Neurobiologically‐based control system for an adaptively walking hexapod

Biological systems such as insects have often been used as a source of inspiration when developing walking robots. Insects' ability to nimbly navigate uneven terrain, and their observed behavioral complexity have been a beacon for engineers who have used behavioral data and hypothesized control systems to develop some remarkably agile robots. The purpose of this paper is to show how it is possible to implement models of relatively recent discoveries of the stick insect's local control system (its thoracic ganglia) for hexapod robot controllers.

Walking control based on a model of the stick insect's thoracic ganglia, and not just observed insect behavior, has now been implemented in a complete hexapod able to walk, perform goal‐seeking behavior, and obstacle surmounting behavior, such as searching and elevator reflexes. Descending modulation of leg controllers is also incorporated via a head module that modifies leg controller parameters to accomplish turning in a role similar to the insect's brain and subesophageal ganglion.

While many of these features have been previously demonstrated in robotic subsystems, such as single‐ and two‐legged test platforms, this is the first time that the neurobiological methods of control have been implemented in a complete, autonomous walking hexapod.

The methods introduced here have minimal computation complexity and can be implemented on small robots with low‐capability microcontrollers. This paper discusses the implementation of the biologically grounded insect control methods and descending modulation of those methods, and demonstrates the performance of the robot for navigating obstacles and performing phototaxis.