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

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