Self-Organized Stick Insect-Like Locomotion under Decentralized Adaptive Neural Control: From Biological Investigation to Robot Simulation

Abstract

Living animals and legged robots share similar challenges for movement control. In particular, the investigation of neural control mechanisms for the self-organized locomotion of insects and hexapod robots can be informative for other fields. The Annam stick insect Medauroidea extradentata is used as a template to develop a biorobotic model to infer walking self-organization with strongly heterogeneous leg lengths. Body dimensions and data on the walking dynamics of the actual stick insect are used for the development of a neural control mechanism, generating self-organized gait patterns that correspond to the real insect observations. The combination of both investigations not only proposes solutions for distributed neural locomotion control but also enables insights into the neural equipment of the biological template. Decentralized neural central pattern generation is utilized with phase modulation based on foot contact feedback to generate adaptive periodic base patterns and a radial basis function premotor network in each leg based on the target trajectories of actual stick insect legs during walking for complex intralimb coordination and self-organized interlimb coordination control. Furthermore, based on both study objects, a robot with heterogeneous leg lengths is constructed to preliminary validate the findings from the simulations and real insect observations.