Matthew Fernandez, Velin Kojouharov,
  Daniel I. Goldman},
    journal={arXiv preprint arXiv:2407.01733},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01733},
    primaryClass={cs.RO}
}