Visual servo control for a human-following robot

Abstract

This thesis presents work completed on the design of control and vision components for use in a monocular vision-based human-following robot. The use of vision in a controller feedback loop is referred to as vision-based or visual servo control. Typically, visual servo techniques can be categorised into image-based visual servoing and position-based visual servoing. This thesis discusses each of these approaches, and argues that a position-based visual servo control approach is more suited to human following. A position-based visual servo strategy consists of three distinct phases: target recognition, target pose estimation and controller calculations. The thesis discusses approaches to each of these phases in detail, and presents a complete, functioning system combining these approaches for the purposes of human following. Traditional approaches to human following typically involve a controller that causes platforms to navigate directly towards targets, but this work argues that better following performance can be obtained through the use of a controller that incorporates target orientation information. Although a purely direction-based controller, aiming to minimise both orientation and translation errors, suffers from various limitations, this thesis shows that a hybrid, gain-scheduling combination of two traditional controllers offers better target following performance than its components. In the case of human following the inclusion of target orientation information requires that a definition and means of estimating a human's orientation be available. This work presents a human orientation measure and experimental results to show that it is suitable for the purposes of wheeled platform control. Results of human following using the proposed hybrid, gain-scheduling controller incorporating this measure are presented to confirm this.