A Process Algebra Genetic Algorithm

A genetic algorithm that utilizes process algebra for coding of solution chromosomes and for defining evolutionary based operators is presented. The algorithm is applicable to mission planning and optimization problems. As an example the high level mission planning for a cooperative group of uninhabited aerial vehicles is investigated. The mission planning problem is cast as an assignment problem, and solutions to the assignment problem are given in the form of chromosomes that are manipulated by evolutionary operators. The evolutionary operators of crossover and mutation are formally defined using the process algebra methodology, along with specific algorithms needed for their execution. The viability of the approach is investigated using simulations and the effectiveness of the algorithm is shown in small, medium, and large scale problems.United States. Air Force Office of Scientific Research (Michigan/AFRL Collaborative Center in Control Science Grant FA 8650-07-2-3744)United States. Air Force Office of Scientific Research (Grant FA8655-09-1-3066

동시도달을 고려한 복수 무인기 임무할당 기법

학위논문 (박사)-- 서울대학교 대학원 공과대학 기계항공공학부, 2017. 8. 김유단.무인항공기의 자율비행 기술이 성숙함에 따라 무인항공기에 요구되는 임무의 복잡도와 정밀도가 증가하고 있다. 최근에는 단일 무인항공기에 의한 감시정찰 임무에서 나아가 다수의 무인항공기의 협력적인 임무수행 능력에 관한 연구가 활발히 수행되고 있다. 본 연구에서는 무인항공기의 협업에 의한 잠재력을 최대한으로 활용하기 위하여 다수의 무인항공기가 동시에 수행해야 하는 임무를 고려하였다. 이러한 임무로는 위험도가 높은 방어 시스템을 동시에 공격하는 임무, 넓은 재난지역을 다수의 무인기가 동시에 수색, 물품지원, 구조 등을 수행하는 임무, 그리고 무거운 물체를 다수의 무인항공기가 협력하여 수송하는 임무 등을 고려할 수 있다. 이와 같이 복잡한 임무를 관리하기 위해 지상 조종사는 다수의 무인항공기를 관제하여야 하며, 이 과정에서 과도한 업무부하는 조종사 실수를 유발하여 임무수행 효율저하로 이어질 수 있다. 본 연구에서는 다수 무인항공기의 동시도달을 고려한 협력 임무할당 문제를 정수계획법으로 정식화하고, 중앙집중형 임무할당 방식과 분산형 임무할당 방식을 연구하였다. 무인항공기로부터 수집된 정보를 기반으로 최적에 가까운 임무할당을 결정하는 중앙집중형 임무할당 방식으로는 모든 해 공간을 탐색하여 최적해를 계산하는 방식, 경험적인 법칙을 통해 신속하게 해를 결정하는 방식, 그리고 메타 휴리스틱 기법의 일종인 군집 최적화 기법을 활용하는 방식을 제안하였다. 분산형 임무할당 방식으로는 개별 무인항공기는 모든 무인항공기가 아닌 이웃 무인항공기들과만 정보를 교류하고, 이를 통하여 자율적으로 임무를 할당하는 기법을 제안하였다. 제한된 통신반경에 따른 실시간 네트워크 위상변화 상황을 고려하기 위하여 집결지 개념을 도입하였으며, 연결된 네트워크 상황에 대하여 수렴성과 확장성을 분석하였다. 제안한 기법들의 성능을 검증하기 위하여 적 대공망 제압작전 시나리오에 대한 수치 시뮬레이션을 수행하고, 제안한 기법 간의 성능을 비교 분석하였다.With increasing demand for unmanned aerial vehicles (UAVs) in military and civilian areas, coordination of multiple UAVs is expected to play a key role in complex missions. As the number of agents and tasks increases, however, a greater burden is imposed on ground operators, which may cause safety issues and performance degradation accomplishing the mission. In particular, the operation requiring temporal and spatial cooperation by UAVs is significantly difficult. This dissertation proposes autonomous task allocation algorithms for cooperative timing missions with simultaneous spatial/temporal involvement of multiple agents. After formulating the task allocation problem into integer programming problems in view of UAVs and tasks, centralized and distributed algorithms are proposed. In the centralized approach, an algorithm to find an optimal solution that minimizes the time to complete all the missions is introduced. Since the exact algorithm is time intensive, heuristic algorithms working in a greedy manner are proposed. A metaheuristic approach is also considered to find a near-optimal solution within a feasible duration. In the distributed approach, market-based task allocation algorithms are designed. The mathematical convergence and scalability analyses show that the proposed algorithms have a polynomial time complexity. The baseline algorithms for a connected network are then extended to address time-varying network topology including isolated sub-networks due to a limited communication range. The performance of the proposed algorithms is demonstrated via Monte Carlo simulations for a scenario involving the suppression of enemy air defenses.Chapter 1 Introduction 1 1.1 Motivation and Objective 1 1.2 Literature Survey 3 1.2.1 Vehicle Routing Problem 3 1.2.2 Centralized and Distributed Control 4 1.2.3 Centralized Control: Optimal Coalition Formation Problem 5 1.2.4 Distributed Control 8 1.3 Research Contribution 10 1.3.1 Systematic Problem Formulation 10 1.3.2 Design of a Centralized TA Algorithm for a Cooperative Timing Mission 11 1.3.3 Design of a Distributed TA Algorithm for a Cooperative Timing Mission 11 1.4 Dissertation Organization 12 Chapter 2 Problem Statement 13 2.1 Assumptions 13 2.2 Agent-based Formulation 15 2.3 Task-based Formulation 19 2.4 Simplified Form of Task-based Formulation 21 Chapter 3 Centralized Task Allocation 23 3.1 Assumptions 23 3.2 Exact Algorithm 24 3.3 Agent-based Sequential Greedy Algorithm: A-SGA 26 3.4 Task-based Sequential Greedy Algorithm: T-SGA 28 3.5 Agent-based Particle Swarm Optimization: A-PSO 30 3.5.1 Preliminaries on PSO 30 3.5.2 Particle Encoding 33 3.5.3 Particle Refinement 33 3.5.4 Score Calculation Considering DAG Constraint 34 3.6 Task-based Particle Swarm Optimization: T-PSO 38 3.6.1 Particle Encoding 38 3.6.2 Particle Refinement 39 3.7 Numerical Results 41 Chapter 4 Distributed Task Allocation 49 4.1 Assumptions 50 4.2 Project Manager-oriented Coalition Formation Algorithm : PCFA 51 4.3 Task-oriented Coalition Formation Algorithm: TCFA 63 4.4 Modified Greedy Distributed Allocation Protocol: Modified GDAP 68 4.5 Properties 71 4.5.1 Convergence 71 4.5.2 Scalability 72 4.5.3 Performance 75 4.5.4 Comparison with GDAP 76 4.6 TA Algorithm in Dynamic Environment 79 4.6.1 Challenges in Dynamic Environment 79 4.6.2 Assumptions 79 4.6.3 Distributed TA Architecture in Dynamic Environment 80 4.6.4 Rally Point 85 4.6.5 Convergence 87 4.6.6 Deletion of Duplicated Allocation 87 4.7 Numerical Results 88 4.7.1 Scalability 88 4.7.2 Application: SEAD Scenario 94 4.7.3 Discussion 106 Chapter 5 Conclusions 107 5.1 Concluding Remarks 107 5.1.1 Problem Statement 107 5.1.2 Centralized Task Allocation 107 5.1.3 Distributed Task Allocation 108 5.2 Future Research 110 Abstract (in Korean) 125Docto

Design of an UAV swarm

This master thesis tries to give an overview on the general aspects involved in the design of an UAV swarm. UAV swarms are continuoulsy gaining popularity amongst researchers and UAV manufacturers, since they allow greater success rates in task accomplishing with reduced times. Appart from this, multiple UAVs cooperating between them opens a new field of missions that can only be carried in this way. All the topics explained within this master thesis will explain all the agents involved in the design of an UAV swarm, from the communication protocols between them, navigation and trajectory analysis and task allocation

Decentralized control for UAV path planning and task allocation

The effort of this research is to move toward enabling Unmanned Air Vehicles to fly in autonomous formations with intelligent mission planning capabilities. In particular, UAVs will be able to autonomously perform path planning and task allocation. During missions, the UAVs must be able to avoid threats and no-fly zones while still reaching their target optimally in time.;A path planning and task allocation approach was first developed that treats the problem as a Multi-dimensional, Multiple-Choice Knapsack Problem. Paths are selected and task assigned while minimizing the UAV team\u27s overall mission cost. Next, a SIMULINK-based centralized simulation environment was created. This simulation uses the path planning and task allocation scheme previously developed, and adds time-varying, dynamic environment aspects. The latter part of the research effort was focused on development of a decentralized simulation environment. This decentralized version includes a vehicle\u27s own decision making capabilities and communication amongst a team of vehicles. (Abstract shortened by UMI.)

Swarm intelligence: novel tools for optimization, feature extraction, and multi-agent system modeling

Abstract Animal swarms in nature are able to adapt to dynamic changes in their envi-ronment, and through cooperation they can solve problems that are crucial for their survival. Only by means of local interactions with other members of the swarm and with the environment, they can achieve a common goal more efficiently than it would be done by a single individual. This problem-solving behavior that results from the multiplicity of such interactions is referred to as Swarm Intelligence. The mathematical models of swarming behavior in nature were initially proposed to solve optimization problems. Nevertheless, this decentralized approach can be a valuable tool for a variety of applications, where emerging global patterns represent a solution to the task at hand. Methods for the solution of difficult computational problems based on Swarm Intelligence have been experimentally demonstrated and reported in the literature. However, a general framework that would facilitate their design does not exist yet. In this dissertation, a new general design methodology for Swarm Intelligence tools is proposed. By defining a discrete space in which the members of the swarm can move, and by modifying the rules of local interactions and setting the adequate objective function for solutions evaluation, the proposed methodology is tested in various domains. The dissertation presents a set of case studies, and focuses on two general approaches. One approach is to apply Swarm Intelligence as a tool for optimization and feature extraction, and the other approach is to model multi-agent systems such that they resemble swarms of animals in nature providing them with the ability to autonomously perform a task at hand. Artificial swarms are designed to be autonomous, scalable, robust, and adaptive to the changes in their environment. In this work, the methods that exploit one or more of these features are presented. First, the proposed methodology is validated in a real-world scenario seen as a combinatorial optimization problem. Then a set of novel tools for feature extraction, more precisely the adaptive edge detection and the broken-edge linking in digital images is proposed. A novel data clustering algorithm is also proposed and applied to image segmentation. Finally, a scalable algorithm based on the proposed methodology is developed for distributed task allocation in multi-agent systems, and applied to a swarm of robots. The newly proposed general methodology provides a guideline for future developers of the Swarm Intelligence tools. Los enjambres de animales en la naturaleza son capaces de adaptarse a cambios dinamicos en su entorno y, por medio de la cooperación, pueden resolver problemas ´ cruciales para su supervivencia. Unicamente por medio de interacciones locales con otros miembros del enjambre y con el entorno, pueden lograr un objetivo común de forma más eficiente que lo haría un solo individuo. Este comportamiento problema-resolutivo que es resultado de la multiplicidad de interacciones se denomina Inteligencia de Enjambre. Los modelos matemáticos de comportamiento de enjambres en entornos naturales fueron propuestos inicialmente para resolver problemas de optimización. Sin embargo, esta aproximación descentralizada puede ser una herramienta valiosa en una variedad de aplicaciones donde patrones globales emergentes representan una solución de las tareas actuales. Aunque en la literatura se muestra la utilidad de los métodos de Inteligencia de Enjambre, no existe un entorno de trabajo que facilite su diseño. En esta memoria de tesis proponemos una nueva metodologia general de diseño para herramientas de Inteligencia de Enjambre. Desarrollamos herramientas noveles que representan ejem-plos ilustrativos de su implementación. Probamos la metodología propuesta en varios dominios definiendo un espacio discreto en el que los miembros del enjambre pueden moverse, modificando las reglas de las interacciones locales y fijando la función objetivo adecuada para evaluar las soluciones. La memoria de tesis presenta un conjunto de casos de estudio y se centra en dos aproximaciones generales. Una aproximación es aplicar Inteligencia de Enjambre como herramienta de optimización y extracción de características mientras que la otra es modelar sistemas multi-agente de tal manera que se asemejen a enjambres de animales en la naturaleza a los que se les confiere la habilidad de ejecutar autónomamente la tarea. Los enjambres artificiales están diseñados para ser autónomos, escalables, robustos y adaptables a los cambios en su entorno. En este trabajo, presentamos métodos que explotan una o más de estas características. Primero, validamos la metodología propuesta en un escenario del mundo real visto como un problema de optimización combinatoria. Después, proponemos un conjunto de herramientas noveles para ex-tracción de características, en concreto la detección adaptativa de bordes y el enlazado de bordes rotos en imágenes digitales, y el agrupamiento de datos para segmentación de imágenes. Finalmente, proponemos un algoritmo escalable para la asignación distribuida de tareas en sistemas multi-agente aplicada a enjambres de robots. La metodología general recién propuesta ofrece una guía para futuros desarrolladores deherramientas de Inteligencia de Enjambre

Asynchronous, distributed optimization for the coordinated planning of air and space assets

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 189-194).Recent decades have seen the development of more advanced sensor and communication systems, with the future certainly holding more innovation in these areas. However, current operations involve "stovepipe" systems in which inefficiencies are inherent. In this thesis, we examine how to increase the value of Earth observations made by coordinating across multiple collection systems. We consider both air and space assets in an asynchronous and distributed environment. We consider requests with time windows and priority levels, some of which require simultaneous observations by different sensors. We consider how these improvements could impact Earth observing sensors in two use areas; climate studies and intelligence collection operations. The primary contributions of this thesis include our approach to the asynchronous and distributed nature of the problem and the development of a value function to facilitate the coordination of the observations with multiple surveillance assets. We embed a carefully constructed value function in a simple optimization problem that we prove can be solved as a Linear Programming (LP) problem. We solve the optimization problem repeatedly over time to intelligently allocate requests to single-mission planners, or "sub-planners." We then show that the value function performs as we intend through empirical and statistical analysis. To test our methodologies, we integrate the coordination planner with two types of sub-planners, an Unmanned Aerial Vehicle (UAV) sub-planner, and a satellite sub-planner. We use the coordinator to generate observation plans for two notional operational Earth Science scenarios. Specifically, we show that coordination offers improvements in the priority of the requests serviced, the quality of those observations, and the ability to take dual collections. We conclude that a coordinated planning framework provides clear benefits.by Thomas Michael Herold.S.M

Distributed Target Engagement in Large-scale Mobile Sensor Networks

Sensor networks comprise an emerging field of study that is expected to touch many aspects of our life. Research in this area was originally motivated by military applications. Afterward sensor networks have demonstrated tremendous promise in many other applications such as infrastructure security, environment and habitat monitoring, industrial sensing, traffic control, and surveillance applications. One key challenge in large-scale sensor networks is the efficient use of the network's resources to collect information about objects in a given Volume of Interest (VOI). Multi-sensor Multi-target tracking in surveillance applications is an example where the success of the network to track targets in a given volume of interest, efficiently and effectively, hinges significantly on the network's ability to allocate the right set of sensors to the right set of targets so as to achieve optimal performance. This task can be even more complicated if the surveillance application is such that the sensors and targets are expected to be mobile. To ensure timely tracking of targets in a given volume of interest, the surveillance sensor network needs to maintain engagement with all targets in this volume. Thus the network must be able to perform the following real-time tasks: 1) sensor-to-target allocation; 2) target tracking; 3) sensor mobility control and coordination. In this research I propose a combination of the Semi-Flocking algorithm, as a multi-target motion control and coordination approach, and a hierarchical Distributed Constraint Optimization Problem (DCOP) modelling algorithm, as an allocation approach, to tackle target engagement problem in large-scale mobile multi-target multi-sensor surveillance systems. Sensor-to-target allocation is an NP-hard problem. Thus, for sensor networks to succeed in such application, an efficient approach that can tackle this NP-hard problem in real-time is disparately needed. This research work proposes a novel approach to tackle this issue by modelling the problem as a Hierarchical DCOP. Although DCOPs has been proven to be both general and efficient they tend to be computationally expensive, and often intractable for large-scale problems. To address this challenge, this research proposes to divide the sensor-to-target allocation problem into smaller sub-DCOPs with shared constraints, eliminating significant computational and communication costs. Furthermore, a non-binary variable modelling is presented to reduce the number of inter-agent constraints. Target tracking and sensor mobility control and coordination are the other main challenges in these networks. Biologically inspired approaches have recently gained significant attention as a tool to address this issue. These approaches are exemplified by the two well-known algorithms, namely, the Flocking algorithm and the Anti-Flocking algorithm. Generally speaking, although these two biologically inspired algorithms have demonstrated promising performance, they expose deficiencies when it comes to their ability to maintain simultaneous reliable dynamic area coverage and target coverage. To address this challenge, Semi-Flocking, a biologically inspired algorithm that benefits from key characteristics of both the Flocking and Anti-Flocking algorithms, is proposed. The Semi-Flocking algorithm approaches the problem by assigning a small flock of sensors to each target, while at the same time leaving some sensors free to explore the environment. Also, this thesis presents an extension of the Semi-Flocking in which it is combined with a constrained clustering approach to provide better coverage over maneuverable targets. To have a reliable target tracking, another extension of Semi-Flocking algorithm is presented which is a coupled distributed estimation and motion control algorithm. In this extension the Semi-Flocking algorithm is employed for the purpose of a multi-target motion control, and Kalman-Consensus Filter (KCF) for the purpose of motion estimation. Finally, this research will show that the proposed Hierarchical DCOP algorithm can be elegantly combined with the Semi-Flocking algorithm and its extensions to create a coupled control and allocation approach. Several experimental analysis conducted in this research illustrate how the operation of the proposed algorithms outperforms other approaches in terms of incurred computational and communication costs, area coverage, target coverage for both linear and maneuverable targets, target detection time, number of undetected targets and target coverage in noise conditions sensor network. Also it is illustrated that this algorithmic combination can successfully engage multiple sensors to multiple mobile targets such that the number of uncovered targets is minimized and the sensors' mean utilization factor sensor surveillance systems.is maximized

Configural decision support tool for schedule management of multiple unmanned aerial vehicles

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.Includes bibliographical references (p. 104-108).As unmanned aerial vehicles (UAVs) become increasingly autonomous, current single-UAV operations involving multiple personnel could transition to a single operator simultaneously supervising multiple UAVs in high-level control tasks. These time-critical, single-operator systems will require advance prediction and mitigation of schedule problems to ensure mission success. However, actions taken to address current schedule problems may create more severe future problems. Decision support could help multi-UAV operators evaluate different schedule management options in real-time and understand the consequences of their decisions. This thesis describes two schedule management decision support tools (DSTs) for single-operator supervisory control of four UAVs performing a time-critical targeting mission. A configural display common to both DSTs, called StarVis, graphically highlights schedule problems during the mission, and provides projections of potential new problems based upon different mission management actions. This configural display was implemented into a multi-UAV mission simulation as two different StarVis DST designs, Local and Q-Global. In making schedule management decisions, Local StarVis displayed the consequences of potential options for a single decision, while the Q-Global design showed the combined effects of multiple decisions. An experiment tested the two StarVis DSTs against a no DST control in a multi-UAV mission supervision task. Subjects using the Local StarVis performed better with higher situation awareness and no significant increase in workload over the other two DST conditions. The disparity in performance between the two StarVis designs is likely explained by the Q-Global StarVis projective "what if" mode overloading its subjects with information. This research highlights how decision support designs applied at different abstraction levels can produce different performance results.by Amy S. Brzezinski.S.M

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available