Sensor data fusion using fuzzy control for VOR-based vision tracking system

Abstract

This paper presents a vision tracking system to achieve high recognition performance under dynamic circumstances, using a fuzzy logic controller. The main concept of the proposed system is based on the vestibulo-ocular reflex (VOR) and the opto-kinetic reflex (OKR) of the human eye. To realize the VOR concept, MEMS inertial sensors and encoders are used for robot motion detection. This concept turns the camera towards a selected target, counteracting the robot motion. Based on the OKR concept, the targeting errors are periodically compensated, using vision information. The fuzzy logic controller uses sensor data fusion to detect slip or collision occurrences. To calculate a heading angle of the camera accurately, the output of the fuzzy logic controller and the vision information from the camera are combined, using an extended Kalman filter. The proposed vision tracking system is implemented in a mobile robot and evaluated experimentally. The experimental results are obtained as the tracking and the recognition success rate using a mobile robot. The developed system achieved the excellent tracking and recognition performance during slip or collision occurrences under dynamic circumstances.

Vision Based Fuzzy Control Approaches for Unmanned Aerial Vehicles

“…The system presented by Kok et al for the context of Prey Capture, with a moving object as a passive 'prey' and a robot as a 'pursuer' in [12].In [13] it is proposed a fuzzy coordination control method among a group of distributed robots using only vision, keeping constant the relative distance and the angle. In [14] it is presented a vision tracking system to achieve high recognition performance under dynamic circumstances, using a fuzzy logic controller.…”