Path planning method based on DBQ algorithm

Abstract

The invention belongs to the field of robot path planning, and particularly relates to a path planning method based on DBQ algorithm. The path planning method provided by the invention performs improvement by reinforced learning of the action selection mechanism in the Dyna-Q algorithm, and mainly solves the following three path planning problems: 1, solving the problem of low learning efficiencyof the robot in the early stages when learning in the environment; 2, improving the accuracy of robot path planning; and 3, speeding up the convergence of the algorithm.

Description

technical field [0001] The invention belongs to the field of robot path planning, and in particular relates to a path planning method based on a DBQ algorithm. Background technique [0002] In recent years, with the development of modern science and technology, especially computer technology, electronic communication technology, and control technology, the performance of mobile robots has been continuously improved, making them widely used in medical and health care, aerospace, machinery manufacturing, education and entertainment, etc. application. The prerequisite for mobile robots to complete various tasks in various fields is to plan an effective path from the starting position to the target point of the task, so path planning technology emerges as the times require. Path planning technology is the basis of research on related technologies of mobile robots, and it is also an important part of robotics. Most of the traditional path planning methods require a complete inf...

AI Technical Summary

Problems solved by technology

[0006] (1) Although the path planning algorithm of the non-intelligent algorithm is flexible, simple, and highly operable, most of them must predict the environmental information, and the robot's ability to perceive the environment is weak

In the case of complex and changeable environment or lack of environmental information, traditional algorithms have poor applicability;

[0007] (2) Although the intelligent path planning algorithm has a certain perception ability to the environment, the algorithm still needs to be improved in terms of the accuracy of robot path planning and the convergence speed of the algorithm;

However, the probability of robots obtaining rewards in unknown environments with sparse reward values ​​is too small, resulting in low learning efficiency in the early stages of learning in such environments.

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