Identification of Military-related Science and Technology

A proof-of-principle demonstration for extracting military-related technologies from a country's total technology publications has been performed, and applied to the Indian science and technology literature#. The method is general and can be applied to the extraction of any meta-category (e.g., intelligence-relevanttechnologies, infrastructure-relevant technologies, etc) which is not easily obtained from document clustering or factor analysis. The methodology for identifying relevant literature on military science appears to provide credible results. The volume of literature retrieved will vary depending on how strongly relevant is the desired literature. For the same definitions of 'military relevant', the volume of India's literature in the Ei Compendex database was an order of magnitude less than that of the USA or China.Defence Science Journal, 2010, 60(3), pp.259-270, DOI:http://dx.doi.org/10.14429/dsj.60.35

Learning Multi-Pursuit Evasion for Safe Targeted Navigation of Drones

Safe navigation of drones in the presence of adversarial physical attacks from multiple pursuers is a challenging task. This paper proposes a novel approach, asynchronous multi-stage deep reinforcement learning (AMS-DRL), to train adversarial neural networks that can learn from the actions of multiple evolved pursuers and adapt quickly to their behavior, enabling the drone to avoid attacks and reach its target. Specifically, AMS-DRL evolves adversarial agents in a pursuit-evasion game where the pursuers and the evader are asynchronously trained in a bipartite graph way during multiple stages. Our approach guarantees convergence by ensuring Nash equilibrium among agents from the game-theory analysis. We evaluate our method in extensive simulations and show that it outperforms baselines with higher navigation success rates. We also analyze how parameters such as the relative maximum speed affect navigation performance. Furthermore, we have conducted physical experiments and validated the effectiveness of the trained policies in real-time flights. A success rate heatmap is introduced to elucidate how spatial geometry influences navigation outcomes. Project website: https://github.com/NTU-ICG/AMS-DRL-for-Pursuit-Evasion.Comment: Accepted by IEEE Transactions on Artificial Intelligenc

Military airborne and maritime application for cooperative behaviors.

As part of DARPA's Software for Distributed Robotics Program within the Information Processing Technologies Office (IPTO), Sandia National Laboratories was tasked with identifying military airborne and maritime missions that require cooperative behaviors as well as identifying generic collective behaviors and performance metrics for these missions. This report documents this study. A prioritized list of general military missions applicable to land, air, and sea has been identified. From the top eight missions, nine generic reusable cooperative behaviors have been defined. A common mathematical framework for cooperative controls has been developed and applied to several of the behaviors. The framework is based on optimization principles and has provably convergent properties. A three-step optimization process is used to develop the decentralized control law that minimizes the behavior's performance index. A connective stability analysis is then performed to determine constraints on the communication sample period and the local control gains. Finally, the communication sample period for four different network protocols is evaluated based on the network graph, which changes throughout the task. Using this mathematical framework, two metrics for evaluating these behaviors are defined. The first metric is the residual error in the global performance index that is used to create the behavior. The second metric is communication sample period between robots, which affects the overall time required for the behavior to reach its goal state

Design and Development of an Automated Mobile Manipulator for Industrial Applications

This thesis presents the modeling, control and coordination of an automated mobile manipulator. A mobile manipulator in this investigation consists of a robotic manipulator and a mobile platform resulting in a hybrid mechanism that includes a mobile platform for locomotion and a manipulator arm for manipulation. The structural complexity of a mobile manipulator is the main challenging issue because it includes several problems like adapting a manipulator and a redundancy mobile platform at non-holonomic constraints. The objective of the thesis is to fabricate an automated mobile manipulator and develop control algorithms that effectively coordinate the arm manipulation and mobility of mobile platform. The research work starts with deriving the motion equations of mobile manipulators. The derivation introduced here makes use of motion equations of robot manipulators and mobile platforms separately, and then integrated them as one entity. The kinematic analysis is performed in two ways namely forward & inverse kinematics. The motion analysis is performed for various WMPs such as, Omnidirectional WMP, Differential three WMP, Three wheeled omni-steer WMP, Tricycle WMP and Two steer WMP. From the obtained motion analysis results, Differential three WMP is chosen as the mobile platform for the developed mobile manipulator. Later motion analysis is carried out for 4-axis articulated arm. Danvit-Hartenberg representation is implemented to perform forward kinematic analysis. Because of this representation, one can easily understand the kinematic equation for a robotic arm. From the obtained arm equation, Inverse kinematic model for the 4-axis robotic manipulator is developed. Motion planning of an intelligent mobile robot is one of the most vital issues in the field of robotics, which includes the generation of optimal collision free trajectories within its work space and finally reaches its target position. For solving this problem, two evolutionary algorithms namely Particle Swarm Optimization (PSO) and Artificial Immune System (AIS) are introduced to move the mobile platform in intelligent manner. The developed algorithms are effective in avoiding obstacles, trap situations and generating optimal paths within its unknown environments. Once the robot reaches its goal (within the work space of the manipulator), the manipulator will generate its trajectories according to task assigned by the user. Simulation analyses are performed using MATLAB-2010 in order to validate the feasibility of the developed methodologies in various unknown environments. Additionally, experiments are carried out on an automated mobile manipulator. ATmega16 Microcontrollers are used to enable the entire robot system movement in desired trajectories by means of robot interface application program. The control program is developed in robot software (Keil) to control the mobile manipulator servomotors via a serial connection through a personal computer. To support the proposed control algorithms both simulation and experimental results are presented. Moreover, validation of the developed methodologies has been made with the ER-400 mobile platform

Prospects for the development and use of Unmanned Aerial Systems

Abstract: This article presents the basic assumptions concerning the prospects for the use and development of unmanned flying systems. Data on the use of combat flying platforms are presented. At the same time, the directions of development and use of civil and combat air systems in tasks for the benefit of the national economy and future armed conflicts were presented. It was indicated that unmanned aerial systems, the use of which brings significant financial benefits, are a type of weapon that changes the approach of UAV users in the most spectacular way, transforming the face of their civil and military application. The use of unmanned aerial vehicles as part of combat tasks and missions forces continuous development personnel in terms of the ability to perform complex tasks related to both civil and military use. At the same time influencing the change of tactics of using unmanned combat systems. On the basis of analyzes and comparisons, the author also proposes that the development work and construction of elements and systems for unmanned aerial platforms become an opportunity for the development of national research institutes and the defense industry, which should consequently be the main supplier of basic types of modern weapons.. Keywords: Unmanned aerial vehicle; UAV; Quadrocopter; Drone; Multisensor technologies; AI; UAV; Stealt

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Artificial intelligence and game theory controlled autonomous UAV swarms

Autonomous unmanned aerial vehicles (UAVs) operating as a swarm can be deployed in austere environments, where cyber electromagnetic activities often require speedy and dynamic adjustments to swarm operations. Use of central controllers, UAV synchronization mechanisms or pre-planned set of actions to control a swarm in such deployments would hinder its ability to deliver expected services. We introduce artificial intelligence and game theory based flight control algorithms to be run by each autonomous UAV to determine its actions in near real-time, while relying only on local spatial, temporal and electromagnetic (EM) information. Each UAV using our flight control algorithms positions itself such that the swarm main-tains mobile ad-hoc network (MANET) connectivity and uniform asset distribution over an area of interest. Typical tasks for swarms using our algorithms include detection, localization and tracking of mobile EM transmitters. We present a formal analysis showing that our algorithms can guide a swarm to maintain a connected MANET, promote a uniform network spread-ing, while avoiding overcrowding with other swarm members. We also prove that they maintain MANET connectivity and, at the same time, they can lead a swarm of autonomous UAVs to follow or avoid an EM transmitter. Simulation experiments in OPNET modeler verify the results of formal analysis that our algorithms are capable of providing an adequate area coverage over a mobile EM source and maintain MANET connectivity. These algorithms are good candidates for civilian and military applications that require agile responses to the changes in dynamic environments for tasks such as detection, localization and tracking mobile EM transmitters

Machine learning for advancing low-temperature plasma modeling and simulation

Machine learning has had an enormous impact in many scientific disciplines. Also in the field of low-temperature plasma modeling and simulation it has attracted significant interest within the past years. Whereas its application should be carefully assessed in general, many aspects of plasma modeling and simulation have benefited substantially from recent developments within the field of machine learning and data-driven modeling. In this survey, we approach two main objectives: (a) We review the state-of-the-art focusing on approaches to low-temperature plasma modeling and simulation. By dividing our survey into plasma physics, plasma chemistry, plasma-surface interactions, and plasma process control, we aim to extensively discuss relevant examples from literature. (b) We provide a perspective of potential advances to plasma science and technology. We specifically elaborate on advances possibly enabled by adaptation from other scientific disciplines. We argue that not only the known unknowns, but also unknown unknowns may be discovered due to the inherent propensity of data-driven methods to spotlight hidden patterns in data