期刊:2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE)日期:2020-09-01卷期号:: 642-646被引量:15
标识
DOI:10.1109/cacre50138.2020.9229933
摘要
It is difficult to detect grasping position of irregular objects automatically which always leads to the failure of grasping task in the current arm robot application scenario. This paper proposes an adaptive grasping position detection method that uses the modified YOLO to recognize and detect contour of an image of the object which sampled by a camera. The detection contour results are then fed into a special regression network trained by prepared datasets to detect the grasping position of the object. The experimental results have shown that the grasping position detection method has good robustness and environmental adaptability and can meet the most needs of general applications in practical working condition.