Locomotion Gait Optimization for a Quadruped Robot

Legged robot gait generation is a challenging task that involves the control of a large number of degrees of freedom (DOF’s) within a mechanical structure that varies during locomotion. A large number of motion parameters have to be considered in order to obtain a stable, natural and efficient locomotion. Legged robot locomotion applies nonlinear dynamical equations of high order with a multidimensional and irregular set of parameters. Therefore, hand-tuning is very hard to achieve and may not ensure the best results. Biological inspired Evolutionary Computation (EC) appears as the natural choice for gait optimization of legged robots. Genetic Algorithms (GAs) is the approach most used. In this study, we focus on the quadruped gait optimization. Usually optimization systems [1] are applied to improve the locomotion performance in biped, quadruped and hexapod robots. Robocup is one of the motivation engines for these works on the AIBO quadruped robot. However, in these works [1, 7], the robot configuration was the required to achieve higher velocities: the robot knees were completely folded and use a trot gait locomotion. In this contribution, we take inspiration from the vertebrate biological motor systems [6], specifically on the neural control systems involved in generating quadruped locomotion. Motor patterns are generated by networks of Central Pattern Generators (CPGs), pools of neurons that produce coordinated patterns of motor activity while being initiated and modulated by simple command signals. Based on previous work [8], we apply (oscillator-based) differential equations to model a limb-CPG, that generates trajectories for hip robot joints. These CPGs are modelled as coupled oscillators and solved using numeric integration. They have been previously applied in drumming [4]. The presented work takes part of a larger project which aims at developing a closed loop control architecture based on dynamical systems for the autonomous generation of robust, adaptive goal-directed quadruped locomotion in unknown, rough terrain. One objective of the project is to generate different types of gaits and be able to achieve transition among these gaits. Therefore, in this work we propose an approach to optimize a specific quadruped gait, a slow crawl gait, which has to respect a large duty factor β . In fact, it is a slow statically stable gait since three legs are kept in ground contact, suitable for postural control. The goal was not to achieve the highest possible velocity in any gait, but rather to achieve the highest velocity for a slow crawl gait, which has to respect a given duty factor and certain phase relationships.