动态环境下的多无人机协同任务规划仿真

系统仿真学报 ›› 2016, Vol. 28 ›› Issue (9): 2126-2132.

动态环境下的多无人机协同任务规划仿真

乔体洲, 郭新平, 李亚威, 张文军, 尹中义

中国航天电子技术研究院无人机系统工程研究所,北京 100094

收稿日期:2015-06-10 修回日期:2015-07-30 出版日期:2016-09-08 发布日期:2020-08-14
作者简介:乔体洲(1983-),男,河北,博士,工程师,研究方向为虚拟现实技术及应用;郭新平(1982-),男,江西,硕士,高工,研究方向为地理信息科学及应用;李亚威(1989-),女,河北,硕士,助工,研究方向为虚拟现实技术及应用。

Cooperative Task Assignment Simulation of Multi-UAVs in Dynamic Environments

Qiao Tizhou, Guo Xinping, Li Yawei, Zhang Wenjun, Yin Zhongyi

UAV Systems Engineering Institute, China Aerospace Electronics Technology Research Institute, Beijing 100094, China

Received:2015-06-10 Revised:2015-07-30 Online:2016-09-08 Published:2020-08-14

摘要/Abstract

摘要： 为解决无人机编队协同任务规划的仿真问题,提出了一个基于HLA/RTI的并行仿真架构进行协同任务规划仿真,通过真实的无人机气动力学仿真和飞控仿真,可以更真实有效地验证算法方案。通过建立无人机的损失概率模型,优化路径时将环境中的不确定性因素和威胁因素考虑在内。通过将每架无人机的失败概率模型与其它所有无人机的选定任务设定耦合联系可以提升协同真实性。为了尽可能实现理想的任务目标,使用随机仿真模型设计协作规划,最大程度提高无人机间的协作关系以提升生存概率。

关键词: 无人机, 协同, 任务分配, 路径规划, 动态环境, 随机环境

Abstract: In order to address the problem of cooperative task assignment for multiple UAVs, a parallel simulation framework was proposed for cooperative task planning based on HLA/RTI simulation through UAV aerodynamic simulations and flight control simulation, which could be more real and effective algorithm validation. The paths were optimized to account for randomization and threats in the environment by modeling the probability of UAV loss. By coupling the failure probabilities of each UAV to the missions of the other UAVs, reality of cooperation was improved significantly. In order to achieve the desired objectives, the stochastic simulation models were used to design cooperation planning to enhance the cooperation between UAVs and improve survival probabilities.

Key words: UAV, cooperation, assignment, path planning, dynamic environment, stochastic environment

中图分类号:

TP391.9

乔体洲, 郭新平, 李亚威, 张文军, 尹中义. 动态环境下的多无人机协同任务规划仿真[J]. 系统仿真学报, 2016, 28(9): 2126-2132.

Qiao Tizhou, Guo Xinping, Li Yawei, Zhang Wenjun, Yin Zhongyi. Cooperative Task Assignment Simulation of Multi-UAVs in Dynamic Environments[J]. Journal of System Simulation, 2016, 28(9): 2126-2132.

参考文献 10

[1]	周昊, 覃征, 邢剑宽. 基于多Agent的多无人机协同决策算法仿真平台设计[J]. 系统仿真学报, 2012, 24(3): 53-60.
[2]	朱毅, 张涛, 程农, 等. 多UAV协同任务规划研究[J]. 系统仿真学报, 2008, 20: 194-199.
[3]	梁国伟, 王社伟, 赵雪森. 多无人机协同任务分配方法[J]. 火力与指挥控制, 2014, 11: 13-17.
[4]	Bellingham J, Tillerson M, Richards A, et al.Multi-Task Allocation and Path Planning for Cooperating UAVs[J]. Cooperative Control: Models, Applications and Algorithms (S1572-1272), 2003(1): 23-41.
[5]	Chandler P, Pachter M.Hierarchical Control for Autonomous Teams[C]// Proceedings of the AIAA Guidance, Navigation, and Control Conference. USA: AIAA, 2001: 632-642.
[6]	Bellingham J S, Tillerson M, Alighanbari M, et al.Cooperative Path Planning for Multiple UAVs in Dynamic and Uncertain Environments[C]// Proceedings of the IEEE Conference on Decision & Control. USA: IEEE, 2002, 3: 2816-2822.
[7]	Murphey R A.An approximate algorithm for a weapon target assignment stochastic program[J]. Approximation and Complexity in Numerical Optimization: Continuous and Discrete Problems (S1571-568X), 2000, 42: 406-421.
[8]	Ryan J L, Bailey T G, Moore J T, et al.Reactive Tabu Search in Unmanned Aerial Reconnaissance Simulations[C]// Proceedings of the Winter Simulation Conference. USA: WSC, 1998: 873-880.
[9]	McLain T W, Chandler P R, Rasmussen S, et al. Cooperative Control of UAV Rendezvous[C]// Proceedings of the American Control Conference. USA: ACC, 2001, 3: 2309-2314.
[10]	Bellingham J S.Coordination and Control of UAV Fleets using Mixed-Integer Linear Programming [D]. Boston, USA: Department of Aeronautics and Astronautics, MIT, 2002.

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