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Target Detection in NAO Robot Golfing

Erwei Su, Long Chen, Yingli Xu, Bin Hu

Year
2021
Citations
2

Abstract

Abstract In robot golfing games, how to identify and track small balls is a critical step for scoring. Considering uneven illumination and intensity, irregular distribution, and blurred borders, an integrated detection method based on the random Hough transform and the Kalman filter is presented to improve the recognition accuracy, i.e., guaranteeing the detection accuracy and the stability in shooting. Experiment on the NAO robot is performed to show the effectiveness of the proposed detecting method. Both theoretical and experimental results suggest that the proposed recognition method can reduce the calculation time of the Hough transform and has a certain degree of robustness against uncertain environments.

Keywords

Hough transformRobustness (evolution)Artificial intelligenceComputer visionKalman filterComputer scienceRobotStability (learning theory)Track (disk drive)Pattern recognition (psychology)

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