Evolution of Biped Locomotion Using Linear Genetic Programming

摘要

Gait generation for bipedal robots is a very complex problem. The basic cycle of a bipedal gait, called a stride, consists of two main phases, namely the single-support phase and the double-support phase, which take place in sequence. During the single-support phase, one foot is in contact with the ground and the other foot is in swing motion, being transferred from back to front position. In the double-support phase, both feet simultaneously touch the ground, and the weight of the robot is shifted from one foot to the other. During the completion of a stride, the stability of the robot changes dynamically, and there is always a risk of tipping over. Thus it is crucial to actively maintain the stability and walking balance of the robot at all times. In the conventional engineering approach, there are two main methods for bipedal gait synthesis: Off-line trajectory generation, and on-line motion planning Both these methods rely on the calculation of reference trajectories, such as e.g. trajectories of joint angles, for the robot to follow. An off-line controller assumes that there exists an adequate dynamic model of the robot and its environment, which can be used to derive a body motion that adheres to a stability criterion, such as e.g.