军用无人水面航行器(USV)用于执行包括反水雷、反潜作战、海上安保、海上作战、特种作战支援、电子战和海上拦截作战支持等7个主要任务使命,通过提升军用USV平台的战术协同自治和控制能力,可以最大限度地减少作战人员配备,降低USV平台的带宽要求,实现更快、更同步的协同控制和机动。军用USV战术协同自治和控制研究涉及到系统工程、控制技术和信息技术等多门学科的深度融合,代表了航海科学与技术的前沿研究领域和未来发展方向。文章改进了军用USV的协同自治和控制系统架构,包括感知及协同系统、引导系统、控制系统、执行机构等组成部分,并对其基本原理和要求进行了详细阐述。
Military Unmanned Surface Vehicles can be used for seven major missions, including anti-mine mines, anti-submarine warfare, maritime security, maritime operations, special operations support, electronic warfare, and maritime interception operations support. The tactical collaborative autonomy and control capabilities of the military USV platform can minimize the deployment of combat personnel, reduce the bandwidth requirements of the USV platform, and enable faster, more synchronized coordinated control and maneuvering. Military USV's tactical collaborative autonomy and control research involves the in-depth integration of multiple disciplines such as systems engineering, control technology, and information technology, and represents the frontier research field and future development direction of maritime science and technology. This article improved the collaborative autonomy and control system architecture of military USV, including components of sensing and coordination systems, guidance systems, control systems, and implementing agencies, and elaborated on its basic principles and requirements.
2018,40(12): 141-145 收稿日期:2018-03-26
DOI:10.3404/j.issn.1672-7649.2018.12.029
分类号:U674.7
基金项目:武器装备预先研究项目
作者简介:李昆鹏(1987-),男,工程师,从事无人系统工程技术等方面的研究
参考文献:
[1] WASIF N, GEORGE W. COLREGs-based collision avoidance strategies for unmanned surface vehicles[J]. Mechatronics, 2012
[2] CARLOS C. Metric assessment of autonomous capabilities in unmanned maritime vehicles[J]. Engineering Applications of Artificial Intelligence, 2014
[3] LIU Yuan-chang, BUCKNALL R. Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment[J]. Ocean Engineering, 2015
[4] THOMAS G, MATTHIAS S, PETER O. Cooperative line of sight target tracking for heterogeneous unmanned marine vehicle teams:From theory to practice[J]. Robotics and Autonomous Systems, 2015
[5] REIHANE R, FARZANEH A, KARO N. Time-varying formation control of a collaborative heterogeneous multi agent system[J]. Robotics and Autonomous Systems, 2014
[6] TOMASZ P. Neural anti-collision system for autonomous surface vehicle[J]. Neurocomputing, 2015
[7] TAN Xiaobin, QIN Guihong, ZHANG Yong, et al. Network security situation awareness using exponential and logarithmic analysis[C]//5th International Conference on Information Assurance and Security, IAS 2009, Xian, China, 2009.
[8] BOCANIALA C. D, SASTRYV V. S. S. On enhanced situational awareness models for unmanned aerial systems[C]//IEEE Aerospace Conference Proceedings in 2010 IEEE Aerospace Conference, Big Sky, MT, United states, 2010.
[9] S. A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres[J]. Annual Reviews in Control, 2012
[10] SEUNGKEUN KIM, HYONDONG OH, JINYOUNG SUK, et al. Coordinated trajectory planning for efficient communication relay using multiple UAVs[J]. Control Engineering Practice, 2014
[11] ANTONELLI G, BAIZID K, CACCAVALE F, et al. CAVIS:a control software architecture for cooperative multi-unmanned aerial vehicle-manipulator systems[C]//The 19th World Congress of The International Federation of Automatic Control. Cape Town, South Africa. 2014.