Achieving Real-Time Path Planning in Unknown Environments Through Deep Neural Networks
Real-time path planning is crucial for intelligent vehicles to achieve autonomous navigation. In this paper, the authors propose a novel deep neural network (DNN) based method for real-time online path planning in unknown cluttered environments. Firstly, an end-to-end DNN architecture named online three-dimensional path planning network (OTDPP-Net) is designed to learn 3D local path planning policies. It determines actions in 3D space based on multiple value iteration computations approximated by recurrent 2D convolutional neural networks. Moreover, a path planning framework is also developed to realize near-optimal real-time online path planning. The effectiveness of the proposed planner is further improved by a switching scheme, and the path quality is optimized by line-of-sight checks. Both virtual and real-world experimental results demonstrate the remarkable performance of the proposed DNN-based path planner in terms of efficiency, success rate and path quality. Different from existing methods, the computational time and effectiveness of the developed DNN-based path planner are both independent of environmental conditions, which reveals its superiority in large-scale complex environments. A video of the experiments can be found at: https://www.youtube.com/watch?v=gb4nSG4hd6s.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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Supplemental Notes:
- Copyright © 2022, IEEE.
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Authors:
- Wu, Keyu
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0000-0001-8493-0712
- Wang, Han
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0000-0001-5448-9903
- Esfahani, Mahdi Abolfazli
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0000-0002-0709-0534
- Yuan, Shenghai
- Publication Date: 2022-3
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References;
- Pagination: pp 2093-2102
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 23
- Issue Number: 3
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Neural networks; Real time data processing; Trajectory control
- Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01847640
- Record Type: Publication
- Files: TRIS
- Created Date: May 31 2022 9:16AM