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abstract: Coordination and control of Intelligent Agents as a team is considered in this thesis. Intelligent agents learn from experiences, and in times of uncertainty use the knowl- edge acquired to make decisions and accomplish their individual or team objectives. Agent objectives are defined using cost functions designed uniquely for the collective task being performed. Individual agent costs are coupled in such a way that group ob- jective is attained while minimizing individual costs. Information Asymmetry refers to situations where interacting agents have no knowledge or partial knowledge of cost functions of other agents. By virtue of their intelligence, i.e., by learning from past experiences agents learn cost functions of other agents, predict their responses and act adaptively to accomplish the team’s goal. Algorithms that agents use for learning others’ cost functions are called Learn- ing Algorithms, and algorithms agents use for computing actuation (control) which drives them towards their goal and minimize their cost functions are called Control Algorithms. Typically knowledge acquired using learning algorithms is used in con- trol algorithms for computing control signals. Learning and control algorithms are designed in such a way that the multi-agent system as a whole remains stable during learning and later at an equilibrium. An equilibrium is defined as the event/point where cost functions of all agents are optimized simultaneously. Cost functions are designed so that the equilibrium coincides with the goal state multi-agent system as a whole is trying to reach. In collective load transport, two or more agents (robots) carry a load from point A to point B in space. Robots could have different control preferences, for example, different actuation abilities, however, are still required to coordinate and perform load transport. Control preferences for each robot are characterized using a scalar parameter θ i unique to the robot being considered and unknown to other robots. With the aid of state and control input observations, agents learn control preferences of other agents, optimize individual costs and drive the multi-agent system to a goal state. Two learning and Control algorithms are presented. In the first algorithm(LCA- 1), an existing work, each agent optimizes a cost function similar to 1-step receding horizon optimal control problem for control. LCA-1 uses recursive least squares as the learning algorithm and guarantees complete learning in two time steps. LCA-1 is experimentally verified as part of this thesis. A novel learning and control algorithm (LCA-2) is proposed and verified in sim- ulations and on hardware. In LCA-2, each agent solves an infinite horizon linear quadratic regulator (LQR) problem for computing control. LCA-2 uses a learning al- gorithm similar to line search methods, and guarantees learning convergence to true values asymptotically. Simulations and hardware implementation show that the LCA-2 is stable for a variety of systems. Load transport is demonstrated using both the algorithms. Ex- periments running algorithm LCA-2 are able to resist disturbances and balance the assumed load better compared to LCA-1. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2018

In this thesis, various control problems originating from the field of mobile robotics are considered. In particular, the thesis deals with problems that are related to the interaction and coordination of multiple mobile units. The scientific contributions are presented in five papers that together constitute the main part of the thesis. The papers are preceded by a longer introductory part, in which some important results from control theory, data processing and robotics are reviewed. In the first of the appended papers, two stabilizing tracking controls are proposed for a non-holonomic robot platform of unicycle type. Tolerance to errors and other properties of the controllers are discussed and a reactive obstacle avoidance control, that can easily be incorporated with the proposed tracking controls, is suggested. In Paper B, the results from Paper~A are extended to multi-agent systems. It is demonstrated how the tracking controls from Paper A can be used as building blocks when putting together formations of robots, in which each robot maintains a fixed position relative its neighbors during translation. In addition, switching between the different control functions is shown to be robust, implying that it is possible to change the shape of a formation on-line. In the first two papers, the tracking problem is facilitated by the assumption that the approximate velocity of the target/leader is known to the tracking robot. Paper C treats the the case where the target velocity is neither directly measurable with the available sensor setup, nor possible to obtain through communication with neighboring agents. Straight-forward computation of the target velocity from available sensor data unfortunately tend to enhance measurement errors and give unreliable estimates. To overcome the difficulties, an alternative approach to velocity estimation is proposed, motivated by the local observability of the given control system. Paper D deals with another problematic aspect of data acquisition. When using range sensors, one often obtains a mixed data set with measurements originating from many different sources. This problem would, for instance, be encountered by a robot moving in a formation, where it was surrounded by other agents. There exist established techniques for sorting mixed data sets off-line, but for time-depending systems where data need to be sorted on-line and only small time delays can be tolerated, established methods fail. The solution presented in the paper is a prediction-correction type algorithm, referred to as CCIA (Classification Correction and Identification algorithm). Finally, in Paper E, we consider the problem of maintaining connectivity in a multi-agent system. Often inter-agent communication abilities are associated with some proximity constraints, so when the robots move in relation to each other, communication links both break and form. In the paper we present a framework for analysis that makes it possible to compute a set of general constraints which, if satisfied, are sufficient to guarantee maintained communication for a given multi-agent system. Constraints are computed for two sorts of consensus-based systems and the results are verified in simulations. / QC 20100715

Submitted by Jessica Andrade (jessicastefanysa@gmail.com) on 2018-06-28T18:00:49Z No. of bitstreams: 1 TELMO.pdf: 5034534 bytes, checksum: d5ff82ed46adf98d29eb0e7b1578bb38 (MD5) / Approved for entry into archive by Margareth Ferreira Pinto (margarethfpinto@hotmail.com) on 2018-07-16T21:35:04Z (GMT) No. of bitstreams: 1 TELMO.pdf: 5034534 bytes, checksum: d5ff82ed46adf98d29eb0e7b1578bb38 (MD5) / Made available in DSpace on 2018-07-16T21:35:04Z (GMT). No. of bitstreams: 1 TELMO.pdf: 5034534 bytes, checksum: d5ff82ed46adf98d29eb0e7b1578bb38 (MD5) Previous issue date: 2015 / O desenvolvimento de sistemas com complexidade necessária a uma abordagem mul- tiagente carrega consigo também aspectos de complexidade relacionados à avaliação dos diferentes níveis e componentes desse sistema. Um sistema multiagente pode ser com- posto por uma série de agentes heterogêneos, que apresentam variabilidade quanto à sua arquitetura interna, modelos utilizados para o seu desenvolvimento, linguagem de programação, de especificação e validação. Agregam-se a isso, contextos específicos de cada solução para com o ambiente para o qual foi projetado. Deste modo, impõem-se mecanismos que permitam estabelecer métricas de avaliação para cada nível do desenvol- vimento de um sistema multiagente, considerando dimensões como organização, comuni- cação entre agentes e os agentes em si. Esta dissertação apresenta como problemática, o estabelecimento de um benchmark para sistemas multiagente dentro do simulador de futebol de robôs Soccer Server 2D. Mais especificamente, este benchmark deve prover métricas e mecanismos de avaliação de esquemas organizacionais multiagente segundo os diferentes cenários que podem se estabelecer dentro da dinâmica de uma partida de futebol. Não obstante, deve-se permitir o estabelecimento de referências de avaliação da coletividade dos times implementados para o Soccer Server 2D, indiferente aos demais níveis de concepção do sistema. / The development of systems with the required complexity for a multi-agent approach, also carries complexity aspects related to the evaluation of different levels and compo- nents of such a system. A multi-agent system can be composed of a series of hetero- geneous agents, which have variability regarding its internal architecture, models used for its development, programming language, specification and validation. Added to this, are situated the particular contexts of each solution towards the environment for which it is designed. Therefore, it is needed mechanisms to establish evaluation metrics for each multi-agent system development level, taking into account dimensions such as or- ganization, communication between agents and the agents themselves. This dissertation presents as a problem, establish a benchmark for multi-agent systems within the robot soccer simulator Soccerserver 2D. Specifically, this benchmark should provide metrics and evaluation mechanisms of multi-agent organization schemes according to different scenarios that can be established within the dynamics of a football match . Nevertheless, it should be allowed the establishment of assessment referrals for the Robocup teams collectivity, regardless to other levels of system design.

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Multi-agent information gathering (MA-IG) enables autonomous robots to cooperatively collect information in an unfamiliar area. In some scenarios, the focus is on gathering the true mapping of a physical quantity such as temperature or magnetic field. This thesis proposes a computationally efficient algorithm known as multi-agent RRT-clustered Stackelberg game (MA-RRTc-SG) to solve MA-IG. During exploration, measurements are taken along robot paths to update the belief of a Gaussian process (GP), which gives a continuous estimation of the physical process. To seek informative paths, agents first resort to self-planning: one individually generates a number of choices using sampling-based algorithms and preserves informative ones. Then, paths from different robots are combined and investigated based on a multi-player Stackelberg game. The Stackelberg game ensures robots select the combination of paths that yield maximum system reward. The reward function plays an important role in the aforementioned two steps. In our work, robots are awarded for selecting informative paths and punished for hazardous movements and large control inputs. In experiments, we first conduct variation studies to investigate the influence of key parameters in the proposed algorithm. Then, the algorithm is tested in a simulation case to map the radiation intensity in a nuclear plant. Results show that using our algorithm, robots are able to collect information in an efficient and cooperative way compared to random exploration. / Multi-agent informationsinsamling gör det möjligt för autonoma robotar att samarbeta samla in information i ett okänt område. I vissa scenarier ligger fokus på att samla in den verkliga kartläggningen av en fysisk storhet som temperatur eller magnetfält. Den här avhandlingen föreslår en beräkningseffektiv algoritm som kallas multi-agent RRT-clustered Stackelberg game (MA-RRTc-SG) för att lösa multi-agent informationsinsamling. Under prospektering görs mätningar längs robotbanor för att uppdatera tron på en Gaussisk process, vilket ger en kontinuerlig uppskattning av den fysiska processen. För att söka informativa vägar tillgriper agenter först självplanering: man genererar individuellt ett antal val med hjälp av samplingsbaserade algoritmer och bevarar informativa. Sedan kombineras och undersöks vägar från olika robotar utifrån en Stackelberg spel för flera spelare. Stackelberg spelet säkerställer att robotar väljer kombinationen av vägar som ger maximal systembelöning. Belöningsfunktionen spelar en viktig roll i de ovan nämnda två stegen. I vårt arbete belönas robotar för att välja informativa vägar och straffas för osäkra rörelser och stora kontrollingångar. I experiment genomför vi först variationsstudier för att undersöka inverkan av nyckelparametrar i den föreslagna algoritmen. Därefter testas algoritmen i ett simuleringsfall för att kartlägga strålningsintensiteten i ett kärnkraftverk. Resultaten visar att med vår algoritm kan robotar samla in information på ett effektivt och samarbetssätt jämfört med slumpmässig utforskning.

The purpose of information acquisition for data and information fusion is to provide relevant and timely information. The acquired information is integrated (or fused) to estimate the state of some environment. The success of information acquisition can be measured in the quality of the environment state estimates generated by the data and information fusion process. In this thesis, we introduce and set out to characterise the concept of large-scale information acquisition. Our interest in this subject is justified both by the identified lack of research on a holistic view on data and information fusion, and the proliferation of networked sensors which promises to enable handy access to a multitude of information sources. We identify a number of properties that could be considered in the context of large-scale information acquisition. The sensors used could be large in number, heterogeneous, complex, and distributed. Also, algorithms for large-scale information acquisition may have to deal with decentralised control and multiple and varying objectives. In the literature, a process that realises information acquisition is frequently denoted sensor management. We, however, introduce the term perception management instead, which encourages an agent perspective on information acquisition. Apart from explictly inviting the wealth of agent theory research into the data and information fusion research, it also highlights that the resource usage of perception management is constrained by the overall control of a system that uses data and information fusion. To address the challenges posed by the concept of large-scale information acquisition, we present a framework which highlights some of its pertinent aspects. We have implemented some important parts of the framework. What becomes evident in our study is the innate complexity of information acquisition for data and information fusion, which suggests approximative solutions. We, furthermore, study one of the possibly most important properties of large-scale information acquisition, decentralised control, in more detail. We propose a recurrent negotiation protocol for (decentralised) multi-agent coordination. Our approach to the negotiations is from an axiomatic bargaining theory perspective; an economics discipline. We identify shortcomings of the most commonly applied bargaining solution and demonstrate in simulations a problem instance where it is inferior to an alternative solution. However, we can not conclude that one of the solutions dominates the other in general. They are both preferable in different situations. We have also implemented the recurrent negotiation protocol on a group of mobile robots. We note some subtle difficulties with transferring bargaining solutions from economics to our computational problem. For instance, the characterising axioms of solutions in bargaining theory are useful to qualitatively compare different solutions, but care has to be taken when translating the solution to algorithms in computer science as some properties might be undesirable, unimportant or risk being lost in the translation. / QC 20100903

Cooperative control problems for multiple vehicle systems can be categorized as either formation control problems with applications to mobile robots, unmanned air vehicles, autonomous underwater vehicles, satellites, aircraft, spacecraft, and automated highway systems, or non-formation control problems such as task assignment, cooperative transport, cooperative role assignment, air traffic control, cooperative timing, and cooperative search. The cooperative control of multiple vehicle systems poses significant theoretical and practical challenges. For cooperative control strategies to be successful, numerous issues must be addressed. We consider three important and correlated issues: consensus seeking, formation keeping, and trajectory tracking. For consensus seeking, we investigate algorithms and protocols so that a team of vehicles can reach consensus on the values of the coordination data in the presence of imperfect sensors, communication dropout, sparse communication topologies, and noisy and unreliable communication links. The main contribution of this dissertation in this area is that we show necessary and/or sufficient conditions for consensus seeking with limited, unidirectional, and unreliable information exchange under fixed and switching interaction topologies (through either communication or sensing). For formation keeping, we apply a so-called "virtual structure" approach to spacecraft formation flying and multi-vehicle formation maneuvers. As a result, single vehicle path planning and trajectory generation techniques can be employed for the virtual structure while trajectory tracking strategies can be employed for each vehicle. The main contribution of this dissertation in this area is that we propose a decentralized architecture for multiple spacecraft formation flying in deep space with formation feedback introduced. This architecture ensures the necessary precision in the presence of actuator saturation, internal and external disturbances, and stringent inter-vehicle communication limitations. A constructive approach based on the satisficing control paradigm is also applied to multi-robot coordination in hardware. For trajectory tracking, we investigate nonlinear tracking controllers for fixed wing unmanned air vehicles and nonholonomic mobile robots with velocity and heading rate constraints. The main contribution of this dissertation in this area is that our proposed tracking controllers are shown to be robust to input uncertainties and measurement noise, and are computationally simple and can be implemented with low-cost, low-power microcontrollers. In addition, our approach allows piecewise continuous reference velocity and heading rate and can be extended to derive a variety of other trajectory tracking strategies.