Abstract: To solve the problem of unstable selection of optimal grasping point and inaccurate grasping angle in dynamic grasping environment, a grasp detection method based on angle constraint and gaussian quality map was proposed. Firstly, the grasping angle were divided into several categories according to the angle value, and the angle value range within the category was constrained to solve the pixel-level annotation loss caused by intensive annotation. morphological open operation method was used to filter the debris generated by multiple annotation stacking in the angle map, and the grasping angle map with stronger annotation consistency was obtained. Secondly, gaussian function was used to optimize the grasping quality map to highlight the importance of the center position of the grasping region and improve the stability of the selection of the optimal grasping point. Finally, based on the fully convolutional network, grasping point and grasping direction attention mechanisms are introduced, and an Attentive Generative Grasping Detection Network (AGGDN) is proposed. Experimental results on Jacquard simulation dataset show that the detection accuracy of proposed method achieves 94.4%, and the single detection time is 11ms, which can effectively improve the grasping detecting ability of complex objects, and has good real-time performance. The experimental results of grasping irregular targets with different poses in the real environment show that, the grasping success rate of proposed method can reach 88.8%, and it has strong generalization ability to the new targets that never appear in the training set, and can be applied to the relevant tasks of robot grasping.