Workspace Trajectory Generation Method for Humanoid Adaptive Walking With Dynamic Motion Primitives

Abstract

To properly function in real-world environments, a humanoid robot must be able to adapt its walking gait to new situations. In this paper, an adaptive bipedal walking control method that uses sensory feedback to modulate dynamic movement primitive (DMP) parameters is presented. This work addresses the challenge of adaptive locomotion by implementing DMPs in the workspace of a humanoid robot. This workspace formulation allows new movements to be created such that the DMP parameters, including the stride, height of the hip joint, foot clearance and forward velocity, are directly related to the walking pattern. One set of DMPs is applied to generate the foot trajectory, and a second set is used to generate the CoM (centre of mass) trajectory in an online fashion. Sensory feedback information is utilized to modify the generated CoM and foot trajectories to improve the walking quality. Furthermore, a staged evolutionary algorithm (EA) is designed to optimize the parameters of the control system to enhance the walking performance. The presented control strategy is demonstrated through simulations and real experiments that focus on the adaptation of the robot's walking pattern over sloped terrain.