SVR sagittal balance of a biped robot controlling the torso and ankle joint angles

This paper describes the control of an autonomous biped robot that combines the use of the torso and the ankle joints movements for its sagittal balance. The innovative characteristic of this controller is the combined use of the ankle and torso joints movements to correct the Zero Moment Point (ZMP). It is used an artificial intelligence technique, the Support Vector Regression, to control the balance of the robot. To obtain a good stable step it is very important to have a good initial legs trajectory design. Having this in mind human-based trajectories were used, leading to smaller control corrections of ankle and torso joints. Different combinations of torso and ankle joints corrections were tested for the balance control on flat horizontal and inclined surfaces and the results presented. In order to evaluate and compare the performance of the balance control methods of a biped robot two performance indexes are proposed.