This paper details the development and analysis of a computational neuroscience model, known as a Synthetic Nervous System, for the control of a simulated worm robot. Using a Synthetic Nervous System controller allows for adaptability of the network with minimal changes to the system. The worm robot kinematics are inspired by earthworm peristalsis which relies on the hydrostatic properties of the worm’s body to produce soft-bodied locomotion. In this paper the hydrostatic worm body is approximated as a chain of two dimensional rhombus shaped segments. Each segment has rigid side lengths, joints at the vertices, and a linear actuator to control the segment geometry. The control network is composed of non-spiking neuron and synapse models. It utilizes central pattern generators, coupled via interneurons and sensory feedback, to coordinate segment contractions and produce a peristaltic waveform that propagates down the body of the robot. A direct perturbation Floquet multiplier analysis was performed to analyze the stability of the peristaltic wave’s limit cycle.
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An Analysis of Peristaltic Locomotion for Maximizing Velocity or Minimizing Cost of Transport of Earthworm-Like Robots
Earthworm-like peristaltic locomotion has been implemented in >50 robots, with many potential applications in otherwise inaccessible terrain. Design guidelines for peristaltic locomotion have come from observations of biology, but robots have empirically explored different structures, actuators, and control waveform shapes than those observed in biological organisms. In this study, we suggest a template analysis based on simplified segments undergoing beam deformations. This analysis enables calculation of the minimum power required by the structure for locomotion and maximum speed of locomotion. Thus, design relationships are shown that apply to peristaltic robots and potentially to earthworms. Specifically, although speed is maximized by moving as many segments as possible, cost of transport (COT) is optimized by moving fewer segments. Furthermore, either soft or relatively stiff segments are possible, but the anisotropy of the stiffnesses is important. Experimentally, we show on our earthworm robot that this method predicts which control waveforms (equivalent to different gaits) correspond to least input power or to maximum velocity. We extend our analysis to 150 segments (similar to that of earthworms) to show that reducing COT is an alternate explanation for why earthworms have so few moving segments. The mathematical relationships developed here between structural properties, actuation power, and waveform shape will enable the design of future robots with more segments and limited onboard power.
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- Award ID(s):
- 1850168
- PAR ID:
- 10252873
- Date Published:
- Journal Name:
- Soft Robotics
- ISSN:
- 2169-5172
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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