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

[1]

Hua Ying-jie, Wang Yan, Ji Zhi-cheng, Path planning and implementation of indoor robot based on trackless navigation, Journal of Yangzhou University (Natural Science Edition), 23, 6, pp. 34-38, (2020)

[2]

Wan Jun, Yao Jia-feng, Yu Liang, Et al., Obstacle avoidance algorithm for redundant manipulators based on pseudo-distance method, Journal of Mechanical Engineering, 56, 17, pp. 59-70, (2020)

[3]

Sun Li-xiang, Sun Xiao-xian, Liu Cheng-ju, Et al., Obstacle avoidance algorithm for mobile robot based on deep reinforcement learning in crowd environment, Information and Control, 51, 1, pp. 107-118, (2022)

[4]

Bian Yong-ming, Ji Peng-cheng, Zhou Yi-he, Et al., Obstacle avoidance path planning of mobile robot based on improved DWA, Chinese Journal of Construction Machinery, 19, 1, pp. 44-49, (2021)

[5]

Sun Shun-yuan, Yu Jing-yuan, An indoor location algorithm for heterogeneous devices and environmental changes, Journal of Jilin University (Science Edition), 61, 4, pp. 915-921, (2023)

[6]

Wang Chuang, Tu Xiao-wei, Research on indoor navigation method based on three linear CCDs, Journal of Yanshan University, 45, 4, pp. 312-320, (2021)

[7]

Wu Dong-ming, Wang Li-juan, Research on obstacle avoidance control system of picking robot-based on ARM+DSP and visual sensor, Journal of Agricultural Mechanization Research, 42, 4, pp. 218-222, (2020)

[8]

Zhang Xiang-zhu, Zhang Li-jia, Song Yi-fan, Et al., Obstacle avoidance algorithm for unmanned aerial vehicle vision based on deep learning, Journal of South China University of Technology (Natural Science Edition), 50, 1, pp. 101-108, (2022)

[9]

Jie Xu-han, Xiong Li-ting, Simulation of grating projection error correction of robot vision navigation sensor, Computer Simulation, 38, 9, pp. 381-385, (2021)

[10]

Li Xiao-feng, Ren Jie, Li Dong, Hierarchical matching algorithm of visual image for mobile robots based on deep reinforcement learning, Journal of Jilin University (Science Edition), 61, 1, pp. 127-135, (2023)

← 1 →