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Real-time and Coordinated UAV Path Planning for Road Traffic Surveillance: A Penalty-based Boundary Intersection Approach

  • Robot and Applications
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Abstract

Traffic surveillance of mountain roads accords problems of high risk, low efficiency, and high cost; thus, unmanned aerial vehicles (UAVs) are introduced for traffic surveillance, working in conjunction with a delivery van. UAVs are assigned to monitor the high-risk road segments, while the low-risk segments are monitored by the delivery van. UAVs take off from and return to the delivery van to improve the speed and flexibility of the traffic surveillance, with reduced risk. First, a real-time and coordinated UAV path-planning problem is presented, in which the number of monitored targets is dynamic and the delivery van is moving. Then, a multi-objective optimization model is proposed to minimize both the coordination value of UAV flight distance and the number of UAVs used. Next, a penalty-based boundary intersection optimization algorithm is proposed, which adopts the decomposition strategy and Pareto technique to acquire the optimized paths in real-time. In addition, a case study is implemented to compare the proposed algorithm with two commonly used algorithms and the results indicate that the proposed algorithm performs better with respect to solution quality and calculation time. Moreover, the cost versus performance of the UAV traffic surveillance was analyzed. This demonstrates that it is effective to use the proposed approach to conduct real-time and coordinated UAV path planning. Finally, the limitations and prospects of UAV traffic surveillance are presented.

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Author information

Authors and Affiliations

  1. School of Transportation and Automotive, Tianjin University of Technology and Education, Tianjin, 300222, China

    Xiaofeng Liu & Qiang Chen

  2. Department of Urban and Regional Planning, University of Florida, Gainesville, 32611, USA

    Zhong-Ren Peng

  3. A*STAR, Institute of High Performance Computing, Singapore, 138632, Singapore

    Li-Ye Zhang

Authors
  1. Xiaofeng Liu
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  2. Zhong-Ren Peng
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  3. Li-Ye Zhang
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  4. Qiang Chen
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Corresponding author

Correspondence to Xiaofeng Liu.

Additional information

This work was supported by the National Natural Science Foundation of China (No. 51408417) and the Science and Technology Plan Project of Tianjin, China (No. 19JCQNJC03400, XC202028, KYQD202107, 2021KJ018).

Xiaofeng Liu received his B.S. and M.S. degrees from the Wuhan University of Technology, Wuhan, China, in 2004 and 2007, respectively, and a Ph.D. degree in traffic information engineering and control from the Tongji University, Shanghai, China, in 2013. He currently works in the School of Automotive and Transportation, Tianjin University of Technology and Education, China. His current research interest lies in the area of transportation applications using unmanned aerial vehicles.

Zhong-Ren Peng received his B.S. degree from the Central China Normal University, Wuhan, China, in 1983, an M.S. degree from the Chinese Academy of Sciences, Beijing, China, in 1986, and a Ph.D. degree from the Portland State University, Oregon, USA, in 1994. He currently works at the Department of Urban and Regional Planning, University of Florida, USA. His current research interest lies in the area of air quality analysis using unmanned aerial vehicles and urban transportation planning.

Li-Ye Zhang received his B.S. and M.S. degrees from the Changsha University of Science and Technology, Changsha, China, in 2002 and 2005, respectively, and a Ph.D. degree in traffic information engineering and control from the Tongji University, Shanghai, China, in 2014. He occupied the traffic safety post-doctoral research work from 2014 to 2016 in the National University of Singapore. He currently works at the Institute of High-Performance Computing, Singapore. His current research interest lies in the area of transportation optimization.

Qiang Chen received his Ph.D. degree from Jilin University, Changchun, China, in 2017. He currently works in the School of Automotive and Transportation, Tianjin University of Technology and Education, China. His currently research interests include road traffic accident scene investigation, intelligent transportation safety, and deep learning.

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Liu, X., Peng, ZR., Zhang, LY. et al. Real-time and Coordinated UAV Path Planning for Road Traffic Surveillance: A Penalty-based Boundary Intersection Approach. Int. J. Control Autom. Syst. 20, 2655–2668 (2022). https://doi.org/10.1007/s12555-020-0565-8

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  • DOI: https://doi.org/10.1007/s12555-020-0565-8

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