Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

In-Network Outlier Detection in Wireless Sensor Networks

To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy usage,(3) only uses single hop communication thus permitting very simple node failure detection and message reliability assurance mechanisms (e.g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data. We examine performance using simulation with real sensor data streams. Our results demonstrate that our approach is accurate and imposes a reasonable communication load and level of power consumption.Comment: Extended version of a paper appearing in the Int'l Conference on Distributed Computing Systems 200

Machine Learning for Unmanned Aerial System (UAS) Networking

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale. With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring. This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS

Ordering-Flexible Multi-Robot Coordination for MovingTarget Convoying Using Long-TermTask Execution

In this paper, we propose a cooperative long-term task execution (LTTE) algorithm for protecting a moving target into the interior of an ordering-flexible convex hull by a team of robots resiliently in the changing environments. Particularly, by designing target-approaching and sensing-neighbor collision-free subtasks, and incorporating these subtasks into the constraints rather than the traditional cost function in an online constraint-based optimization framework, the proposed LTTE can systematically guarantee long-term target convoying under changing environments in the n-dimensional Euclidean space. Then, the introduction of slack variables allow for the constraint violation of different subtasks; i.e., the attraction from target-approaching constraints and the repulsion from time-varying collision-avoidance constraints, which results in the desired formation with arbitrary spatial ordering sequences. Rigorous analysis is provided to guarantee asymptotical convergence with challenging nonlinear couplings induced by time-varying collision-free constraints. Finally, 2D experiments using three autonomous mobile robots (AMRs) are conducted to validate the effectiveness of the proposed algorithm, and 3D simulations tackling changing environmental elements, such as different initial positions, some robots suddenly breakdown and static obstacles are presented to demonstrate the multi-dimensional adaptability, robustness and the ability of obstacle avoidance of the proposed method

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

Ad-hoc wireless networking for supporting on-site communication

Ad-hoc networks are self-organized wireless networks. They have the potential to be widely used in emergency salvation, construction sites, and military fields. However, the research about the efficient usage of ad-hoc networking in engineering applications is still limited. In this research, a new approach for investigating problems related to deploying ad-hoc wireless networks for supporting on-site communication and collaboration is proposed. Several modes of communication which are common in on-site applications are considered including location information, text messaging, voice and video communications, and file transmission. A prototype system is implemented for testing these modes based on available ad-hoc network protocols and using mobile devices. In addition, in order to verify our proposed approach, several tests are designed and implemented to demonstrate the usefulness of the prototype system. The results from the tests showed that our prototype system is applicable for ad-hoc wireless networks. Furthermore, a new protocol based on clustering to improve data accessibility in ad-hoc networks is tested using a simulation tool to study its performance under different scenarios. The simulation results showed the impact of the area size, wireless range, number of nodes, and node speed on data accessibility