Open AccessPath Planning Method of Mobile Robot Based on Q-learning
Author(s) -
Qi Jiang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2181/1/012030
Subject(s) - motion planning , robot , computer science , mobile robot , convergence (economics) , shortest path problem , q learning , grid , path (computing) , process (computing) , reinforcement learning , artificial intelligence , human–computer interaction , theoretical computer science , mathematics , graph , geometry , economics , programming language , economic growth , operating system
With the progress of science and technology, mobile robots gradually play an increasingly important role in the industry, military, science and technology and other fields. Aiming at the core problem of path planning in the path planning of mobile robots, this paper studies and designs a path planning method based on a Q-learning algorithm. Q-learning is widely used in robot path planning, as it only needs the interaction between the current state and the environment to make rewards and punishments for robot actions, to make decisions on the next action. Aiming at the problems of low efficiency and slow convergence in the original Q-learning algorithm, this paper improved the algorithm to enable the robot to quickly complete the planning and get the optimal and shortest path. The grid method was used to establish the environment running program to visualize the convergence process and obtain data. Finally, software simulation is used to establish the environment and code the robot to simulate the real environment, which proves the practical value of the algorithm.