Gait Synthesis in Legged Robot Locomotion Using a CPG-Based Model

Abstract

6.1 Conclusions A state of the art review was exposed for locomotion modes in quadrupeds and hexapods. In the review were identified the most relevant components for each neurophysiologic model; also the advantages and disadvantages of each model were discussed. It must be noticed that some coincidences in the proposed problem, related to the modeling using not only the conventional method but also the neurophysiologic approach were found; in both cases, the model is based on two systems: one modeling the temporal coordination among the legs and the other one modelling the trajectory control for each leg. The proposed idea is to divide the locomotion trajectory generation issue in two problems: the coordination of the phase relationships among the legs and the controlled movement of the joints for each leg, simplifying the design and implementation for the whole locomotion system. One of the models presented was a locomotion model based on Recurrent Neural Networks (CTRNN), synthesized using genetic algorithms. The locomotion system is based on CPG concept, using coupled oscillators and NN. In order to analyze the output waveform of the temporal trajectory of the legs, a fitness function was employed. Such model leads to an explicit control of the leg speeds during the locomotion, and to control also the support factor, to control the phase relationships among the legs and also to the explicit control of the spatial trajectory described by each tip of the legs. It must be pointed out that the parameter synthesis of the CTRNN using GAs does not assure the absolute convergence to a practical solution. The feedforward neural networks were used in two different applications: one, in the determination of the transition profiles during the movement of one leg; the other, for the transformation of temporal references into spatial references. With the use of feedforward neural networks it was possible to get a model for the locomotion trajectories whose main