A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

Constrained Task Assignment and Scheduling on Networks of Arbitrary Topology.

This dissertation develops a framework to address centralized and distributed constrained task assignment and task scheduling problems. This framework is used to prove properties of these problems that can be exploited, develop effective solution algorithms, and to prove important properties such as correctness, completeness and optimality. The centralized task assignment and task scheduling problem treated here is expressed as a vehicle routing problem with the goal of optimizing mission time subject to mission constraints on task precedence and agent capability. The algorithm developed to solve this problem is able to coordinate vehicle (agent) timing for task completion. This class of problems is NP-hard and analytical guarantees on solution quality are often unavailable. This dissertation develops a technique for determining solution quality that can be used on a large class of problems and does not rely on traditional analytical guarantees. For distributed problems several agents must communicate to collectively solve a distributed task assignment and task scheduling problem. The distributed task assignment and task scheduling algorithms developed here allow for the optimization of constrained military missions in situations where the communication network may be incomplete and only locally known. Two problems are developed. The distributed task assignment problem incorporates communication constraints that must be satisfied; this is the Communication-Constrained Distributed Assignment Problem. A novel distributed assignment algorithm, the Stochastic Bidding Algorithm, solves this problem. The algorithm is correct, probabilistically complete, and has linear average-case time complexity. The distributed task scheduling problem addressed here is to minimize mission time subject to arbitrary predicate mission constraints; this is the Minimum-time Arbitrarily-constrained Distributed Scheduling Problem. The Optimal Distributed Non-sequential Backtracking Algorithm solves this problem. The algorithm is correct, complete, outputs time optimal schedules, and has low average-case time complexity. Separation of the task assignment and task scheduling problems is exploited here to ameliorate the effects of an incomplete communication network. The mission-modeling conditions that allow this and the benefits gained are discussed in detail. It is shown that the distributed task assignment and task scheduling algorithms developed here can operate concurrently and maintain their correctness, completeness, and optimality properties.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91527/1/jpjack_1.pd

Models and Methods for Multi-Actor Systems

2010/2011The study of the models and methods to apply to multi-actor systems is a widely discussed research topic in the related scientific literature. The multi-actor systems are defined as systems that are characterized by the presence of several autonomous elements, of different decision makers and of complex rules that allow the communication, the coordination and the connection of the components of such systems. Commonly, the study of Multi-Actor System, MAS, recalls the well-known issues concerning the multi-agent systems. The research topic related to the multi-agent system firstly started to appear in scientific literature in 1980s, mainly in relation to the computer science and artificial intelligence. In this dissertation, in particular, the study of the multi-agent systems, and specifically of the multi-actor systems, is taken into account merely in relation to the distinctive features of complexity that characterize such systems and not to the issues concerning the agent-oriented software engineering. Therefore, the research results presented in this thesis are focused on the development and on the realization of innovative models and methodologies to face the management and the decision making mechanisms applied to complex multi-actor systems. This dissertation especially focuses on two different examples of multi-actor systems in two very diverse perspectives. The former deals with the research problem related to intermodal transportation networks, while the latter with the so called consensus problem in distributed networks of agents. Concerning the research problem related to the intermodal logistic systems, the research activity addresses the management of their more and more increasing complexity by the applications of the modern Information and Communication Technologies (ICT) tools that are key solutions to achieve the efficiency and to enhance logistics competitiveness. The related scientific literature still seems lacking in addressing with adequate attention the impact of these new techniques on the management of these complex systems and, moreover, there is an apparent lack of a systematic and general methodology to describe in detail the multiplicity of elements that can influence the dynamics and the corresponding information and decision making structure of intermodal transportation systems. The innovative results presented in this dissertation are focused on the development of an Integrated System, IS, devoted to manage intermodal transportation networks at the tactical as well as operational decision level to be used by decision makers both in off-line planning and real time management. To specify the Integrated System, a reference model is developed relying on a top-down metamodeling procedure. These innovative research results are a contribution to bridge the gap and to propose not only a systematic modeling approach devoted to describe a generic multi-actor logistic system, but also a management technique based on a closed loop strategy. The second example of application is focused on a topic that is widely discussed in scientific literature related to the study of the multi-actor collective behaviors in a distributed network. The interaction protocols that allow the agents to reach the convergence to a common value is called consensus or agreement problem. This research problem is particularly studied in the context of cooperative control of multi-agent systems because the agents are autonomous, independent and have to interact in a distributed network. The presented research results address the investigation of new and fast alignment protocols that enhance the performances of the standard iteration protocols for particular topologies of digraphs on the basis of a triangular splitting of the standard iteration matrix. The examined examples, the models and the methodologies applied to analyze them, are very different in the two cases and this testifies the large extent of research problems related to the multi-actor systems.L’analisi di modelli e metodi da sviluppare e da applicare nel contesto dei sistemi multi-attoriali costituisce un tema molto variegato e discusso nella letteratura scientifica internazionale. I sistemi multi-attoriali sono sistemi che si contraddistinguono per la presenza di molti elementi autonomi diversi tra loro, di molteplici decisori e di complesse regole che determinano la comunicazione, il coordinamento e la connessione all'interno di tali sistemi. Frequentemente, facendo riferimento a sistemi multi-attoriali, Multi-Actor Systems, si richiama il tema molto attuale dei sistemi multi agente, Multi-Agent Systems. Diffusisi a partire dal 1980, i sistemi multi agente sono spesso studiati in relazione alle metodologie di sviluppo dell'ingegneria del software. Nel presente lavoro di tesi, il tema dei sistemi multi-agente, ed in particolare di quelli multi-attoriali, non viene analizzato in questo contesto, ma in relazione alle tecniche decisionali da adottare per gestire sistemi caratterizzati da un alto livello di complessità. In tale ambito, i risultati presentati all'interno di questa dissertazione sono focalizzati sullo sviluppo e sulla realizzazione di nuovi metodi e di nuove metodologie, in grado di affrontare la gestione della complessità dei sistemi multi-attoriali. Vengono in particolare esaminate due diverse problematiche, in due contesti completamente diversi e con tecniche differenti, a testimoniare le vaste applicazioni che riguardano i sistemi multi-attoriali. I problemi analizzati sono incentrati, in primo luogo, su un'applicazione inerente la gestione di sistemi logistici intermodali ed, in secondo luogo, sullo studio delle regole o protocolli di interazione in una rete distribuita di agenti autonomi. Per quanto riguarda l'aspetto legato ai sistemi intermodali di trasporto, un tema molto discusso nella letteratura scientifica recente, l'analisi si focalizza sulla gestione della loro sempre crescente complessità, tramite l'utilizzo di sistemi dell'Information and Communication Technology, ICT. Questi strumenti richiedono metodi e modelli che sono innovativi rispetto a quanto è presente nella letteratura scientifica, all'interno della quale è stata riscontrata la mancanza di un approccio sistematico e sufficientemente ad alto livello per la realizzazione di una metodologia in grado di descrivere allo stesso tempo sia la molteplicità di elementi che influenzano le dinamiche e le informazioni, sia le strutture decisionali dei sistemi intermodali. L'innovazione dei risultati presentati in questa tesi si focalizza proprio sull'esigenza di proporre un sistema integrato, Integrated System (IS), basato su un metamodello delle reti intermodali di trasporto, che fornisca un valido supporto ai decisori sia a livello tattico che operativo. Il secondo aspetto affrontato in questa tesi riguarda un altro argomento di largo ed attuale interesse nella letteratura scientifica, che viene comunemente chiamato problema del consenso. Questo problema affronta lo studio di come diversi agenti autonomi collocati su una rete distribuita siano in grado di comunicare e di accordarsi su un valore comune, senza la presenza di un decisore centrale. A questo scopo ci sono degli algoritmi che specificano le regole o protocolli di interazione tra i diversi agenti. In tale contesto, i risultati proposti si focalizzano su alcune problematiche rappresentate dal protocollo classico del consenso e soprattutto sulla sua scarsa efficienza in particolari conformazioni delle reti di agenti. Il lavoro di tesi propone, quindi, un approccio di suddivisione, splitting, della matrice standard di iterazione, di tipo triangolare, che presenta notevoli vantaggi in termini di performance rispetto all'algoritmo classico. Lo studio di problemi multi-attoriali, pertanto, richiede lo sviluppo di innovative metodologie decisionali e di nuovi metodi di gestione delle comunicazioni, per rispondere al livello sempre crescente di complessità, offrendo in questo modo alcuni spunti molto interessanti per la ricerca.XXIV Ciclo198

A Mechanism Design Approach to Bandwidth Allocation in Tactical Data Networks

The defense sector is undergoing a phase of rapid technological advancement, in the pursuit of its goal of information superiority. This goal depends on a large network of complex interconnected systems - sensors, weapons, soldiers - linked through a maze of heterogeneous networks. The sheer scale and size of these networks prompt behaviors that go beyond conglomerations of systems or `system-of-systems\u27. The lack of a central locus and disjointed, competing interests among large clusters of systems makes this characteristic of an Ultra Large Scale (ULS) system. These traits of ULS systems challenge and undermine the fundamental assumptions of today\u27s software and system engineering approaches. In the absence of a centralized controller it is likely that system users may behave opportunistically to meet their local mission requirements, rather than the objectives of the system as a whole. In these settings, methods and tools based on economics and game theory (like Mechanism Design) are likely to play an important role in achieving globally optimal behavior, when the participants behave selfishly. Against this background, this thesis explores the potential of using computational mechanisms to govern the behavior of ultra-large-scale systems and achieve an optimal allocation of constrained computational resources Our research focusses on improving the quality and accuracy of the common operating picture through the efficient allocation of bandwidth in tactical data networks among self-interested actors, who may resort to strategic behavior dictated by self-interest. This research problem presents the kind of challenges we anticipate when we have to deal with ULS systems and, by addressing this problem, we hope to develop a methodology which will be applicable for ULS system of the future. We build upon the previous works which investigate the application of auction-based mechanism design to dynamic, performance-critical and resource-constrained systems of interest to the defense community. In this thesis, we consider a scenario where a number of military platforms have been tasked with the goal of detecting and tracking targets. The sensors onboard a military platform have a partial and inaccurate view of the operating picture and need to make use of data transmitted from neighboring sensors in order to improve the accuracy of their own measurements. The communication takes place over tactical data networks with scarce bandwidth. The problem is compounded by the possibility that the local goals of military platforms might not be aligned with the global system goal. Such a scenario might occur in multi-flag, multi-platform military exercises, where the military commanders of each platform are more concerned with the well-being of their own platform over others. Therefore there is a need to design a mechanism that efficiently allocates the flow of data within the network to ensure that the resulting global performance maximizes the information gain of the entire system, despite the self-interested actions of the individual actors. We propose a two-stage mechanism based on modified strictly-proper scoring rules, with unknown costs, whereby multiple sensor platforms can provide estimates of limited precisions and the center does not have to rely on knowledge of the actual outcome when calculating payments. In particular, our work emphasizes the importance of applying robust optimization techniques to deal with the uncertainty in the operating environment. We apply our robust optimization - based scoring rules algorithm to an agent-based model framework of the combat tactical data network, and analyze the results obtained. Through the work we hope to demonstrate how mechanism design, perched at the intersection of game theory and microeconomics, is aptly suited to address one set of challenges of the ULS system paradigm - challenges not amenable to traditional system engineering approaches

Distributed optimal and predictive control methods for networks of dynamic systems

Several recent approaches to distributed control design over networks of interconnected dynamic systems rely on certain assumptions, such as identical subsystem dynamics, absence of dynamical couplings, linear dynamics and undirected interaction schemes. In this thesis, we investigate systematic methods for relaxing a number of simplifying factors leading to a unifying approach for solving general distributed-control stabilization problems of networks of dynamic agents. We show that the gain-margin property of LQR control holds for complex multiplicative input perturbations and a generic symmetric positive definite input weighting matrix. Proving also that the potentially non-simple structure of the Laplacian matrix can be neglected for stability analysis and control design, we extend two well-known distributed LQR-based control methods originally established for undirected networks of identical linear systems, to the directed case. We then propose a distributed feedback method for tackling large-scale regulation problems of a general class of interconnected non-identical dynamic agents with undirected and directed topology. In particular, we assume that local agents share a minimal set of structural properties, such as input dimension, state dimension and controllability indices. Our approach relies on the solution of certain model matching type problems using local linear state-feedback and input matrix transformations which map the agent dynamics to a target system, selected to minimize the joint control effort of the local feedback-control schemes. By adapting well-established distributed LQR control design methodologies to our framework, the stabilization problem of a network of non-identical dynamical agents is solved. We thereafter consider a networked scheme synthesized by multiple agents with nonlinear dynamics. Assuming that agents are feedback linearizable in a neighborhood near their equilibrium points, we propose a nonlinear model matching control design for stabilizing networks of multiple heterogeneous nonlinear agents. Motivated by the structure of a large-scale LQR optimal problem, we propose a stabilizing distributed state-feedback controller for networks of identical dynamically coupled linear agents. First, a fully centralized controller is designed which is subsequently substituted by a distributed state-feedback gain with sparse structure. The control scheme is obtained byoptimizing an LQR performance index with a tuning parameter utilized to emphasize/deemphasize relative state difference between coupled systems. Sufficient conditions for stability of the proposed scheme are derived based on the inertia of a convex combination of two Hurwitz matrices. An extended simulation study involving distributed load frequency control design of a multi-area power network, illustrates the applicability of the proposed method. Finally, we propose a fully distributed consensus-based model matching scheme adapted to a model predictive control setting for tackling a structured receding horizon regulation problem

Coalitional model predictive control for systems of systems

An aspect so far rarely contemplated in distributed control problems is the explicit consideration of individual (local) interests of the components of a complex system. Indeed, the focus of the majority of the literature about distributed control has been the overall system performance. While on one hand this permitted to address fundamental properties of centralized control, such as system-wide optimality and stability, one the other hand it implied assuming unrestricted cooperation across local controllers. However, when dealing with multi-agent systems with a strong heterogeneous character, cooperation between the agents cannot be taken for granted (due to, for example, logistics, market competition), and selfish interests may not be neglected. Another critical point that must be kept into consideration is the diversity characterizing systems of systems (SoS), yielding very complex interactions between the agents involved (one example of such system is the smart grid). In order to tackle such inherent aspects of SoS, the research presented in this thesis has been concerned with the development of a novel framework, the coalitional control, that extends the scope of advanced control methods (in particular MPC) by drawing concepts from cooperative game theory that are suited for the inherent heterogeneity of SoS, providing as well an economical interpretation useful to explicitly take into account local selfish interests. Thus, coalitional control aims at governing the association/dissociation dynamics of the agents controlling the system, according to the expected benefits of their possible cooperation. From a control theoretical perspective, this framework is founded on the theory of switched systems and variable structure/topology networked systems, topics that are recently experiencing a renewed interest within the community. The main concepts and challenges in coalitional control, and the links with cooperative network game theory are presented in this document, tracing a path from model partitioning to the control schemes whose principles delineate the idea of coalitional control. This thesis focuses on two basic architectures: (i) a hierarchically supervised evolution of the coalitional structure, and (ii) a protocol for autonomous negotiation between the agents, with specific mechanisms for benefit redistribution, leading to the emergence of cooperating clusters.Premio Extraordinario de Doctorado U

An Industrial Data Analysis and Supervision Framework for Predictive Manufacturing Systems

Due to the advancements in the Information and Communication Technologies field in the modern interconnected world, the manufacturing industry is becoming a more and more data rich environment, with large volumes of data being generated on a daily basis, thus presenting a new set of opportunities to be explored towards improving the efficiency and quality of production processes. This can be done through the development of the so called Predictive Manufacturing Systems. These systems aim to improve manufacturing processes through a combination of concepts such as Cyber-Physical Production Systems, Machine Learning and real-time Data Analytics in order to predict future states and events in production. This can be used in a wide array of applications, including predictive maintenance policies, improving quality control through the early detection of faults and defects or optimize energy consumption, to name a few. Therefore, the research efforts presented in this document focus on the design and development of a generic framework to guide the implementation of predictive manufacturing systems through a set of common requirements and components. This approach aims to enable manufacturers to extract, analyse, interpret and transform their data into actionable knowledge that can be leveraged into a business advantage. To this end a list of goals, functional and non-functional requirements is defined for these systems based on a thorough literature review and empirical knowledge. Subsequently the Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework is proposed, along with a detailed description of each of its main components. Finally, a pilot implementation is presented for each of this components, followed by the demonstration of the proposed framework in three different scenarios including several use cases in varied real-world industrial areas. In this way the proposed work aims to provide a common foundation for the full realization of Predictive Manufacturing Systems