Deep reinforcement learning obstacle avoidance navigation method fusing global training

A technology of reinforcement learning and navigation methods, applied in two-dimensional position/course control, vehicle position/route/altitude control, non-electric variable control, etc., can solve the constraints of popularization and application, weak environmental adaptability, and deep reinforcement learning convergence To achieve high environmental friendliness, reduce environmental interference, and improve environmental friendliness

Active Publication Date: 2021-06-01
ZHEJIANG UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

However, problems such as poor convergence of deep reinforcement learning and weak environmental adaptability also restrict the popularization and application of this method.

Method used

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  • Deep reinforcement learning obstacle avoidance navigation method fusing global training
  • Deep reinforcement learning obstacle avoidance navigation method fusing global training
  • Deep reinforcement learning obstacle avoidance navigation method fusing global training

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Embodiment Construction

[0072] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0073] In the training phase of deep reinforcement learning, the embodiment implemented according to the complete method of the content of the present invention is as follows:

[0074] (1) Establish a global map based on known partial environmental information, such as figure 1 As shown in , the white grid represents the passable area of ​​the robot, and the black grid represents the boundary of the environment or dynamic obstacles;

[0075] (2) Through the SLAM module, the initial point of the robot is obtained, and the position and the target point are simultaneously input into the fast exploration random tree algorithm module to obtain the initial path of the robot navigation task, such as figure 2 shown;

[0076] (3) Input the initial path into the deep reinforcement learning module as the global control of robot navigation, and give rewards and ...

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Abstract

The invention discloses a deep reinforcement learning obstacle avoidance navigation method fusing global training. The method comprises the steps that: an initial path is planned according to known information; a robot moves from an initial point to a target point along the initial path; a temporary target is selected, and the robot moves towards the temporary target; an accumulated expected reward and punishment value is set, navigation is conducted continuously for multiple times, and a reward and punishment value is given to each frame in the robot navigation process; an interaction tuple is obtained from a sensor; the interaction tuple is input into a deep learning network for training; and a to-be-navigated interaction tuple is input into the trained deep learning network, an optimal path and an accumulated expected reward and punishment value are output, and the robot moves according to the optimal path. According to the method, the convergence speed of deep reinforcement learning can be effectively improved, the navigation efficiency of the robot is improved in the navigation process, meanwhile, the motion of the robot is environment-friendly, and the influence on the surrounding environment is reduced to the minimum.

Description

technical field [0001] The invention relates to a dynamic environment obstacle avoidance navigation method for a robot, in particular to a deep reinforcement learning obstacle avoidance navigation method integrated with global training. Background technique [0002] For automatic navigation robots working in dynamic environments such as delivery robots and indoor service robots, obstacle avoidance navigation is a very important function. The robot must reach the target point safely and quickly while avoiding various obstacles. The research on obstacle avoidance for static obstacles is relatively mature, and the problem of obstacle avoidance navigation for dynamic obstacles is more complicated, because it needs to predict the unknown surrounding dynamics (such as pedestrians, vehicles or other robots). As the complexity of the surrounding environment increases, the passable area of ​​traditional navigation methods will become smaller and smaller until the robot cannot plan a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/02
CPCG05D1/0221G05D1/0257G05D2201/0217
Inventor 项志宇应充圣叶育文
Owner ZHEJIANG UNIV
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