Robot adaptive grabbing method based on deep reinforcement learning

Abstract

The invention provides a robot adaptive grabbing method based on deep reinforcement learning. The method comprises the following steps: when distanced a certain distance away from an object to be grabbed, a robot obtaining a picture of a target through a pick-up head in the front, then according to the picture, calculating position information of the target by use of a binocular distance measurement method, and applying the calculated position information to robot navigation; when the target goes into the grabbing scope of a manipulator, taking a picture of the target through the pick-up head in the front again, and by use of a DDPG-based deep reinforcement learning network trained in advance, performing data dimension reduction feature extraction on the picture; and according to a feature extraction result, obtaining a control strategy of the robot, and the robot controlling a movement path and the posture of the manipulator by use of a control strategy so as to realize adaptive grabbing of the target. The grabbing method can realize adaptive grabbing of objects which are in different sizes and shapes and are not fixedly positioned and has quite good market application prospect.

Description

technical field [0001] The invention relates to a method for a robot to grab an object, in particular to a robot adaptive grabbing method based on deep reinforcement learning. Background technique [0002] Autonomous robots are highly intelligent service robots that can learn from the external environment. In order to realize the functions of various basic activities (such as positioning, moving, and grasping), it is necessary for the robot to be equipped with a robotic arm and a robotic gripper and to fuse information from multiple sensors for machine learning (such as deep learning and reinforcement learning), and to communicate with the external environment. Interaction, realize its various functions such as perception, decision-making and action. At present, most grasping robots work in the situation where the size, shape and position of the objects to be grasped are relatively fixed, and the grasping technology is mainly based on sensors such as ultrasonic, infrared an...

AI Technical Summary

Problems solved by technology

[0003] The technical problem to be solved by the present invention is that the existing situation that cannot adapt to the more complicated grasping environment, and the size, shape and position of the grasped objects are not fixed

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