A Synthetic Nervous System Controls a Simulated Cockroach

Rubeo, Scott; Szczecinski, Nicholas S.; Quinn, Roger D.

doi:10.3390/app8010006

Cited by 14 publications

(8 citation statements)

References 61 publications

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“…Discoveries made in both insects (Cruse 1985) and crustaceans (Cruse and Muller 1986) were synthesized in perhaps the most famous example of insect-inspired coordination rules, the 'Cruse Rules' for interleg coordination during walking (Cruse 1990). These behavioral rules do not explicitly incorporate CPGs, but they have inspired inter-CPG connections for neuromechanical models of walking insects (Rubeo et al 2017). The Cruse Rules describe how the points at which a leg lifts off at the end of a step (i.e.…”

Section: Insect Discoveriesmentioning

confidence: 99%

A perspective on the neuromorphic control of legged locomotion in past, present, and future insect-like robots

Szczecinski

Goldsmith

Nourse

et al. 2023

Neuromorph. Comput. Eng.

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This article is a historical perspective on how the study of the neuromechanics of insects and other arthropods has inspired the construction, and especially the control, of hexapod robots.Many hexapod robots’ control systems share common features, including:1. Direction of motor output of each joint (i.e., to flex or extend) in the leg is gated by an oscillatory or bistable gating mechanism; 2. The phasing between each joint is influenced by proprioceptive feedback from the periphery (e.g., joint angles, leg load) or central connections between joint controllers; and3. Behavior can be directed (e.g., walking along a curved path) via low-dimensional, broadly-acting descending inputs to the network.These distributed control schemes are inspired by, and in some robots, closely mimic the organization of the nervous systems of insects, the natural hexapods, as well as crustaceans. Nearly a century of research has revealed organizational principles such as central pattern generators (CPGs), the role of proprioceptive feedback in control, and command neurons.These concepts have inspired the control systems of hexapod robots in the past, in which these structures were applied to robot controllers with neuromorphic (i.e., distributed) organization, but not neuromorphic computational units (i.e., neurons) or computational hardware (i.e., hardware-accelerated neurons). Presently, several hexapod robots are controlled with neuromorphic computational units with or without neuromorphic organization, almost always without neuromorphic hardware. In the near future, we expect to see hexapod robots whose controllers include neuromorphic organization, computational units, and hardware. Such robots may exhibit the full mobility of their insect counterparts thanks to a “biology-first” approach to controller design.This perspective is not a comprehensive review of the neuroscientific literature but is meant to give a gentle introduction into the principles that underlie models and inspire neuromorphic robot controllers. Deeper reviews of particular topics from biology are suggested throughout.

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Section: Insect Discoveriesmentioning

confidence: 99%

A perspective on the neuromorphic control of legged locomotion in past, present, and future insect-like robots

Szczecinski

Goldsmith

Nourse

et al. 2023

Neuromorph. Comput. Eng.

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“…A portion of Drosophibot's leg controller is shown in figure 2. Its architecture is strongly based on our group's previous neural stepping controllers [55,64,[68][69][70]. The network is hierarchically organized, with lower levels possessing as much autonomy as possible while still generating coordinated body-wide motion.…”

Section: Neural Controller Assembly and Tuningmentioning

confidence: 99%

Neurodynamic modeling of the fruit flyDrosophila melanogaster

2020

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This manuscript describes neuromechanical modeling of the fruit fly Drosophila melanogaster in the form of a hexapod robot, Drosophibot, and an accompanying dynamic simulation. Drosophibot is a testbed for real-time dynamical neural controllers modeled after the anatomy and function of the insect nervous system. As such, Drosophibot has been designed to capture features of the animal's biomechanics in order to better test the neural controllers. These features include: dynamically scaling the robot to match the fruit fly by designing its joint elasticity and movement speed; a biomimetic actuator control scheme that converts neural activity into motion in the same way as observed in insects; biomimetic sensing, including proprioception from all leg joints and strain sensing from all leg segments; and passively compliant tarsi that mimic the animal's passive compliance to the walking substrate. We incorporated these features into a dynamical simulation of Drosophibot, and demonstrate that its actuators and sensors perform in an animal-like way. We used this simulation to test a neural walking controller based on anatomical and behavioral data from insects. Finally, we describe Drosophibot's hardware and show that the animal-like features of the simulation transfer to the physical robot.

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“…A distributed SNS is designed based on pathways known to exist in insects, as well as hypothetical pathways that produced insect-like motion. Each joint's controller is designed to function as a proportional-integral (PI) feedback loop and tuned with numerical optimization [11].…”

Section: Multi-legged Robotsmentioning

confidence: 99%