重访机制驱动的多无人机协同动目标搜索方法

doi:10.7527/S1000-6893.2019.23314

电子电气工程与控制

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重访机制驱动的多无人机协同动目标搜索方法

张哲璇^1,2, 龙腾^1,2, 徐广通^1,2, 王仰杰¹

1. 北京理工大学宇航学院, 北京 100081;
2. 北京理工大学飞行器动力学与控制教育部重点实验室, 北京 100081

收稿日期:2019-07-25 修回日期:2019-08-23 出版日期:2020-05-15 发布日期:2019-11-14
通讯作者: 龙腾 E-mail:tenglong@bit.edu.cn
基金资助:
国家自然科学基金（51675047）

Revisit mechanism driven multi-UAV cooperative search planning method for moving targets

ZHANG Zhexuan^1,2, LONG Teng^1,2, XU Guangtong^1,2, WANG Yangjie¹

1. School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China;
2. Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education, Beijing Institute of Technology, Beijing 100081, China

Received:2019-07-25 Revised:2019-08-23 Online:2020-05-15 Published:2019-11-14
Supported by:
National Natural Science Foundation of China (51675047)

摘要/Abstract

摘要： 为实现多无人机高效捕获灰色任务区域内的移动目标，考虑传感器探测概率与虚警概率，提出了重访机制驱动的协同搜索规划（RMD-CSP）方法，以降低目标遗漏与误判概率。考虑无人机飞行性能约束，以最大化任务执行效能为目标建立多无人机协同搜索模型。根据目标先验信息初始化环境搜索信息图（包括目标概率分布图、环境不确定度图与环境搜索状态图），利用无人机实时探测信息，基于贝叶斯准则持续更新搜索信息图。定制基于环境不确定度更新的重访机制，通过增加长时间未被重访区域的环境不确定度，引导无人机搜索该区域，降低移动目标的遗漏概率；定制基于目标函数权重更新的重访机制，引导无人机快速重访发现新的疑似目标的区域，对疑似目标进行再次确认，减少由于传感器虚警概率造成的目标误判概率。采用滚动时域规划架构，将搜索规划问题分解为一系列短时域规划问题，提升了求解效率。在典型任务想定下，通过数值仿真试验验证了所提方法的有效性。仿真结果表明，RMD-CSP能够在秒级时间内生成每个时域的搜索航迹，相比于光栅式搜索方法与标准的概率启发式搜索方法，能够引导无人机捕获更多的移动目标，同时减少误判次数，有效提升了多无人机协同搜索的任务效能。

关键词: 协同搜索规划, 移动目标, 搜索信息图, 重访机制, 探测概率, 虚警概率

Abstract: To efficiently capture moving targets in unknown regions using multi-UAVs, this paper presents a Revisit Mechanism Driven Cooperative Search Planning (RMD-CSP) method to reduce the probability of missing targets and judgmental errors of the sensors. The multi-UAV cooperative search model, subject to the flight performance constraints, is established to maximize the task execution performance. The search maps (i.e., target probability maps, uncertainty maps, and environment search status maps) are initialized according to the prior information of the target, and then updated using Bayes Criterion according to the detected information by the UAVs. The revisit mechanism based on environment-uncertainty-renewal is customized to reduce the missing-target probability. This mechanism guides the UAVs to search the region that has not been revisited for a long time by enlarging the uncertainty of this region. In addition, the revisit mechanism based on objective-function-weight-renewal is customized to direct the UAVs to revisit the region where a new suspected target is found, and check the existence of the target to reduce the judgmental errors caused by the false-alarm probability of the sensors. Based on the receding horizon framework, the search planning problems are divided into a series of short-horizon planning problems to save computational costs. Simulation studies are conducted under classical mission scenarios to verify the effectiveness of the proposed method. Simulation results demonstrate that the RMD-SCP can generate search paths in seconds for each receding horizon. Compared with the scan-search algorithm and the standard probability heuristic algorithm, the RMD-CSP can guide the UAVs to capture more moving targets with fewer judgmental errors, indicating the effectiveness of the proposed method in improving the efficiency of multi-UAV cooperative search missions.

Key words: cooperative search planning, moving targets, search maps, revisit mechanisms, detection probability, false-alarm probability

中图分类号:

张哲璇, 龙腾, 徐广通, 王仰杰. 重访机制驱动的多无人机协同动目标搜索方法[J]. 航空学报, 2020, 41(5): 323314-323314.

ZHANG Zhexuan, LONG Teng, XU Guangtong, WANG Yangjie. Revisit mechanism driven multi-UAV cooperative search planning method for moving targets[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(5): 323314-323314.

参考文献 31

[1]	DARRAH M, NILAND W, STOLARIK B, et al. UAV cooperative task assignments for a SEAD mission using genetic algorithms[C]//AIAA Guidance, Navigation, and Control Conference and Exhibit. Reston:AIAA, 2006.
[2]	NIGAM N, BIENIAWSKI S, KROO I, et al. Control of multiple UAVs for persistent surveillance:Algorithm and flight test results[J]. IEEE Transactions on Control Systems Technology, 2012, 20(5):1236-1251.
[3]	BERTUCCELLI L F, HOW J P. Robust UAV search for environments with imprecise probability maps[C]//IEEE Conference on Decision and Control. Piscataway:IEEE Press, 2005.
[4]	KOLLER D, FRIEDMAN N. Probabilistic graphical models:Principles and techniques[M]. Cambridge:MIT Press, 2009.
[5]	TSAMARDINOS I, BROWN L E, ALIFERIS C F. The max-min hill-climbing Bayesian network structure learning algorithm[J]. Machine Learing, 2006, 65(1):31-78.
[6]	BERTUCCELLI L F, HOW J P. UAV Search for dynamic targets with uncertain motion models[C]//IEEE Conference on Decision and Control. Piscatawoy:IEEE Press, 2006.
[7]	BERTUCCELLI L, BECKERS W, CUMMINGS M. Developing operator models for UAV search scheduling[C]//AIAA Guidance, Navigation, and Control Conference. Reston:AIAA, 2010.
[8]	VALENTE J, BARRIENTOS A, CERRO J D, et al. Multi-robot visual coverage path planning:Geometrical metamorphosis of the workspace through raster graphics based approaches[C]//International Conference on Computational Science and Its Applications, 2011.
[9]	CORTES J, MARTINEZ S, KARATAS T. Coverage control for mobile sensing networks[C]//IEEE International Conference on Robotics and Automation. Piscatawoy:IEEE Press, 2003.
[10]	HU J, XIE L, LUM K Y, et al. Multiagent information fusion and cooperative control in target search[J]. IEEE Transactions on Control Systems Technology, 2013, 21(4):1223-1235.
[11]	沈东, 魏瑞轩, 祁晓明, 等. 基于MTPM和DPM的多无人机协同广域目标搜索滚动时域决策[J]. 自动化学报, 2014, 40(7):1391-1403. SHEN D, WEI R X, QI X M, et al. Receding horizon decision method based on MTPM and DPM for multi-UAVs cooperative large area target search[J]. Acta Automatica Sinica, 2014, 40(7):1391-1403(in Chinese).
[12]	彭辉. 分布式多无人机协同区域搜索中的关键问题研究[D]. 长沙:国防科技大学. 2009. PENG H. Research on key issues in distributed multi-UAV cooperative area search[D]. Changsha:National University of Defense Technology, 2009(in Chinese).
[13]	彭辉, 沈林成, 朱华勇. 基于分布式模型预测控制的多UAV协同区域搜索[J]. 航空学报, 2010, 31(3):593-601. PENG H, SHEN L C, ZHU H Y. Multiple UAV cooperative area search based on distributed model predictive control[J]. Acta Aeronautica et Astronautica Sinica, 2010, 31(3):593-601(in Chinese).
[14]	CHEN G, ZHEN Z, GONG H. A self-organized search and attack algorithm for multiple unmanned aerial vehicles[J]. Aerospace Science and Technology, 2016, 54:229-240.
[15]	ZHEN Z, XING D, CHEN G. Cooperative search-attack mission planning for multi-UAV based on intelligent self-organized algorithm[J]. Aerospace Science and Technology, 2018, 76:402-411.
[16]	田菁, 陈岩, 沈林成. 不确定环境中多无人机协同搜索算法[J]. 电子与信息学报, 2007, 29(10):2325-2328. TIAN J, CHEN Y, SHEN L C. Cooperative search algorithm for multi-UAVs in uncertainty environment[J]. Journal of Electronics and Information Technology, 2007, 29(10):2325-2328(in Chinese).
[17]	刘重, 高晓光, 符小卫. 带信息素回访机制的多无人机分布式协同目标搜索[J]. 系统工程于电子技术, 2017, 39(9):1998-2011. LIU Z, GAO X G, FU X W. Multi-UAVs distributed cooperative target search algorithm with controllable revisit mechanism based on digital pheromone[J]. Systems Engineering and Electronics, 2017, 39(9):1998-2011(in Chinese).
[18]	SUN A K, LIU H. Cooperative UAV search for moving targets using a modified diffusion uncertainty model[C]//AIAA Guidance, Navigation, and Control Conference. Reston:AIAA, 2009.
[19]	朱梦圆, 吕娜, 陈柯帆, 等. 航空集群协同搜索马尔可夫运动目标方法[J]. 系统工程与电子技术, 2019, 41(9):2041-2047. ZHU M Y, LYU N, CHEN K F, et al. Collaborative aeronautic swarm search of Markov moving targets[J]. Systems Engineering and Electronics, 2019, 41(9):2041-2047(in Chinese).
[20]	孙希霞, 蔡超. 基于多蚁群系统的多无人机协同目标搜索方法[J]. 战术导弹技术, 2014(6):26-31. SUN X X, CAI C. A cooperative target searching method based on multiple ant colony optimization algorithm[J]. Tactical Missile Technology, 2014(6):26-31(in Chinese).
[21]	王允良, 李为吉. 物理规划方法及其在飞机方案设计中的应用[J]. 航空学报, 2005, 26(5):562-566. WANG Y L, LI W J. Physical programming and its application in aircraft concept design[J]. Acta Aeronautica et Astronautica Sinica, 2005, 26(5):562-566(in Chinese).
[22]	黄其旺. 基于改进概率图的多无人机协同搜索策略研究[D]. 长沙:国防科技大学, 2012. HUANG Q W. Cooperative searching strategy for multiple unmanned aerial vehicles based on modified probability map[D]. Changsha:National University of Defense Technology, 2012(in Chinese).
[23]	温永禄. 不同信息条件下的多无人机协同区域搜索航迹规划研究[D]. 北京:北京理工大学, 2016. WEN Y L. Study of multiple UAV cooperative path plan for area search under various area information conditions[D]. Beijing:Beijing Institute of Technology, 2016(in Chinese).
[24]	MIRZAEI M, SHARIFI F, GORDON B W, et al. Cooperative multi-vehicle search and coverage problem in uncertain environments[J]. Unmanned Systems, 2015, 3(1):35-47.
[25]	TIRUMALAI A P, SCHUNCK B G, JAIN R C. Evidential reasoning for building environment maps[J]. IEEE Transactions on Systems Man Cybernetics, 2002, 25(1):10-20.
[26]	WANG X, YADAV V, BALAKRISHNAN S N. Cooperative UAV formation flying with obstacle/collision avoidance[J]. IEEE Transactions on Control Systems Technology, 2007, 15(4):672-679.
[27]	YAO M, ZHAO M. Unmanned aerial vehicle dynamic path planning in an uncertain environment[J]. Robotica, 2015, 33(3):611-621.
[28]	祁晓明, 魏瑞轩, 沈东, 等. 基于运动目标预测的多无人机分布式协同搜索[J]. 系统工程与电子技术, 2014, 36(12):2417-2425. QI X M, WEI R X, SHEN D. Distributed cooperative search methods of multi-UAV based on prediction of moving targets[J]. Systems Engineering and Electronics, 2014, 36(12):2417-2425(in Chinese).
[29]	李相民, 薄宁, 代进进. 基于模型预测控制的多无人机避碰航迹规划研究[J]. 西北工业大学学报, 2017, 35(3):513-522. LI X M, BO N, DAI J J. Study on collision avoidance path planning for multi-UAVs based on model predictive control[J]. Journal of Northwestern Polytechnical University, 2017, 35(3):513-522(in Chinese).
[30]	温永禄, 龙腾, 刘大卫, 等. 多飞行器协同光栅式搜索模式的效能对比研究[J]. 战术导弹技术, 2015(4):10-13. WEN Y L, LONG T, LIU D W, et al. Research on the comparison of several multiple aerial vehicles cooperative raster searching mode[J]. Tactical Missile Technology, 2015(4):10-13(in Chinese).
[31]	肖爱明, 陈朝晖, 周国辉. 多舰协同侦察的目标探测概率[J]. 电子信息对抗技术, 2012(1):71-74. XIAO A M, CHEN Z H, ZHOU G H. Surveillance probability based on multi-platform cooperative reconnaissa-nce[J]. Electronic Information Warfare Technology, 2012(1):71-74(in Chinese).

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重访机制驱动的多无人机协同动目标搜索方法

Revisit mechanism driven multi-UAV cooperative search planning method for moving targets

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