Obstacle avoidance planning algorithm for indoor navigation robot based on deep learning

被引：0

作者：

Liu C.-H. ^{[1
]}

Wang S.-C. ^{[1
]}

Zheng C. ^{[1
]}

Chen X.-L. ^{[1
]}

Hao C.-L. ^{[1
]}

机构：

[1] College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2023年 / 53卷 / 12期

关键词：

artificial intelligence; deep learning; indoor navigation robot; obstacle avoidance planning; simulation map;

D O I：

10.13229/j.cnki.jdxbgxb.20221013

中图分类号：

学科分类号：

摘要：

To reduce the collision probability between robots and obstacles and improve the efficiency of robots，a deep learning based obstacle avoidance planning algorithm for indoor navigation robots was proposed. Firstly，through the indoor navigation robot navigation system，combined with deep learning，the detection and recognition capabilities of moving and non moving obstacles in the robot's environment were improved，thereby obtaining practical reactive obstacle avoidance navigation information that is more in line with the actual scene. Then，using this information，a simulation map was constructed，and an optimal task execution route was selected within the simulation map，the problem of difficulty in obstacle avoidance planning caused by disorderly and irregular obstacles was solved，and obstacle avoidance planning for indoor navigation robots was achieved. The experimental results show that the proposed method has a more reliable obstacle avoidance path，and the obstacle avoidance planning time does not exceed 1.2 s，effectively improving the obstacle avoidance accuracy and work efficiency of indoor navigation robots. © 2023 Editorial Board of Jilin University. All rights reserved.

引用

页码：3558 / 3564

页数：6

共 10 条

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