Robot trajectory planning method based on deep learning

Abstract

The invention discloses a robot trajectory planning method based on deep learning, which comprises the following steps: firstly, establishing a kinematics model of a robot, providing a basic planningtrajectory for the robot, moving the robot, acquiring real-time information of the robot, including information such as position and moment, establishing a dynamics model of the robot, and then obtaining an optimal planning trajectory by utilizing Q-learning reinforcement learning; and the modeling and learning are carried out based on actual acquired data, and modeling under an ideal environmentis avoided. The robot trajectory planning method based on deep learning can be applied to industrial robots in various complex environments because the industrial robots have the capabilities of parameter self-learning and self-adjusting. Under the condition that the consistency of the robots is good, the models learned by the robots can be shared with the robot platforms of the same type. The robot trajectory planning method based on deep learning has wide application prospect in industrial production.

Description

technical field [0001] The invention relates to the field of trajectory planning and deep learning of industrial robots, in particular to a trajectory planning method for intelligent robots based on deep learning. Background technique [0002] Since the first and second industrial revolutions, industrial robots have been demonstrating strong social productivity. Domestic industrial robots are expanding from the traditional automobile industry to machinery, light industry, electronics, food and other fields, especially in the continuous trajectory such as spraying, grinding, polishing and so on. Trajectory planning is one of the most important modules in industrial robot systems. There are many studies on robot trajectory planning, mainly based on kinematics models and dynamics models. The trajectory planning algorithm based on the robot kinematics model is widely used in practice because it only considers the kinematics constraints and has the advantages of simple implement...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of continuous trajectory planning of robots, and propose a method of using deep neural network to modify the reference trajectory, fitting the dynamic model parameters of the robot, and using reinforcement learning to find the optimal trajectory

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