Journal of System Simulation
Abstract
Abstract: In order to integrate visual information into the robot navigation process, improve the robot's recognition rate of various types of obstacles, and reduce the occurrence of dangerous events, a local path planning network based on two-dimensional CNN and LSTM is designed, and a local path planning approach based on deep learning is proposed. The network uses the image from camera and the global path to generate the current steering angle required for obstacle avoidance and navigation. A simulated indoor scene is built for training and validating the network. A path evaluation method that uses the total length and the average curvature change rate of path and the distance between robot and obstacle as metrics is also proposed. Experiments show that the proposed approach has good local path generation capability in both simulated and real scenes.
Recommended Citation
Liu, Zesen; Bi, Sheng; Guo, Chuanhong; Wang, Yankui; and Dong, Min
(2024)
"Deep Learning Based Local Path Planning Method for Moving Robots,"
Journal of System Simulation: Vol. 36:
Iss.
5, Article 14.
DOI: 10.16182/j.issn1004731x.joss.22-1546
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol36/iss5/14
First Page
1199
Last Page
1210
CLC
TP391.9
Recommended Citation
Liu Zesen, Bi Sheng, Guo Chuanhong, et al. Deep Learning Based Local Path Planning Method for Moving Robots[J]. Journal of System Simulation, 2024, 36(5): 1199-1210.
DOI
10.16182/j.issn1004731x.joss.22-1546
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