Mobile robot path planning method with combination of depth automatic encoder and Q-learning algorithm

Abstract

The invention provides a mobile robot path planning method with combination of a depth automatic encoder and a Q-learning algorithm. The method comprises a depth automatic encoder part, a BP neural network part and a reinforced learning part. The depth automatic encoder part mainly adopts the depth automatic encoder to process images of an environment in which a robot is positioned so that the characteristics of the image data are acquired, and a foundation is laid for subsequent environment cognition. The BP neural network part is mainly for realizing fitting of reward values and the image characteristic data so that combination of the depth automatic encoder and the reinforced learning can be realized. According to the Q-learning algorithm, knowledge is obtained in an action-evaluation environment via interactive learning with the environment, and an action scheme is improved to be suitable for the environment to achieve the desired purpose. The robot interacts with the environment to realize autonomous learning, and finally a feasible path from a start point to a terminal point can be found. System image processing capacity can be enhanced, and environment cognition can be realized via combination of the depth automatic encoder and the BP neural network.

Description

technical field [0001] The invention relates to a path planning method for a mobile robot combining a deep autoencoder and a Q learning algorithm, and belongs to the field of path planning for robots. Background technique [0002] Path planning is a fundamental problem in the field of mobile robotics. Mobile robot path planning refers to how to find an appropriate motion path from a given starting point to an end point in a working environment with obstacles, so that the robot can bypass all obstacles safely and without collision during the motion process. [0003] With the development of robot technology, robots have begun to be applied to unknown environments. Compared with the research on path planning of mobile robots in known environments, the exploration of unknown environments has brought new challenges. Since the robot does not have prior knowledge of the environment in an unknown environment, the mobile robot will inevitably encounter various obstacles during the p...

AI Technical Summary

Problems solved by technology

All of the above methods require manual image processing

The traditional manual extraction of image features is a very laborious and heuristic (requires professional knowledge) method. Whether or not good features can be selected depends largely on experience and luck, and its adjustment takes a lot of time

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