An optimal routing policy for unmanned aerial vehicles (analytical and cross-entropy simulation approach)

Abstract

We consider a real-world problem of military intelligence unit equipped with identical unmanned aerial vehicles producing real-time imagery and responsible for heterogeneous regions (with requests of real-time jobs) required to be under nonstop surveillance. Under certain assumptions these real-time systems can be treated as queueing networks.The use of the system involving unmanned aerial vehicles relies on the principle of availability, namely on its ability to process the maximal portion of real-time tasks. We show that even very large number of vehicles does not guarantee the maximal system availability without proper choice of routing probabilities. We compute analytically (for exponentially distributed service and maintenance times) and via simulation using Cross-Entropy method (for generally distributed service times) optimal routing probabilities which maximize system availability. Notations and abbreviationsCE Cross Entropy CMC Crude Monte Carlo RTS Real-Time System UAV Unmanned Aerial Vehicle N Number of identical UAVs/servers r Number of heterogeneous regions/channels r kNumber of real-time tasks/jobs in k-th region at any instant (r k ≥ 1) λThe maintenance rate (exponential distribution)

Connectivity Maintenance Based on Multiple Relay UAVs Selection Scheme in Cooperative Surveillance

“…Due to the limited capacity of single UAV, in the future information and network centric environment, multiple UAVs have been widely employed in military and civil applications [1][2][3][4][5] due to the feasibility and scalability in complex tasks in recent years, such as target tracking, wildlife monitoring, disaster rescue and so on. Serving as communication relays for remote monitoring and situation awareness [6], multiple UAVs dynamically constructing a reliable peer-to-peer communication links chain has been studied in recent years [7][8][9].…”

A Systems-Of-Systems Conceptual Model and Live Virtual Constructive Simulation Framework for Improved Nuclear Disaster Emergency Preparedness, Response, and Mitigation

“…These models are all specialized to produce high fidelity predictions of very specific components of fuel and reactor behavior, accident progression, radionuclide transport, and dosing and some do so with fairly fast run times (NRC 2015a). Literature on operations research, nuclear power plants, nuclear reactor accidents and simulation is vast with (Saggiani and Teodorani 2004;Farradyne 2005;Bernard et al 2008;McGonigle et al 2008;Peräjärvi et al 2008;Alexis et al 2009;Flint et al 2009;Girault et al 2010;Ianovsky and Kreimer 2011;Alver et al 2012;Marconi et al 2012;Towler et al 2012;Ai-Omari et al 2013;Chaimatanan et al 2013;Mase 2013Mase , 2015Holden and Dickerson 2013;Liu et al 2014;MacFarlane et al 2014;Ouyang et al 2014;Hu et al 2015;Sheng et al 2015;Wang et al 2015b) being but a few examples. Many of these works utilize simulation techniques, due in part to the complex nature of UAS and their environmental interactions and the difficulty in analytically evaluating real-world systems (Law and Kelton 2000).…”

Performance Evaluation of Multiple Unmanned Aerial Vehicles Operating in General Regime with Shortage of Maintenance Facilities

“…In [11], it is shown how the complex UAV availability model with ample maintenance facilities and general life time and maintenance distributions can be tackled analytically by using a basic model from reliability theory. In [12], it was shown that even very large number of UAVs did not guarantee the maximum system availability, and optimal routing probabilities were computed analytically (for exponentially distributed service times) via Cross Entropy [13]- [15] simulation approach (for generally distributed service times).…”