The Design and Control of a Bipedal Robot with Sensory Feedback

Abstract

A stable walking motion requires effective gait balancing and robust posture correction algorithms. However, to develop and implement such intelligent motion algorithms remains a challenging task for researchers. Effective sensory feedback for stable posture control is essential for bipedal locomotion. In order to minimize the modelling errors and disturbances, this paper presents an effective sensory system and an alternative approach in generating a stable Centre-of-Mass (CoM) trajectory by using an observer-based augmented model predictive control technique with sensory feedback. The proposed approach is used to apply an Augmented Model Predictive Control (AMPC) algorithm with an on-line time shift and to look ahead to process future data to optimize a control signal by minimizing the cost function so that the system is able to track the desired Zero Moment Point (ZMP) as closely as possible, and at the same time to limit the motion jerk. The robot's feet are fitted with force sensors to measure the contact force's location. An observer is also implemented into the system.