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| Author | : |
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| Total Pages | : 10 |
| Release | : 2006 |
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This paper compares two algorithms applied to the task allocation of multiple Unmanned Aerial Vehicles (UAVs) for an electronic warfare mission. The electronic warfare mission scenario is discussed and a review of both the genetic algorithm and simulated annealing algorithm is given. The encoding of the problem and the functions and operations needed to implement each algorithm is outlined and compared. The algorithms were implemented and tested in Matlab. A discussion of the performance analysis for the time to convergence and quality of solutions in a fixed period of time is given.
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| Total Pages | : 10 |
| Release | : 2006 |
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This paper explores the parallelization of a Genetic Algorithm (GA) utilized for task assignment of a team of Unmanned Air Vehicles conducting a Suppression of Enemy Air Defense mission. The GA has been developed and implemented in the Multi-UAV simulation environment for testing. The algorithm has been parallelized with each UAV acting as an independent processor. Two different implementations are explored, one where each UAV independently runs a GA, and the best overall solution is selected at the end, and one where the UAVs exchange information several times during the evolution of generations. The results of these implementations are compared to the original, non-parallelized GA performance.
| Author | : Yasmina Bestaoui Sebbane |
| Publisher | : CRC Press |
| Total Pages | : 344 |
| Release | : 2020-03-27 |
| Genre | : Computers |
| ISBN | : 1000049981 |
Multi-robot systems are a major research topic in robotics. Designing, testing, and deploying aerial robots in the real world is a possibility due to recent technological advances. This book explores different aspects of cooperation in multiagent systems. It covers the team approach as well as deterministic decision-making. It also presents distributed receding horizon control, as well as conflict resolution, artificial potentials, and symbolic planning. The book also covers association with limited communications, as well as genetic algorithms and game theory reasoning. Multiagent decision-making and algorithms for optimal planning are also covered along with case studies. Key features: Provides a comprehensive introduction to multi-robot systems planning and task allocation Explores multi-robot aerial planning; flight planning; orienteering and coverage; and deployment, patrolling, and foraging Includes real-world case studies Treats different aspects of cooperation in multiagent systems Both scientists and practitioners in the field of robotics will find this text valuable.
| Author | : Tal Shima |
| Publisher | : SIAM |
| Total Pages | : 180 |
| Release | : 2009-01-01 |
| Genre | : Mathematics |
| ISBN | : 0898718589 |
Unmanned aerial vehicles (UAVs) are increasingly used in military missions because they have the advantages of not placing human life at risk and of lowering operation costs via decreased vehicle weight. These benefits can be fully realized only if UAVs work cooperatively in groups with an efficient exchange of information. This book provides an authoritative reference on cooperative decision and control of UAVs and the means available to solve problems involving them.
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| Total Pages | : 7 |
| Release | : 2005 |
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A multiple task assignment problem for cooperating uninhabited aerial vehicles is posed as a combinatorial optimization problem. A genetic algorithm for assigning the multiple agents to perform multiple tasks on multiple targets is proposed. The algorithm allows efficiently solving this NP- hard problem that has prohibitive computational complexity for classical combinatorial optimization methods. It also allows taking into account the unique requirements of the scenario such as task precedence and coordination, timing constraints, and flyable trajectories. The performance of the algorithm is compared to that of deterministic branch and bound search and stochastic random search methods. Monte Carlo simulations demonstrate the viability of the genetic algorithm, providing good feasible solutions quickly. Moreover, it converges near to the optimal solution considerably faster than the other methods for some test cases. This makes real time implementation for high dimensional problems feasible.
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| Total Pages | : 15 |
| Release | : 2005 |
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A problem of assigning multiple unmanned aerial vehicles (UAVs) to simultaneously perform cooperative tasks on consecutive targets is posed as a new NP-hard combinatorial optimization problem. The investigated scenario consists of multiple ground moving targets prosecuted by a team of heterogeneous UAVs carrying designated sensors and/or weapons. To successfully prosecute each target it first needs to be simultaneously tracked by multiple UAVs, from significantly different line of sight angles to reduce the position estimate errors, and then attacked by a different UAV carrying a weapon. Even for small sized scenarios, the problem has prohibitive computational complexity for classical combinatorial optimization methods due to timing constraints on the simultaneous tasks and the coupling between task assignment and path planning for each UAV. A genetic algorithm (GA) is proposed for efficiently searching the space of feasible solutions. A matrix representation of the GA chromosomes simplifies the encoding process and the application of the genetic operators. To further simplify the encoding, the chromosome is composed of sets of multiple genes, each corresponding to the entire set of assignments on each target. Simulation results conform the viability of the proposed assignment algorithm for different sized scenarios. The sensitivity of the performance to variations in GA tuning parameters is also investigated.
| Author | : Karen Haigh |
| Publisher | : Artech House |
| Total Pages | : 288 |
| Release | : 2021-07-31 |
| Genre | : Technology & Engineering |
| ISBN | : 1630818127 |
This comprehensive book gives an overview of how cognitive systems and artificial intelligence (AI) can be used in electronic warfare (EW). Readers will learn how EW systems respond more quickly and effectively to battlefield conditions where sophisticated radars and spectrum congestion put a high priority on EW systems that can characterize and classify novel waveforms, discern intent, and devise and test countermeasures. Specific techniques are covered for optimizing a cognitive EW system as well as evaluating its ability to learn new information in real time. The book presents AI for electronic support (ES), including characterization, classification, patterns of life, and intent recognition. Optimization techniques, including temporal tradeoffs and distributed optimization challenges are also discussed. The issues concerning real-time in-mission machine learning and suggests some approaches to address this important challenge are presented and described. The book covers electronic battle management, data management, and knowledge sharing. Evaluation approaches, including how to show that a machine learning system can learn how to handle novel environments, are also discussed. Written by experts with first-hand experience in AI-based EW, this is the first book on in-mission real-time learning and optimization.
| Author | : Yasmina Bestaoui Sebbane |
| Publisher | : CRC Press |
| Total Pages | : 396 |
| Release | : 2018-03-14 |
| Genre | : Computers |
| ISBN | : 1351339397 |
Intelligent Autonomy of UAVs: Advanced Missions and Future Use provides an approach to the formulation of the fundamental task typical to any mission and provides guidelines of how this task can be solved by different generic robotic problems. As such, this book aims to provide a systems engineering approach to UAV projects, discovering the real problems that need to be resolved independently of the application. After an introduction to the rapidly evolving field of aerial robotics, the book presents topics such as autonomy, mission analysis, human-UAV teams, homogeneous and heterogeneous UAV teams, and finally, UAV-UGV teams. It then covers generic robotic problems such as orienteering and coverage. The book next introduces deployment, patrolling, and foraging, while the last part of the book tackles an important application: aerial search, tracking, and surveillance. This book is meant for both scientists and practitioners. For practitioners, it presents existing solutions that are categorized according to various missions: surveillance and reconnaissance, 3D mapping, urban monitoring, precision agriculture, forestry, disaster assessment and monitoring, security, industrial plant inspection, etc. For scientists, it provides an overview of generic robotic problems such as coverage and orienteering; deployment, patrolling and foraging; search, tracking, and surveillance. The design and analysis of algorithms raise a unique combination of questions from many fields, including robotics, operational research, control theory, and computer science.
| Author | : |
| Publisher | : Jeffrey Frank Jones |
| Total Pages | : 3840 |
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Over 3,800 total pages ... Just a sample of the studies / publications included: Drone Swarms Terrorist and Insurgent Unmanned Aerial Vehicles: Use, Potentials, and Military Implications Countering A2/AD with Swarming Stunning Swarms: An Airpower Alternative to Collateral Damage Ideal Directed-Energy System To Defeat Small Unmanned Aircraft System Swarms Break the Kill Chain, not the Budget: How to Avoid U.S. Strategic Retrenchment Gyges Effect: An Ethical Critique of Lethal Remotely Piloted Aircraft Human Robotic Swarm Interaction Using an Artificial Physics Approach Swarming UAS II Swarming Unmanned Aircraft Systems Communication Free Robot Swarming UAV Swarm Attack: Protection System Alternatives for Destroyers Confidential and Authenticated Communications in a Large Fixed-Wing UAV Swarm UAV Swarm Behavior Modeling for Early Exposure of Failure Modes Optimized Landing of Autonomous Unmanned Aerial Vehicle Swarms Mini, Micro, and Swarming Unmanned Aerial Vehicles: A Baseline Study UAV Swarm Operational Risk Assessment System SmartSwarms: Distributed UAVs that Think Command and Control Autonomous UxV's UAV Swarm Tactics: An Agent-Based Simulation and Markov Process Analysis A Novel Communications Protocol Using Geographic Routing for Swarming UAVs Performing a Search Mission Accelerating the Kill Chain via Future Unmanned Aircraft Evolution of Control Programs for a Swarm of Autonomous Unmanned Aerial Vehicles AFIT UAV Swarm Mission Planning and Simulation System A Genetic Algorithm for UAV Routing Integrated with a Parallel Swarm Simulation Applying Cooperative Localization to Swarm UAVS Using an Extended Kalman Filter A Secure Group Communication Architecture for a Swarm of Autonomous Unmanned Aerial Vehicles Braving the Swarm: Lowering Anticipated Group Bias in Integrated Fire/Police Units Facing Paramilitary Terrorism Distributed Beamforming in a Swarm UAV Network Integrating UAS Flocking Operations with Formation Drag Reduction Tracking with a Cooperatively Controlled Swarm of GMTI Equipped UAVS Using Agent-Based Modeling to Evaluate UAS Behaviors in a Target-Rich Environment Experimental Analysis of Integration of Tactical Unmanned Aerial Vehicles and Naval Special Warfare Operations Forces Target Acquisition Involving Multiple Unmanned Air Vehicles: Interfaces for Small Unmanned Air Systems (ISUS) Program Tools for the Conceptual Design and Engineering Analysis of Micro Air Vehicles Architectural Considerations for Single Operator Management of Multiple Unmanned Aerial Vehicles
| Author | : Samantha Raye Rennu |
| Publisher | : |
| Total Pages | : 71 |
| Release | : 2020 |
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The purpose of this thesis is to produce a closed-loop feedback mission planning tool that allows for the operator to control multiple Unmanned Aerial Vehicles (UAV) within a mission. Different styles of UAVs and mission planners that are available on the market were evaluated and selected for their cost, size, ability to customize, and fit for mission work. It was determined that commercially available mission planners do not provide the level of automation required, such as allowing for different algorithms for assigning UAV tasks and for planning UAV flight paths within a mission. Comparisons were made between different algorithms for path planning and tasking. From these comparisons, a bio-inspired machine-learning algorithm, Genetic Algorithm (GA), was chosen for assigning tasks to UAVs and Dubins path was chosen for modeling UAV flight paths within the mission simulation. Since market mission planners didn't allow for control of multiple UAVs, or wouldn't allow for the operator to add algorithms to increase usability and automation of the program, it was decided to create a Graphic User Interface (GUI) that would allow the operator to customize UAVs and the mission scenario. A test mission scenario was then designed, which included 9 Points of Interest (POI), 1 to 3 Targets of Interest (TOI), 3 to 5 UAVs, as well as simulation options that modeled failure of a task or a UAV crash. Operator feedback was incorporated into the simulation by allowing the operator to determine a course of action if a failure occurred, such as reprogramming the other UAVs to complete the tasks left by the crashed UAV or reassessing a failed task. Overall mission times decreased for reprogramming the UAVs versus running a separate mission to complete any tasks left by the crashed UAV. Additional code was added to the GA and Dubins path to increase speed without decreasing solution fitness.
