Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (12): 3558-3564.doi: 10.13229/j.cnki.jdxbgxb.20221013

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

Chun-hui LIU(),Si-chang WANG,Ce ZHENG,Xiu-lian CHEN,Chun-lei HAO   

  1. College of Electrical Engineering and Automation，Shandong University of Science and Technology，Qingdao 266590，China
  • Received:2022-08-10 Online:2023-12-01 Published:2024-01-12

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Abstract:

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.

Key words: artificial intelligence, indoor navigation robot, deep learning, simulation map, obstacle avoidance planning

CLC Number: 

  • TP242

Fig.1

Indoor navigation robot physical map and flat map"

Fig.2

Specific process of deep learning to optimize the navigation system to collect environmental information"

Fig.3

Test objects"

Fig.4

Test environment"

Fig.5

Obstacle avoidance results of different methods"

Table 1

Time consumed by the robot from the starting point to the end point under different methods"

方法离散型室内障碍物环境聚集型室内障碍物环境陷阱型室内障碍物环境
本文0.50.81.2
文献［3］0.91.52.0
文献［4］1.11.72.6
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