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Leg-local neural mechanisms for searching and learning enhance robotic locomotion

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Abstract

Adapting motor output based on environmental forces is critical for successful locomotion in the real world. Arthropods use at least two neural mechanisms to adjust muscle activation while walking based on detected forces. Mechanism 1 uses negative feedback of leg depressor force to ensure that each stance leg supports an appropriate amount of the body’s weight. Mechanism 2 encourages searching for ground contact if the leg supports no body weight. We expand the neural controller for MantisBot, a robot based upon a praying mantis, to include these mechanisms by incorporating leg-local memory and command neurons, as observed in arthropods. We present results from MantisBot transitioning between searching and stepping, mimicking data from animals as reported in the literature.

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Authors and Affiliations

  1. 10900 Euclid Ave, Cleveland, OH, 44106, USA

    Nicholas S. Szczecinski & Roger D. Quinn

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  1. Nicholas S. Szczecinski
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  2. Roger D. Quinn
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Correspondence to Nicholas S. Szczecinski.

Additional information

This article belongs to a Special Issue on Neural Coding.

This work was supported by NASA Space Technology Research Fellowship NNX12AN24H, as well as a GAANN Grant.

A Appendix

A Appendix

In our system, \(\hbox {d}V_s/\hbox {d}F = 20\) mV\(/2^{10} = 19.53 \times 10^{-3}\) mV; \(\hbox {d}F/\hbox {d}\theta = 326\) rad \(^{-1}\) (empirical); and \(\hbox {d}\theta /\hbox {d}V_\theta = 1.972\) rad / 20 mV = \(98.61 \times 10^{-3}\) rad/mV.

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Szczecinski, N.S., Quinn, R.D. Leg-local neural mechanisms for searching and learning enhance robotic locomotion. Biol Cybern 112, 99–112 (2018). https://doi.org/10.1007/s00422-017-0726-x

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