Planning the search for separating parts of the launch vehicle using a group of unmanned aerial vehicles

2020 ◽  
Author(s):  
V.I. Goncharenko ◽  
G.N. Lebedev ◽  
D.A. Mikhaylin
Keyword(s):  
Genetic Algorithm ◽  
Unmanned Aerial Vehicles ◽  
Launch Vehicles ◽  
Maintenance Schedule ◽  
Mobile Objects ◽  
Aerial Vehicles ◽  
The Right ◽  
Original Approach ◽  
Research Show ◽  
Better Than

The paper deals with the processes of maintaining a special class of mobile objects, whose schedules are either given or require preassignment in order to maintain these objects at the right time and in the right place. The posed problem of planning the flight of a group of aerial vehicles is solved using a continuous form of dynamic programming, according to which the Bellman equation in partial derivatives corresponds to the optimality condition. An original approach to solving the problem of pre-flight and operational planning of actions of a group of unmanned aerial vehicles based on a genetic algorithm is proposed. The fundamental difference between the problem being solved and the well-known traveling salesman problem is in taking into account the required maintenance schedule. The developed planning automation tool makes it possible to increase the efficiency of measures to detect separating parts of launch vehicles using a group of unmanned aerial vehicles. Findings of research show that the developed genetic algorithm is better not only than algorithms based on one-parameter and two-parameter criteria, but even better than algorithms based on a three-parameter criterion.

Protection of a Stationary Object from the Massive Impact of Mobile Objects under Conditions of von Neumann's Mixed Game

2021 ◽  
Vol 27 (10) ◽  
pp. 521-530
Author(s):  
O. N. Maslov ◽  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Simulation Modeling ◽  
Statistical Simulation ◽  
Operating Conditions ◽  
Protection System ◽  
Stationary Object ◽  
Physical Protection ◽  
Mobile Objects ◽  
Aerial Vehicles ◽  
Statistical Risk

The method of statistical simulation modeling (SSM) has been used to analyze the operating conditions and the efficiency of the physical protection system of a stationary object from the massive impact of unmanned aerial vehicles (drones). It is shown that the conditions of the problem correspond to the reflexive version of a two-sided von Neumann's mixed game. statistical risk-oriented characteristics for two variants of the object protection system implementation using force mechanical and electromagnetic effects on the "drones cloud" are determined. The possibilities and the prospects for using the results obtained using the SSM method are presented.

scholarly journals The Wane of Command: Evidence on Drone Strikes and Control within Terrorist Organizations

2020 ◽  
pp. 1-20
Author(s):  
ANOUK S. RIGTERINK
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Natural Experiment ◽  
Terrorist Attacks ◽  
Terrorist Organizations ◽  
Aerial Vehicles ◽  
Terrorist Groups ◽  
Drone Strikes ◽  
And Control ◽  
The Impact ◽  
Better Than

This paper investigates how counterterrorism targeting terrorist leaders affects terrorist attacks. This effect is theoretically ambiguous and depends on whether terrorist groups are modeled as unitary actors or not. The paper exploits a natural experiment provided by strikes by Unmanned Aerial Vehicles (drones) “hitting” and “missing” terrorist leaders in Pakistan. Results suggest that terrorist groups increase the number of attacks they commit after a drone “hit” on their leader compared with after a “miss.” This increase is statistically significant for 3 out of 6 months after a hit, when it ranges between 47.7% and 70.3%. Additional analysis of heterogenous effects across groups and leaders, and the impact of drone hits on the type of attack, terrorist group infighting, and splintering, suggest that principal-agent problems—(new) terrorist leaders struggling to control and discipline their operatives—account for these results better than alternative theoretical explanations.

scholarly journals Mission Planning for Unmanned Aerial Vehicles Based on Voronoi Diagram-Tabu Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei Tan ◽  
Yong-jiang Hu ◽  
Yue-fei Zhao ◽  
Wen-guang Li ◽  
Xiao-meng Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Unmanned Aerial Vehicles ◽  
Voronoi Diagram ◽  
Pso Algorithm ◽  
Mission Planning ◽  
Cooperative System ◽  
Aerial Vehicles ◽  
Rendezvous Problem ◽  
Aerial Vehicle ◽  
Multi Uav

Unmanned aerial vehicles (UAVs) are increasingly used in different military missions. In this paper, we focus on the autonomous mission allocation and planning abilities for the UAV systems. Such abilities enable adaptation to more complex and dynamic mission environments. We first examine the mission planning of a single unmanned aerial vehicle. Based on that, we then investigate the multi-UAV cooperative system under the mission background of cooperative target destruction and show that it is a many-to-one rendezvous problem. A heterogeneous UAV cooperative mission planning model is then proposed where the mission background is generated based on the Voronoi diagram. We then adopt the tabu genetic algorithm (TGA) to obtain multi-UAV mission planning. The simulation results show that the single-UAV and multi-UAV mission planning can be effectively realized by the Voronoi diagram-TGA (V-TGA). It is also shown that the proposed algorithm improves the performance by 3% in comparison with the Voronoi diagram-particle swarm optimization (V-PSO) algorithm.

scholarly journals Smart Search System of Autonomous Flight UAVs for Disaster Rescue

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6810
Author(s):  
Donggeun Oh ◽  
Junghee Han
Keyword(s):  
Genetic Algorithm ◽  
Unmanned Aerial Vehicles ◽  
Base Stations ◽  
Localization Algorithm ◽  
Tracking Accuracy ◽  
Long Distance ◽  
Search System ◽  
Autonomous Flight ◽  
Shadow Area ◽  
Aerial Vehicles

UAVs (Unmanned Aerial Vehicles) have been developed and adopted for various fields including military, IT, agriculture, construction, and so on. In particular, UAVs are being heavily used in the field of disaster relief thanks to the fact that UAVs are becoming smaller and more intelligent. Search for a person in a disaster site can be difficult if the mobile communication network is not available, and if the person is in the GPS shadow area. Recently, the search for survivors using unmanned aerial vehicles has been studied, but there are several problems as the search is mainly using images taken with cameras (including thermal imaging cameras). For example, it is difficult to distinguish a distressed person from a long distance especially in the presence of cover. Considering these challenges, we proposed an autonomous UAV smart search system that can complete their missions without interference in search and tracking of castaways even in disaster areas where communication with base stations is likely to be lost. To achieve this goal, we first make UAVs perform autonomous flight with locating and approaching the distressed people without the help of the ground control server (GCS). Second, to locate a survivor accurately, we developed a genetic-based localization algorithm by detecting changes in the signal strength between distress and drones inside the search system. Specifically, we modeled our target platform with a genetic algorithm and we re-defined the genetic algorithm customized to the disaster site’s environment for tracking accuracy. Finally, we verified the proposed search system in several real-world sites and found that it successfully located targets with autonomous flight.

scholarly journals Control System Architecture for Automatic Recovery of Fixed-Wing Unmanned Aerial Vehicles in a Moving Arrest System

2021 ◽  
Vol 103 (4) ◽  
Author(s):  
Kristoffer Gryte ◽  
Martin L. Sollie ◽  
Tor Arne Johansen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Low Cost ◽  
Field Testing ◽  
Satellite System ◽  
Base Station ◽  
Research Literature ◽  
Aerial Vehicles ◽  
Global Navigation Satellite ◽  
The Right ◽  
Automatic Recovery

AbstractAutomatic recovery is an important step in enabling fully autonomous missions using fixed-wing unmanned aerial vehicles (UAVs) operating from ships or other moving platforms. However, automatic recovery in moving arrest systems is only briefly studied in the research literature, and is not yet an option when using low-cost, commercial off-the-shelf (COTS) autopilots. Acknowledging the reliability and low cost of COTS avionics, this paper adds recovery functionality as a modular extension based on non-intrusive additions to an autopilot with very general assumptions on its interface. This is achieved by line-of-sight guidance, which sends an augmented desired position to the autopilot, to ensure line-following along a virtual runway that guides the UAV into the arrest system. The translation and rotation of this line is determined by the pose of the arrest system, determined using two Global Navigation Satellite System (GNSS) receivers, where one is configured as a Real-Time Kinematic (RTK) base station. The relative position of the UAV and arrest system is also precisely estimated using RTK GNSS. Through extensive field testing, on two different fixed-wing UAVs, the system has shown its performance and reliability; 43 recovery attempts in a stationary net hit 0.01 ± 0.25m to the right and 0.07 ± 0.20m below the target in calm conditions. Further, 15 recoveries in a barge-mounted, ship-towed net hit 0.06 ± 0.53m to the right and 0.98 ± 0.27m below the target in winds up to 4 m/s. The remaining error is largely systematic, caused by communication delays, and could be reduced with more integral effect or through direct compensation.

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