Ground Contact Parameter Estimation Guided Gait Planning for Hexapod Robots

Abstract

Environment sensing for legged robots can improve their mobility performance. A foot-ground contact model can be used to evaluate the properties of the robot's contact with the environment. A contact parameter estimator based on artificial neural networks was developed to guide the gait planning of a hexapod robot so that it can achieve higher mobility efficiency. This contact parameter estimator can use acceleration, velocity, and contact force data as inputs and output contact parameters. The estimator avoided using deformations as input which are difficult to measure. Meanwhile, the robot's cost of transport with different contact parameters is tested and recorded. Accordingly, the hexapod robot was guided to choose a better gait shape between cycloid and rectangular. The simulation proved that changes in the gait shape according to the contact parameters can reduce the hexapod's cost of transport.