Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (2): 463-472.doi: 10.23919/JSEE.2023.000118


Distributed collaborative complete coverage path planning based on hybrid strategy

Jia ZHANG1,2(), Xin DU1,2(), Qichen DONG1,2(), Bin XIN1,2,*()   

  1. 1 School of Automation, Beijing Institute of Technology, Beijing 100081, China
    2 National Key Laboratory of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China
  • Received:2022-03-01 Online:2024-04-18 Published:2024-04-18
  • Contact: Bin XIN;;;
  • About author:
    ZHANG Jia was born in 1980. She received her Ph. D. degree from Beijing Institute of Technology in 2015. She is a senior experimentalist in Beijing Institute of Technology. Her research interests are multi-agent system and intelligent information processing. E-mail:

    DU Xin was born in 1996. He received his M.S. degree from Beijing Institute of Technology in 2021. He is an engineer in China Aerospace Science and Industry Group now. His research interests are intelligence optimization algorithm, multi-agent system, and task planning method. E-mail:

    DONG Qichen was born in 1998. He received his B.S. degree from Beijing Institute of Technology, Beijing, China in 2020. He is currently pursuing his M.E. degree in pattern recognition and intelligent system in Beijing Institute of Technology. His research interests are distributed multi-agent system, task assignment, and path planning. E-mail:

    XIN Bin was born in 1982. He received his Ph. D. degree from Beijing Institute of Technology in 2012. He is a professor in Beijing Institute of Technology. His research interests are multi-agent collaboration and intelligent optimization. E-mail:
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61903036, 61822304) and Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100).


Collaborative coverage path planning (CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle (UAV) collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment. Due to flexible control of small UAVs, it can be considered that all UAVs fly at the same altitude, that is, they perform search tasks on a two-dimensional plane. Based on the agents’ motion characteristics and environmental information, a mathematical model of CCPP problem is established. The minimum time for UAVs to complete the CCPP is the objective function, and complete coverage constraint, no-fly constraint, collision avoidance constraint, and communication constraint are considered. Four motion strategies and two communication strategies are designed. Then a distributed CCPP algorithm is designed based on hybrid strategies. Simulation results compared with pattern-based genetic algorithm (PBGA) and random search method show that the proposed method has stronger real-time performance and better scalability and can complete the complete CCPP task more efficiently and stably.

Key words: multi-agent cooperation, unmanned aerial vehicles (UAV), distributed algorithm, complete coverage path planning (CCPP)