Locomotion Control for Electrically Powered Quadruped Robot Dynarobin

This paper presents the methodology used for finding the optimal set of foot trajectories for a quadruped robot using manual tuning and multiobjective genetic algorithm optimization that provide energy efficient and fast locomotion. Manual trajectory tuning is used to obtain initial set of trajectories for the multiobjective GA optimization. The multiobjective optimization evaluates the energy per distance and average speed criteria on a robot simulation model. Robot locomotion is achieved by open-loop execution of foot trajectories generated in the local leg coordinate system. Two different foot trajectory formulations are formed for the two optimization processes. The manual optimization was carried out on smaller physically interpretative parameter set while the multiobjective GA optimization is carried out on a larger, more flexible parameter set. A multiobjective optimization is introduced to tune the foot trajectory parameters in order to achieve energy optimal and fast robot locomotion. The obtained Pareto frontier showed that the bound gait is optimal for lower speeds while the trot gait enabled the robot to reach its maximum speed. The paper identifies the correlation between the stride frequency and robot speed for each identified gait laying on the Pareto frontier. Finally we discuss the trajectory shape of solutions obtained using multiobjective optimization. Index terms multiobjective optimization, quadruped robot gait, foot trajectory, inverse kinematics, energy and speed criteria