Mechanical arm autonomous grabbing method based on deep reinforcement learning and dynamic movement primitives

Abstract

The invention discloses a mechanical arm autonomous grabbing method based on deep reinforcement learning and dynamic movement primitives. The mechanical arm autonomous grabbing method includes the following steps that firstly, a camera image assembly is installed, it is ensured that the recognition area is not shielded, grabbing target area images are preprocessed and sent to a deep reinforcementlearning intelligent agent as state information; secondly, a local strategy near-end optimization training model is established on the basis of the state and deep reinforcement leaning principle; thirdly, a new mixed movement primitive model is established by fusing the dynamic movement primitives and imitation learning; and fourthly, a mechanical arm is trained to autonomously grab objects on thebasis of the models. By means of the mechanical arm autonomous grabbing method, the problem that the mechanical arm joint movement based on traditional deep reinforcement learning is unsmooth can beeffectively solved, the learning problem of primitive parameters is converted into the reinforcement learning problem through combination with the dynamic movement primitive algorithm, and by means ofthe training method of deep reinforcement learning, the mechanical arm can complete the autonomous grabbing task.

Description

technical field [0001] The invention relates to the technical field of robotic arms and deep reinforcement learning training systems, in particular to an autonomous grasping method for robotic arms based on deep reinforcement learning and dynamic motion primitives. Background technique [0002] At present, the research of robot technology has changed from the traditional mechanical dynamics to the direction of intelligent control, especially after comprehensively absorbing the research results of control theory, artificial neural network and machine learning, robot technology has gradually become an artificial intelligence One of the cores of the field. As one of the research hotspots in the field of machine learning in recent years, deep reinforcement learning has achieved rich results both in theoretical research and in practical applications. However, a good deep reinforcement learning algorithm is not enough for robots to solve real-life problems. This is because the c...

AI Technical Summary

Problems solved by technology

Since the joint motion of the robot is driven and controlled by the motor, if the motion trajectory (angle trajectory, angular velocity trajectory and angular acceleration trajectory) of the motor has large fluctuations, the driving torque of the motor will also have large fluctuations at this time. even large mutation values, which can easily cause damage to robot joints

Images