Real-Time Identification and Predictive Control of Fast Mobile Robots Using Global Vision Sensing

Abstract

This paper presents a predictive controller for intercepting mobile targets. A global vision system is used to identify fast moving objects and uses a color threshold technique to calculate their position and orientation. The inherent systemic noise in the raw sensor data, as well as vision quantization noise, is smoothed using Kalman filtering before being fed to the controller, and it is shown that this leads to superior accuracy of the controller. The predictive controller is based on the state transition-based control (STBC) technique. As a case study, STBC has been applied to a goalkeeper's behavior in robot soccer which includes interception and clearance of ball. Further evaluation of the controller has been done for shooting the ball toward a target position. The system is examined for both stationary and moving objects. It is shown that predictive filtering of rough sensor data is essential to increase the reliability and accuracy of detection, and thus interception, of fast moving objects.