Abstract
Autonomous parking is a valuable feature in many mobile robot applications. As compared to self-driving automobiles, auto-parking is more challenging for a small scale robot equipped with a front camera only, due to the camera view limited by the height of robot and the narrow Field of View (FOV) provided by the inexpensive camera. In this research, auto-parking of such a small scale mobile robot is accomplished in a four-step process: identification of available parking space using transfer learning based on the AlexNet; image processing for the detection of parking space boundary lines; kinematics-based target prediction when the parking space disappears from the camera view partially or completely; and motion control on the robot navigating towards the center of the parking space. Results show that a 95% accuracy has been achieved on identification of available parking spaces. The detection of boundary lines and prediction of target have also been successfully implemented in MATLAB. The testing of motion control and image capture for deep learning is performed on a self-built small-scale mobile robot.
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Li, M., Ni, L.G. (2022). Small Scale Mobile Robot Auto-parking Using Deep Learning, Image Processing, and Kinematics-Based Target Prediction. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-030-82199-9_19
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DOI: https://doi.org/10.1007/978-3-030-82199-9_19
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