This paper presents techniques for building system configuration, control architecture, and implementation of a vision-based wheeled mobile robot (WMR). The completed WMR has been built with the dead-reckoning method so as to determine the vehicle's velocity and posture by the numerical differentiation/integration over short travelling. The developed proportional-integral-derivative (PID) controllers show good transient performances; that is, the velocity of right and left wheels can track the commands quickly and correctly. Moreover, the path-tracking control laws have also been executed within the digital signal processor (DSP)-based controller in the WMR. The image-recognized system can obtain motion information at 15 frames/s by using the hybrid intelligent system (HIS) model, which is one of the well-known colour detection methods. The better performance a vision system has, the more successful the control laws design. The WMR obtains its posture from the dead-reckoning device together with the vision system. These subsystems are integrated, and the operators of the whole system are completed. This WMR system can be thought of as a platform for testing various tracking control laws and a signal-filtering method. To solve the problem of position/orientation tracking control of the WMR, two kinematical optimal non-linear predictive control laws are developed to manipulate the vehicle to follow the desired trajectories asymptotically. A Kalman filter scheme is used to reduce the bad effect of the imagine nose; thereby the accuracy of pose estimation can be improved. The experimental system is composed of a wireless RS232 modem, a DSP-based controller for the WMR, and a vision system with a host computer. A computation-effective and high-performance DSP-based controller is constructed for executing the developed sophisticated path-tracking laws. Finally, the simulation and experimental results show the feasibility and effectiveness of the proposed control laws.
2A2-E12 An Environment Recognition System for a Wheel Type Mobile Robot with Rough Terrain Movement : Third Report: Instrument of Self-Localization System using Image Processing
