Abstract
When the medicine-picking robot grasps drugs, its flexibility and accuracy in grasping detection mainly depend on the precision of visual guidance for the robot. The result of grasping detection directly determines whether the grasping task can be successfully completed. This study aims to enable a faster learning speed for the robot, reduce the search space for the grasping pose of the medicine-picking robot, and improve the grasping accuracy of the robot in unstructured environments. For this purpose, a self-learning DDPG grasping algorithm based on detection constraints is proposed and applied in automated pharmacy detection. The algorithm primarily consists of two steps. First, it extracts candidate grasping areas by analyzing the boundaries of the medicine. Second, with the aid of deep reinforcement learning, it inputs images with candidate grasping areas into an autonomous learning network, conducts adaptive noise exploration and perturbation in the search space, detects the optimal grasping point of the medicine from the image in real time, feeds it back to the medicine-fetching robot, adjusts the grasping pose through autonomous learning, and controls the robot to complete the training grasping. Experiments demonstrate that this method achieves a minimum of 15% improvement in grab detection accuracy compared with the four other grab detection methods. Within the confidence interval, it can achieve a grab success rate of 95%, which verifies the feasibility and effectiveness of this method.