Military Unmanned Vehicles Research Articles

PKI-SMR: PKI based secure multipath routing for unmanned military vehicles (UMV) in VANETs

PKI-SMR: PKI based secure multipath routing for unmanned military vehicles (UMV) in VANETs

Weight-Based Clustering Algorithm for Military Vehicles Communication in VANET

In vehicular ad-hoc network (VANET), every vehicle node indicates a mobile node and it acts as a transmitter, receiver and router for the delivery of the information. VANET is a subgroup of mobile ad-hoc network (MANET) and is related to the dynamic topology. Dynamic network scenarios are more challenging issues as compared to MANET topologies, so finding a suitable algorithm for all VANET applications is the major challenge for the researchers. Routing protocols in VANET are divided into six parts i.e., cluster-based, geocast-based, topology-based, position-based, and broadcast-based. Autonomous robots and unmanned military vehicles (UMVs) become part of the advanced warfare strategy to execute dangerous war field operations and military combat missions. The military vehicles (MVs) transfer information to each other in order to achieve required military tasks collectively. In the proposed work, rhombus shaped area is divided into multiple clusters using a weight-based clustering algorithm for transmitting the event information to the vehicles. Intersection clustering with rhombus shaped area which are very effective for clustering. To choose cluster head (CH), the proposed method has used two weighted metrics, one is real time average speed and the other parameter is degree. This work is useful for choosing right CH in the network. Each vehicle in the same cluster transmits the data to the CH instead of broadcasting it. The simulation has been done in the SUMO and NETSIM simulator, which shows the network performance for the different protocols like Ad-hoc on-demand distance vector (AODV), dynamic source routing (DSR) in terms of packet delivery ratio, throughput, delay, overhead transmission, mean and standard deviation.

An observation algorithm for key motion states of skid-steered wheeled unmanned vehicle

With the increasing demand of military and civilian in the intelligent vehicles, the skid-steering theory has been widely used in unmanned ground vehicles, especially in unmanned military vehicles and unmanned surveillance platforms. Due to its driving environment complex and variable, which requires stricter dynamic control system. In order to improve the active safety performance of the skid-steering unmanned vehicle and develop the key technologies such as behavior decision planning technology, path tracking, and dynamic control technology, it is necessary to develop the dynamic state parameter observation system based on skid-steering theory. In this paper, an observation using Strong Track External Kalman Filter theory with noise matrix adaptive is designed to estimate vehicle kinematic parameters based on a 6 × 6 skid-steered unmanned vehicle. First, kinematic and dynamic model is built to analyze the characters of a skid-steered wheeled vehicle. Then a tire force estimation method based on dynamic model is presented to observe the tire longitude and vertical force. The tire force data is also used by Dugoff nonlinear model. Then an External Kalman Filter theory is designed to estimate vehicle kinematic parameters. To increase the accuracy and the robustness of the observer, the Strong Tracking EKF (STEKF) and noise adaptive adjustment is designed. Finally, a combined simulation using TruckSim and Simulink and the experiment using a 6 × 6 skid-steered unmanned vehicle verifies the efficiency of the observer. Results show that the observer is able to estimate the skid-steered wheeled vehicle states, and it also shows that the yaw rate result in the slip angle difference between each tire.

Adaptive 3D Imaging for Moving Targets Based on a SIMO InISAR Imaging System in 0.2 THz Band

Terahertz (THz) imaging technology has received increased attention in recent years and has been widely applied, whereas the three-dimensional (3D) imaging for moving targets remains to be solved. In this paper, an adaptive 3D imaging scheme is proposed based on a single input and multi-output (SIMO) interferometric inverse synthetic aperture radar (InISAR) imaging system to achieve 3D images of moving targets in THz band. With a specially designed SIMO antenna array, the angular information of the targets can be determined using the phase response difference in different receiving channels, which then enables accurate tracking by adaptively adjusting the antenna beam direction. On the basis of stable tracking, the high-resolution imaging can be achieved. A combined motion compensation method is proposed to produce well-focused and coherent inverse synthetic aperture radar (ISAR) images from different channels, based on which the interferometric imaging is performed, thus forming the 3D imaging results. Lastly, proof-of-principle experiments were performed with a 0.2 THz SIMO imaging system, verifying the effectiveness of the proposed scheme. Non-cooperative moving targets were accurately tracked and the 3D images obtained clearly identify the targets. Moreover, the dynamic imaging results of the moving targets were achieved. The promising results demonstrate the superiority of the proposed scheme over the existing THz imaging systems in realizing 3D imaging for moving targets. The proposed scheme shows great potential in detecting and monitoring moving targets with non-cooperative movement, including unmanned military vehicles and space debris.

Беспилотная техника для целей лесного комплекса и мониторинга состояния водных объектов

Military-industrial complexes of many countries have invested billions of dollars in the industry over 120 years of development and have achieved significant progresses in the creation of surface, water and underwater equipment. Groups of military unmanned vehicles include unmanned aerial vehicles (UAVs), deck-based UAVs, water, underwater and hybrid unmanned vehicles (UAVs). Their purpose is intelligence, surveillance, strategic research projects, and military transport purposes. As for the civilian use of unmanned vehicles, this is a young industry, it is 15 years old. The beginning of the era of non-military unmanned aerial vehicles (UAVs) and then unmanned floating vehicles (UAVs) is considered to be 2006, when the US Federal Aviation Administration allowed the flights of small custom drones. At the same time, they were given a definition: a device that is remotely controlled by an operator; a device that moves along a pre-planned route. Unmanned vehicles the use of which for the purposes of the forest complex of our country is carried out or is planned to begin in the near future must meet some special requirements that are discussed in this article.

The modelling and control of the drive system of an Ackermann Robot using GA optimization

This paper provides the mathematical modelling and control optimization, of the drive system of an Ackermann four wheeled autonomous robot, with Genetic algorithm used for tuning the proportional, integral and derivative (PID) Controller parameters. The aim and main objective of this work is focused on the control of the driving speed input from the rear wheels of the robot and control. The robot drive in proportion to obstacle input ahead of the four wheeled chassis using genetic algorithms. A controlled platform that can be deployed for driverless vehicle in the nearest future and military unmanned vehicle is our major concern. The controlled system response stabilized in 0.675 seconds, after exciting the system with a step response. Variation for the system also shows, that the cost function was minimized or adjusted to obtain optimal PID parameters as Proportional (P) = 12.671, Integral (I) = -0.399, Derivative (D) = 1477561, at a value of 9.6778*10-4.Keywords: Ackermann steering, modelling, optimization, Genetic Algorithm, control, PID, driverless vehicle

A Study on the Haptic Control Technology for Unmanned Military Vehicle Driving Control

A Study on the Haptic Control Technology for Unmanned Military Vehicle Driving Control

확장된 페트리네트를 이용한 차량형 군사로봇의 운용자 성능 및 통신장애 영향분석

Unmanned military vehicles (UMVs) are most commonly characterized as dealing with dull, dirty, and dangerous tasks with automation. Although most of the UMVs are designed to a high degree of autonomy, the human operator will still intervene in the robot’s operation, and teleoperate them to achieve his or her mission. Thus, operator capacity, together with robot autonomy and user interface, is one of the most important design factors in the research and development of the UMVs. Further, communication may affect the operator task performance. In this paper, we analyze the operator performance and the communication effects on the operator performance by using the extended Petri nets, called OTSim nets. The OTSim nets was designed by the authors, being extended using pure Petri nets.

Operator Capacity Assessment Method for the Supervisory Control of Unmanned Military Vehicle

Operator Capacity Assessment Method for the Supervisory Control of Unmanned Military Vehicle

Trust-based coalition formation in multi-agent systems

In this paper, we provide a framework to study trust-based coalition formation in multi-agent systems using cooperative game theory as the underlying mathematical framework. We describe how to study trust dynamics between agents as a result of their trust synergy and trust liability in cooperative coalitions. We also rigorously justify the behaviors of agents for different classes of games and discuss how to exploit the formal properties of these games for cooperative control in an unmanned military vehicle convoy.

UVs, Network-centric Operations, and the Challenge for Arms Control

The development of unmanned aerial vehicles (UAVs) and other unmanned vehicles (UVs) goes back at least a hundred years. During the Cold War they were used primarily for reconnaissance missions over heavily defended territories. In more recent times modern armed forces have begun to arm UVs and there is now the prospect that conventional wars (and not just nuclear wars) could be fought by remote control across continents. Defensive and reconnaissance roles for UVs seem to be far less ethically and legally problematic than the new offensive roles that UVs are gradually taking over. The growing autonomy of armed UVs is a particular concern as it raises the spectre of Terminators that can decide by themselves when to attack and what target to engage. As pointed out in the pr cis article, although we are still a long way from fully autonomous UVs, there are good reasons to assume that full autonomy for military UVs could materialise in the future because of rapid technological progress and because of considerations relating to military effectiveness. In the absence of relevant laws for restricting military UVs there could be serious negative consequences, such as increased dangers for civilians as a result of out of control robots, an increased propensity towards the use of force as a result of a combination of low political costs and a 'Playstation' mentality that makes killing too easy, and even the increased danger of accidental war triggered by automated defensive systems. At the same time, Brendan Gogarty and Meredith Hagger expressed hope that '[t]here is still a chance to at least shape the way UVs are used and how far they proliferate within militaries and beyond.'

Color machine vision for autonomous vehicles

Color can be a useful feature in autonomous vehicle systems that are based on machine vision, for tasks such as obstacle detection, lane/road following, and recognition of miscellaneous scene objects. Unfortunately, few existing autonomous vehicle systems use color to its full extent, largely because color-based recognition in outdoor scenes is complicated, and existing color machine-vision techniques have not been shown to be effective in realistic outdoor images. This paper presents a technique for achieving effective real-time color recognition in outdoor scenes. The technique uses multivariate decision trees for piecewise linear non-parametric function approximation to learn the color of a target object from training samples, and then detects targets by classifying pixels based on the approximated function. The method has been successfully tested in several domains, such as autonomous highway navigation, off-road navigation and target detection for unmanned military vehicles, in projects such as the U.S. National Automated Highway System (AHS) and the U.S. Defense Advanced Project Agency — Unmanned Ground Vehicle (DARPA-UGV). MDT-based systems have been used in stand-alone mode, as well as in conjunction with systems based on other sensor configurations.