Vision-Based Human Tracking Control of a Wheeled Inverted Pendulum Robot

Abstract

In this paper, a vision-based adaptive control is designed for a wheeled inverted pendulum (WIP) robot to track a moving human target by integration of multisensor data. A new algorithm is employed in the system to combine an OptiTrack camera and a Kinect camera, such that more robust and efficient performance can be achieved for human target detection and tracking. Robust adaptive control has been developed for the WIP robot to maintain its balance on two wheels and to follow the human target using visual feedback. Leader-follower control, dynamic balance control and visual tracking are efficiently combined together to achieved desired tracking and balancing performance. Extensive experiment studies have been performed to test the effectiveness of the proposed control strategies.