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Global Off-Road Path Planning of Unmanned Ground Vehicles Based on the Raw Remote Sensing Map
Technical Paper
2023-01-0699
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Unmanned Ground Vehicle (UGV) has a wide range of applications in the military, agriculture, firefighting and other fields. Path planning, as a key aspect of autonomous driving technology, plays an essential role for UGV to accomplish the established driving tasks. At present, there are many global path planning algorithms in grid maps on unstructured roads, while general grid maps do not consider the specific elevation or ground type difference of each grid, and unstructured roads are generally considered as flat and open roads. On the contrary, the unmanned off-road is always a bumpy road with undulating terrain, and meanwhile, the landform is complex and the types of features are diverse. In order to ensure the safety and improve the efficiency of autonomous driving of UGV in off-road environment, this paper proposes a global off-road path planning method for UGV based on the raw image of remote sensing map. Firstly, the raw image is gridded. The map elevation information is assigned based on the digital elevation model (DEM) and the terrain is classified and labeled in the grid map based on the back propagation neural network (BPNN). Based on the reconstructed off-road grid map, a modified A* algorithm considering the safety and efficiency of UGV passage is designed for global path planning on off-road environment. Simulation results based on real off-road environment show that the proposed global planning algorithm can avoid impassable areas and make UGVs drive on high traffic efficiency roads as much as possible.
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Citation
Zhang, J., Xie, F., Wang, C., Liu, Q. et al., "Global Off-Road Path Planning of Unmanned Ground Vehicles Based on the Raw Remote Sensing Map," SAE Technical Paper 2023-01-0699, 2023, https://doi.org/10.4271/2023-01-0699.Also In
References
- Dolgov , D. , Thrun , S. , Montemerlo , M. , and Diebel , J. Path Planning for Autonomous Vehicles in Unknown Semi-Structured Environments The international journal of robotics research . 29 5 2010 485 501
- Dallas , J. , Weng , Y. , and Ersal , T. Combined Trajectory Planning and Tracking for Autonomous Vehicles on Deformable Terrains Dynamic Systems and Control Conference 84270 2020
- Dickmanns , E.D. and Graefe , V. Dynamic Monocular Machine Vision Machine vision and applications . 1 4 1988 223 240
- Paden , B. , Čáp , M. , Yong , S.Z. , Yershov , D. et al. A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles IEEE Transactions on intelligent vehicles . 1 1 2016 33 55
- Dallas , J. , Cole , M.P. , Jayakumar , P. , and Ersal , T. Terrain Adaptive Trajectory Planning and Tracking on Deformable Terrains IEEE Transactions on Vehicular Technology . 70 11 2021 11255 11268
- Lacroix , S. , Mallet , A. , Bonnafous , D. , Bauzil , G. et al. Autonomous Rover Navigation on Unknown Terrains: Functions and Integration The International Journal of Robotics Research . 21 10–11 2002 917 942
- Polidori , L. and El Hage , M. Digital Elevation Model Quality Assessment Methods: A Critical Review Remote sensing . 12 21 2022 3522
- Uysal , M. , Toprak , A.S. , and Polat , N. DEM Generation with UAV Photogrammetry and Accuracy Analysis in Sahitler Hill Measurement . 73 2015 539 543
- Wang , S. , Kodagoda , S. , and Ranasinghe , R. Road Terrain Type Classification Based on Laser Measurement System Data Australasian Conference on Robotics and Automation 2012
- Weiss , C. , Frohlich , H. , and Zell , A. Vibration-Based Terrain Classification Using Support Vector Machines IEEE/RSJ international conference on intelligent robots and systems 2006
- Otsu , K. , Ono , M. , Fuchs , T.J. , Baldwin , I. et al. Autonomous Terrain Classification with Co-and Self-Training Approach IEEE Robotics and Automation Letters . 1 2 2016 814 819
- Marir , B. A Modular Support Vector Machine for Active Learning of Urban Remote Sensing Images Classification in Algeria JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING . 46 04 2018 515 529
- Zhang , J.H. The Study of Remote Sensing Image Classification Based on Support Vector Machine Sensors and Transducers . 159 11 2013 46 53
- Peng , T.Q. A Remote Sensing Image Classification Method Based on Evidence Theory and Neural Networks International Conference on Neural Networks and Signal Processing 2003 02 170 174
- Li , T.W. , Liu , X.J. , Chen , Z.J. et al. Study on the Accuracy and Algorithms for Calculating Slopes and Aspects Based on the Digital Elevation Model Arid Land Geography . 27 3 2004 398 404
- Wu , T.Y. , Xu , J.H. , Liu , J.Y. et al. Research of Cross-Country Path Planning Based on Improved a* Algorithm Application Research of Computers . 6 2013 1724 1726