A temperature-first grasping method for dense objects with robotic arms based on deep reinforcement learning

Abstract

The invention discloses a temperature-priority grabbing method for dense objects with a robotic arm based on deep reinforcement learning, comprising the following steps: step S1, constructing a working space of the robotic arm, and constructing a real-time state; step S2, preprocessing the state information; step S3 1. Pass the preprocessed information forward through the Q network to obtain the corresponding Q value; step S4, guide the action of the manipulator according to the Q value and the ε‑greedy strategy, and obtain rewards through the reward function; step S5, continuously pass the target Q The network updates the weights to realize the training of the Q network; step S6, records the relevant data in the training process and the final trained model, and obtains the optimal grasping strategy of the manipulator. The invention is aimed at grasping scenes with irregular shapes, dense stacking, and temperature factors that need to be prioritized; the action of the mechanical arm is designed according to the deep reinforcement learning algorithm, the grasping performance of the mechanical arm is improved, and infrared images are introduced to make the mechanical arm have a priority to grasp The ability to extract higher temperature objects is characterized.

Description

technical field [0001] The present invention relates to a temperature-priority grabbing method for dense objects with a manipulator based on deep reinforcement learning. Deep reinforcement learning is applied to the grasping task of a manipulator, and pushing and grasping are put into a joint action within a reinforcement learning framework to promote Promote grasping and set temperature rewards, so that the robotic arm can better grasp dense objects and have the ability to preferentially grasp high-temperature objects. Background technique [0002] At present, the application and function of robotic arms are becoming more and more perfect; with the rapid development of robotic arm technology, robotic arms have been widely used in industrial tasks such as handling, palletizing, cutting, welding, etc., which not only liberate manpower, but also improve industrial production. The efficiency and quality of the robot arm; among them, the grasping task of the robotic arm is the b...

AI Technical Summary

Problems solved by technology

[0004] The characteristics of the above grasping scenarios have brought difficulties to the grasping work of the robotic arm; the actual modeling process of the model-based method is usually complicated, and it is difficult to apply to unstructured scenarios; Grasping has some effect, but it is not effective for densely stacked objects, and it cannot give priority to hazard conditions

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