Electronic Resource

ASSESSING THE EFFECTIVENESS OF A COMBAT UGV SWARM IN URBAN OPERATIONS

التفاصيل البيبلوغرافية
العنوان: ASSESSING THE EFFECTIVENESS OF A COMBAT UGV SWARM IN URBAN OPERATIONS
المؤلفون: Yakimenko, Oleg A., Papoulias, Fotis A., Systems Engineering (SE), Teow, Boon Hong Aaron
بيانات النشر: Monterey, CA; Naval Postgraduate School 2018-10-26T19:20:45Z 2018-10-26T19:20:45Z 2018-09
نوع الوثيقة: Electronic Resource
مستخلص: Due to its complexity, an urban area is a challenging multi-dimensional environment for ground warfare. Recent technological advancements have enabled militaries to utilize different-size unmanned ground vehicles (UGV) to support a variety of missions. This thesis presents guidance algorithms for a search and kill mission developed for some generic UGV swarms, which may be an attractive application, particularly for smaller UGVs operating in an urban environment. Through a series of computer simulations, the research evaluates the feasibility and effectiveness of the algorithms in executing such a mission in indoor and outdoor urban environments. The developed simulation allows varying many parameters, thus achieving closeness to the real-world situation when different environments, platforms, sensors, and weapons are used. Computer simulations presented in this paper may also assist military leaders in choosing key mission parameters to maximize the outcome of potential future engagements.
http://archive.org/details/assessingtheeffe1094560354
Outstanding Thesis
Army, Singapore
Approved for public release; distribution is unlimited.
مصطلحات الفهرس: swarm, unmanned ground vehicle, UGV, Particle Swarm Optimization, modeling and simulation, Thesis
URL: https://hdl.handle.net/10945/60354
الاتاحة: Open access content. Open access content
Copyright is reserved by the copyright owner.
ملاحظة: application/pdf
Other Numbers: AD# oai:calhoun.nps.edu:10945/60354
31668
1076482884
المصدر المساهم: NAVAL POSTGRADUATE SCH
From OAIster®, provided by the OCLC Cooperative.
رقم الانضمام: edsoai.on1076482884
قاعدة البيانات: OAIster