The thesis introduces an innovative UAV detection system. The commercial UAV market is booming. Meanwhile, the risks and threats from improper UAV usages are also booming. CUAS is to protect the public and facilities. The problem is a lack of an intelligent platform which can adapt many sensors for UAV detection. The hypothesis is that, the system can track the UAV’s movement by applying the multi-agent system (MAS) to UAV route track. The experiment proves that the multi-agent system benefits for the UAV track. <br>

<div>Enhancing the capability to control a tremendous range of physical actuators and sensors, combined with wireless technology and the Internet of Things (IoT), apparel technologies play a significant role in supporting safe, comfortable and healthy living, observing each customer’s conditions. Since apparel technologies have advanced to enable humans to work as a team with the clothing they wear, the interaction between a human and apparel is further enhanced with the introduction of sensors, wireless network, and artificially intelligent techniques. A variety of wearable technologies have been developed and spread to meet the needs of customers, however, some wearable devices are considered as non-practical tech-oriented, not consumer-oriented.</div><div>The purpose of this research is to develop an apparel system which integrates intelligent autonomous agents, human-based sensors, wireless network protocol, mobile application management system and a zipper robot. This research is an augmentation to the existing research and literature, which are limited to the zipping and unzipping process without much built in intelligence. This research is to face the challenges of the elderly and people with self-care difficulties. The intent is to provide a scientific path for intelligent zipper robot systems with potential, not only to help people, but also to be commercialized.</div><div>The research develops an intelligent system to control of zippers fixed on garments, based on the profile and desire of the human. The theoretical and practical elements of developing small, integrated, intelligent zipper robots that interact with an application by using a lightweight MQTT protocol for application in the daily lives of diverse populations of people with physical challenges. The system functions as intelligent automatized garment to ensure users could positively utilize a zipper robot device to assist in putting on garments which also makes them feel comfortable wearing and interacting with the system. This research is an approach towards the “future of fashion”, and the goal is to incentivize and inspire others to develop new instances of wearable robots and sensors that help people with specific needs to live a better life.</div>

In the simulation of human crowd behavior including evacuation planning, transportation management, and safety engineering in architecture design, the development of pedestrian model for higher behavior fidelity is an important task. To construct plausible facsimiles of real crowd movements, simulations should exhibit human behaviors for navigation, pedestrian decision-making, and social behaviors such as grouping and crowding. The research field is quite mature in some sense, with a large number of approaches that have been proposed to path finding, collision avoidance, and visually pleasing steering behaviors of virtual humans. However, there is still a clear disparity between the variety of approaches and the quality of crowd behaviors in simulations. Many social science field studies inform us that crowds are typically composed of multiple social groups (James, 1953; Coleman and James, 1961; Aveni, 1977). These observations indicate that one component of the complexity of crowd dynamics emerges from the presence of various patterns of social interactions within small groups that make up the crowd. Hence, realism in a crowd simulation may be enhanced when virtual characters are organized in multiple social groups, and exhibit human-like coordination behaviors. Motivated by the need for modeling groups in a crowd, we present a multi-agent model for large crowd simulations that incorporates socially plausible group behaviors. A computational model for multi-agent coordination and interaction informed by well- established Common Ground theory (Clark, 1996; Clark and Brennan, 1991) is proposed. In our approach, the task of navigation in a group is viewed as performing a joint activity which requires maintaining a state of common ground among group members regarding walking strategies and route choices. That is, group members communicate with, and adapt their behaviors to each other in order to maintain group cohesiveness while walking. In the course of interaction, an agent may present gestures or other behavioral cues according to its communicative purpose. It also considers the spatiotemporal conditions of the agent-group's environment in which the agent interacts when selecting a kind of motions. With the incorporation of our agent model, we provide a unified framework for crowd simulation and animation which accommodates high-level socially-aware behavioral realism of animated characters. The communicative purpose and motion selection of agents are consistently carried through from simulation to animation, and a resulted sequence of animated character behaviors forms not merely a chain of reactive or random gestures but a socially meaningful interactions. We conducted several experiments in order to investigate the impact of our social group interaction model in crowd simulation and animation. By showing that group communicative behaviors have a substantial influence on the overall distribution of a crowd, we demonstrate the importance of incorporating a model of social group interaction into multi-agent simulations of large crowd behaviors. With a series of perceptual user studies, we show that our model produces more believable behaviors of animated characters from the viewpoint of human observers. / Ph. D.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19124号 / 情博第570号 / 新制||情||100(附属図書館) / 32075 / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 杉江 俊治, 教授 太田 快人, 教授 大塚 敏之, 准教授 東 俊一 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第21916号 / 情博第699号 / 新制||情||120(附属図書館) / 京都大学大学院情報学研究科数理工学専攻 / (主査)准教授 加嶋 健司, 教授 太田 快人, 教授 山下 信雄 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Recent rapid advances in computing, communication, sensing, and actuation, together with miniaturization technologies, have offered unprecedented opportunities to employ large numbers of autonomous vehicles (air, ground, and water) working cooperatively to accomplish a mission. Cooperative control of such multi-agent dynamical systems has potential impact on numerous civilian, homeland security, and military applications. Compared with single-agent control problems, new theoretical and practical challenges emerge and need to be addressed in cooperative control of multiagent dynamical systems, including but not limited to problem size, task coupling, limited computational resources at individual agent level, communication constraints, and the need for real-time obstacle/collision avoidance. In order to address these challenges, a unified optimal multi-agent cooperative control strategy is proposed to formulate the multi-objective cooperative control problem into one unified optimal control framework. Many cooperative behaviors, such as consensus, cooperative tracking, formation, obstacle/collision avoidance, or flocking with cohesion and repulsion, can be treated in one optimization process. An innovative inverse optimal control approach is utilized to include these cooperative objectives in derived cost functions such that a closedorm cooperative control law can be obtained. In addition, the control law is distributed and only depends on the local neighboring agents’ information. Therefore, this new method does not demand intensive computational load and is easy for real-time onboard implementation. Furthermore, it is very scalable to large multi-agent cooperative dynamical systems. The closed-loop asymptotic stability and optimality are theoretically proved. Simulations based on MATLAB are conducted to validate the cooperative behaviors including consensus, Rendezvous, formation flying, and flocking, as well as the obstacle avoidance performance.

In this dissertation, we consider differential games for multi-agent systems under distributed information where every agent is only able to acquire information about the others according to a directed information graph of local communication/sensor networks. Such games arise naturally from many applications including mobile robot coordination, power system optimization, multiplayer pursuit-evasion games, etc. Since the admissible strategy of each agent has to conform to the information graph constraint, the conventional game strategy design approaches based upon Riccati equation(s) are not applicable because all the agents are required to have the information of the entire system. Accordingly, the game strategy design under distributed information is commonly known to be challenging. Toward this end, we propose novel open-loop and feedback game strategy design approaches for Nash equilibrium and noninferior solutions with a focus on linear quadratic differential games. For the open-loop design, approximate Nash/noninferior game strategies are proposed by integrating distributed state estimation into the open-loop global-information Nash/noninferior strategies such that, without global information, the distributed game strategies can be made arbitrarily close to and asymptotically converge over time to the global-information strategies. For the feedback design, we propose the best achievable performance indices based approach under which the distributed strategies form a Nash equilibrium or noninferior solution with respect to a set of performance indices that are the closest to the original indices. This approach overcomes two issues in the classical optimal output feedback approach: the simultaneous optimization and initial state dependence. The proposed open-loop and feedback design approaches are applied to an unmanned aerial vehicle formation control problem and a multi-pursuer single-evader differential game problem, respectively. Simulation results of several scenarios are presented for illustration.

<p dir="ltr">The problem of system identification is to learn the system dynamics from data. While classical system identification theories focused primarily on achieving asymptotic consistency, recent efforts have sought to characterize the number of samples needed to achieve a desired level of accuracy in the learned model. This thesis focuses on finite sample analysis for identifying/learning dynamical systems.</p><p dir="ltr">In the first part of this thesis, we provide novel results on finite sample analysis for learning different linear systems. We first consider the system identification problem of a fully observed system (i.e., all states of the system can be perfectly measured), leveraging data generated from an auxiliary system that shares ``similar" dynamics. We provide insights on the benefits of using the auxiliary data, and guidelines on selecting the weight parameter during the model training process. Subsequently, we consider the system identification problem for a partially observed autonomous linear system, where only a subset of states and multiple short trajectories of the system can be observed. We present a finite sample error bound and characterize the learning rate. </p><p dir="ltr">In the second part of this thesis, we explore the practical usage of finite sample analysis under several different scenarios. We first consider a parameter learning problem in a distributed setting, where a group of agents wishes to collaboratively learn the underlying model. We propose a distributed parameter estimation algorithm and provide finite time bounds on the estimation error. We show that our analysis allows us to determine a time at which the communication can be stopped (due to the costs associated with communications), while meeting a desired estimation accuracy. Subsequently, we consider the problem of online change point detection for a linear system, where the user observes data in an online manner, and the goal is to determine when the underlying system dynamics change. We provide an online change point detection algorithm, and a data-dependent threshold that allows one to achieve a pre-specified upper bound on the probability of making a false alarm. We further provide a finite-sample-based lower bound for the probability of detecting a change point with a certain delay.</p><p dir="ltr">Finally, we extend the results to linear model identification from non-linear systems. We provide a data acquisition algorithm followed by a regularized least squares algorithm, along with an associated finite sample error bound on the learned linearized dynamics. Our error bound demonstrates a trade-off between the error due to nonlinearity and the error due to noise, and shows that one can learn the linearized dynamics with arbitrarily small error given sufficiently many samples.</p>

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This research presents both theoretical foundation and numerical simulation work for design and analysis of a multi-agent dynamic system on the collective formation behavior patterns of grouped agents. A mass model with tunable control parameters is proposed. This model can realistically represent the aggregation pattern and the formation shape of multiple agents. Stability analysis is also provided to prove the stability of the second-order dynamic system. Several simulations will also be given according to the proposed model to show the aggregation patterns. The research on self-organizing characteristics of collective agent behaviors has a wide range of applications in nature and engineering. The formation such as a flock of birds, a school of fish, or a swarm of locusts, is the emergence of ordered state in which the moving agents can organize as formation. Design and control of the self-organizing dynamic system has implications on wireless general design of mobile sensor networks, sensor network data fusion, attitude alignment of satellite clusters and congestion control of communication networks. / Electrical and Computer Engineering