A building block attitude detection algorithm based on robot self-error-correction

ZeYuan Cai; ZhiQuan Feng; LiRan Zhou; XiaoHui Yang

doi:10.1117/12.2623052

1 December 2021 A building block attitude detection algorithm based on robot self-error-correction

ZeYuan Cai, ZhiQuan Feng, LiRan Zhou, XiaoHui Yang

Proceedings Volume 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering; 1207902 (2021) https://doi.org/10.1117/12.2623052
Event: 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, Xi'an, China

Abstract

We propose a pose detection algorithm for robot arm grabbing square building blocks in this paper. The main innovations of this method are:(a) A new method combining image segmentation and neural network is constructed. (b): A method for self-correction of posture detection results is proposed, and. (c) The image segmentation method is used to realize the function of automatically marking the position and angle of the target object. Compared with traditional methods that only use image segmentation or neural networks for detection, the method in this paper has absorbed their advantages, which has both the robustness of neural network and the accuracy of image segmentation. Using the method in this paper on the Xarm7 manipulator arm has achieved a grabbing success rate of 92%, which proves the effectiveness of the method in this paper.

Citation Download Citation

ZeYuan Cai, ZhiQuan Feng, LiRan Zhou, and XiaoHui Yang "A building block attitude detection algorithm based on robot self-error-correction", Proc. SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 1207902 (1 December 2021); https://doi.org/10.1117/12.2623052

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