Mobile robot navigation method based on imitation learning and deep reinforcement learning

A technology for mobile robots and reinforcement learning, applied in neural learning methods, two-dimensional position/lane control, instruments, etc., can solve problems such as learning efficiency and performance degradation, training performance cannot exceed demonstration experience, etc., to reduce dependence and improve The effect of learning efficiency

Inactive Publication Date: 2021-03-02
NANJING UNIV OF SCI & TECH
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Problems solved by technology

Imitation learning requires a large number of successful demonstrations to complete the learning task, and the performance of training cannot exceed the demonstration experience, and the demonstration experience is not always optimal; reinforcement learning methods require randomness and sparse rewards in the initial exploration process. A large amount of time is spent interacting with the environment, and some over-exploration experience is learned during the exploration process, which will lead to a decline in learning efficiency and performance

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  • Mobile robot navigation method based on imitation learning and deep reinforcement learning
  • Mobile robot navigation method based on imitation learning and deep reinforcement learning
  • Mobile robot navigation method based on imitation learning and deep reinforcement learning

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Embodiment Construction

[0031] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiment of the present invention provides a mobile robot mapless navigation method based on the coupling framework of imitation learning and deep reinforcement learning, such as figure 1 As shown, it mainly includes the following steps:

[0032] Step S100 establishes the mobile robot and the environment model, initializes the mobile robot and the scanning laser rangefinder, and sets parameters;

[0033] Step S101, using the Ubuntu kinetic operating system, the ROS operating platform and its integrated dynamics simulation software Gazebo as the simulation training environment, arrange the training environment as a square area of ​​20×20m, the obstacles are regularly placed cylindrical pillars, and the mobile robot Learning a map-...

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Abstract

The invention provides a mobile robot navigation method based on imitation learning and deep reinforcement learning. The mobile robot navigation method comprises the following steps: step 1, establishing an environment model of a mobile robot; 2, constructing a navigation control framework based on imitation learning and deep reinforcement learning algorithm coupling, and training the mobile robotmodel by using the coupled navigation framework; and 3, realizing a navigation task by utilizing the trained model.

Description

technical field [0001] The invention relates to a mobile robot navigation technology, in particular to a mobile robot navigation method based on imitation learning and deep reinforcement learning. Background technique [0002] With the rapid development of mobile robot technology, more and more mobile robots have entered the fields of people's life, service and production. In robotics applications, navigation plays an important role and lays the foundation for further execution of other tasks. The traditional navigation framework is mainly composed of perception module, map module and planning module, but the amount of engineering in the selection of representative feature values ​​of the environment, the computational complexity in the process of feature calculation and planning, and the large amount of storage resources and transmission bandwidth required for maintaining the map Both further limit the application of traditional navigation methods. [0003] Machine learni...

Claims

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Application Information

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IPC IPC(8): G05D1/02G06N3/04G06N3/08
CPCG05D1/0221G05D1/0231G05D1/0223G06N3/08G06N3/048G06N3/045
Inventor 陈飞王海梅朱倩梅
Owner NANJING UNIV OF SCI & TECH
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