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Path planning method based on Q-learning algorithm

A learning algorithm and path planning technology, applied in two-dimensional position/channel control, vehicle position/route/altitude control, instruments and other directions, can solve the problems of Q-learning algorithm staying in simulation, lack of combination of practical problems, etc. Efficiency improvement, fast speed, fast convergence effect

Active Publication Date: 2018-09-28
JILIN UNIV
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Problems solved by technology

[0010] At the same time, after reviewing a large number of papers, we found that the exploration of Q-learning algorithms mostly stays on simulation, lack of integration with practical problems

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Embodiment Construction

[0030] see Figure 1 to Figure 6 Shown:

[0031] The path planning method based on Q-learning algorithm provided by the present invention, its method is as follows:

[0032] Step 1: Use ordinary cameras to collect images of our actual environment to obtain basic information;

[0033] Step 2: MATLAB reads the image information collected by the camera, and performs binary processing on the image to determine the coordinates of obstacles in the image;

[0034] Step 3: Segment the graphics. In order to simplify the learning process, we use the grid method to build the environment model. We divide the picture in the previous step into 10×10 grids, and judge in the program. If there is an obstacle, the grid is defined as a grid with obstacles, and the robot cannot pass through it; other grids are defined as grids without obstacles, and the robot can pass through;

[0035] The fourth step: use the improved Q-learning algorithm to plan the path, first define the starting point and ...

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Abstract

The invention discloses a path planning method based on a Q-learning algorithm. The method includes the following steps: firstly, obtaining basic information; secondly, determining the coordinate of an obstacle in an image; thirdly, carrying out segmentation on the image; fourthly, planning paths through an improved Q-learning algorithm; fifthly, obtaining an optimal path, and drawing the optimalpath through MATLAB according to a learning result; and sixthly, controlling a robot to walk for verification, and using a computer to control the robot to walk to verify the path on the basis of thelearning result. The beneficial effects are that a simulation experiment is performed in a grid environment, the method is successfully applied to path planning of the mobile robot in a multi-obstacleenvironment, and the result proves feasibility of the algorithm; the improved Q-learning algorithm can converge faster, with the learning frequency being obviously reduced and the efficiency being improved by 20% to the maximum; and the algorithm framework exhibits high universality for solving similar problems.

Description

technical field [0001] The invention relates to a path planning method, in particular to a path planning method based on a Q-learning algorithm. Background technique [0002] At present, an important milestone in reinforcement learning is the Q-learning algorithm. Q-learning is the most representative reinforcement learning method similar to the dynamic programming algorithm proposed by Watkins [1] in 1989. It provides intelligent systems A learning capability that uses experienced action sequences to select optimal actions in a Markov environment, and does not require a model of the environment. The Q-learning algorithm is actually a variation of the Markov decision process. It is currently the most understandable and widely used reinforcement learning method, and it learns in an incremental manner. Since Watkins proposed the Q-learning algorithm and proved its convergence, the algorithm has received widespread attention in the field of artificial intelligence and machine ...

Claims

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Application Information

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IPC IPC(8): G05D1/02
CPCG05D1/0221G05D1/0223G05D1/0246G05D1/0276
Inventor 千承辉马天录刘凯张宇轩
Owner JILIN UNIV
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