A Neuromechanical Controller of a Hexapod Robot for Walking on Sponge, Gravel and Snow Surfaces

Abstract

Physiological studies suggest that the integration of neural circuits and biomechanics (e.g., muscles) is a key for animals to achieve robust and efficient locomotion over challenging surfaces. Inspired by these studies, we present a neurome-chanical controller of a hexapod robot for walking on soft elastic and loose surfaces. It consists of a modular neural network (MNN) and virtual agonist-antagonist mechanisms (VAAM, i.e., a muscle model). The MNN coordinates 18 joints and generates basic locomotion while variable joint compliance for walking on different surfaces is achieved by the VAAM. The changeable compliance of each joint does not depend on physical compliant mechanisms or joint torque sensing. Instead, the compliance is altered by two internal pa-rameters of the VAAM. The performance of the controller is tested on a physical hexapod robot for walking on soft elas-tic (e.g., sponge) and loose (e.g., gravel and snow) surfaces. The experimental results show that the controller enables the hexapod robot to achieve variably compliant leg behaviors, thereby leading to more energy-efficient locomotion on dif-ferent surfaces. In addition, a finding of the experiments com-plies with the finding of physiological experiments on cock-roach locomotion on soft elastic surfaces.