Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (5): 1515-1521.DOI: 10.11772/j.issn.1001-9081.2019112047

• Frontier & interdisciplinary applications • Previous Articles     Next Articles

Multi-unmanned aerial vehicle adaptive formation cooperative trajectory planning

XU Yang1,2, QIN Xiaolin1,2, LIU Jia1,2, ZHANG Lige1,2   

  1. 1.Chengdu Institute of Computer Application, Chinese Academy of Sciences, ChengduSichuan 610041, China
    2.School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100080, China
  • Received:2019-12-02 Revised:2020-01-07 Online:2020-05-10 Published:2020-05-15
  • Contact: QIN Xiaolin, born in 1980, Ph. D., research fellow. His research interests include artificial intelligence, automatic reasoning.
  • About author:XU Yang, born in 1994, M. S., candidate. His research interests include UAV cooperative control, machine learning, swarm intelligence.QIN Xiaolin, born in 1980, Ph. D., research fellow. His research interests include artificial intelligence, automatic reasoning.LIU Jia, born in 1995, M. S., candidate. Her research interests include trajectory planning, machine learning.ZHANG Lige, born in 1995, Ph. D., candidate. His research interests include machine learning, optimization algorithm.
  • Supported by:

    This work is partially supported by the National Natural Science Foundation of China (61402537), The “Light of West China” Program of Chinese Academy of Sciences, the Special Funding for Talents of the Organization Department of Sichuan Provincial Party Committee, the Open Fund Project of Guangxi Key Laboratory of Hybrid Computing and IC Design Analysis (HCIC201706).


许洋1,2, 秦小林1,2, 刘佳1,2, 张力戈1,2   

  1. 1.中国科学院 成都计算机应用研究所,成都 610041
    2.中国科学院大学 计算机与控制学院,北京 100080
  • 通讯作者: 秦小林(1980—)
  • 作者简介:许洋(1994—),男,重庆人,硕士研究生,主要研究方向:无人机协同控制、机器学习、集群智能; 秦小林(1980—),男,重庆合川人,研究员,博士生导师,博士,主要研究方向:人工智能、自动推理; 刘佳(1995—),女,宁夏银川人,硕士研究生,主要研究方向:航迹规划、机器学习; 张力戈(1995—),男,山西原平人,博士研究生,主要研究方向:机器学习、优化算法。
  • 基金资助:



Aiming at the problem of neglecting some narrow roads due to the formation constraints in the multi-UAV (Unmanned Aerial Vehicle) cooperative trajectory planning, a Fast Particle Swarm Optimization method based on Adaptive Distributed Model Predictive Control (ADMPC-FPSO) was proposed. In the method, the formation strategy combining leader-follower method and virtual structure method was used to construct adaptive virtual formation guidance points to complete the cooperative formation control task. According to the idea of model predictive control, combined with the distributed control method, the cooperative trajectory planning was transformed into a rolling online optimization problem, and the minimum distance and other performance indicators were used as cost functions. By designing the evaluation function criterion, the variable weight fast particle swarm optimization algorithm was used to solve the problem. The simulation results show that the proposed algorithm can effectively realize the multi-UAV cooperative trajectory planning, can quickly complete the adaptive formation transformation according to the environmental changes, and has lower cost than the traditional formation strategy.

Key words: trajectory planning, cooperative control, adaptive, model predictive control, distributed, particle swarm optimization



关键词: 航迹规划, 协同控制, 自适应, 模型预测控制, 分布式, 粒子群优化

CLC Number: