Path Planning and Obstacle Avoidance Based on Stereo Vision for Autonomous Guided Cart

• Main Authors: Yung-Hsiang Chen, 陳永祥
• Other Authors: Ying-Shing Shiao
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/14089691261411177975

碩士 === 國立彰化師範大學 === 電機工程學系 === 94 === This thesis investigated an autonomous guided cart (AGC) with machine vision. This mechanism has self-navigating ability in indoor environments. This research includes binocular camera control, 3D scene reconstruction, obstacle detection and path planning. The two axis parallel manipulators and the motion control are design for precisely manipulating the binocular cameras with pan and tilt grabbing ability. The disparities in the binocular images are used to render the 3D navigation scenes in which the obstacles are detected and the available movement paths are found. Base on the Hough Transform image processing approach, the left, right and end of a corridor are identified and the movable space and obstacles detected. The variable template method was used to design a pattern identification algorithm for the AGC to recognize the destination pattern. The navigating map in which the correspondence paths of departures, obstacles and destinations are obtained using 3D navigation environment rendered models. The results obtained using the navigation map and the cart kinematics are employed to plan the movement and avoidance paths for the AGC. After obtaining the movement and avoidance paths, the trajectory tracking and vision servo controller uses the pre-defined speed command to control AGC to reach the destination. The vision servo controlled AGC hardware design and software implementation, which includes the cart body structure, motor drivers, motion control and two parallel axis servo motor control manipulators are proposed. The experimental results demonstrate that vision navigation control is feasible.