This paper details the development and validation of a dynamic 3D compliant worm-like robot model controlled by a Synthetic Nervous System (SNS). The model was built and simulated in the physics engine Mujoco which is able to approximate soft bodied dynamics and generate contact, gravitational, frictional, and internal forces. These capabilities allow the model to realistically simulate the movements and dynamic behavior of a physical soft-bodied worm-robot. For validation, the results of this simulation were compared to data gathered from a physical worm robot and found to closely match key behaviors such as deformation propagation along the compliant structure and actuator efficiency losses in the middle segments. The SNS controller was previously developed for a simple 2D kinematic model and has been successfully implemented on this 3D model with little alteration. It uses coupled oscillators to generate coordinated actuator control signals and induce peristaltic locomotion. This model will be useful for analyzing dynamic effects during peristaltic locomotion like contact forces and slip as well as developing and improving control algorithms that avoid unwanted slip.
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A Synthetic Nervous System with Coupled Oscillators Controls Peristaltic Locomotion
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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- Award ID(s):
- 2015317
- NSF-PAR ID:
- 10424816
- Date Published:
- Journal Name:
- Biomimetic and Biohybrid Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-031-20470-8_25
