This project first explored various methods of designing a communication and control system for an AGV. It then implemented a SOA based communication system in ROS on the selected AGV. The ROS package created in the project implemented functions of the Aria and ArNetworking libraries from Adept. The next step of the project implemented the functions of teleoperation, mapping and transfer of maps and navigation from Aria into ROS. The packages built implemented these functions in different ways to test the best method to transfer Aria functions into ROS. A generic set of rules were then formulated that aided the conversion of these functions for users unfamiliar with either of the two packages (ROS and Aria).

Resource allocation in control and communication systems constitutes the distribution of (finite) system resources in a way that achieves maximum system functionality and or cost effectiveness. Specific resource allocation problems in subband coding, Discrete Multi-tone modulation based systems and autonomous multi-agent control are addressed in this thesis. In subband coding, the number of bits used (out of a target bit budget) to code a sub- band signal are allocated in a way that minimizes the coding distortion. In Discrete Multi-tone modulation based systems, high bit rate streams are split into several parallel lower rate streams. These individual data streams are transmitted over different subchannels. Given a target bit rate, the goal of resource allocation is to distribute the bits among the different subchannels such that the total transmitted power is minimized. The last problem is achieving stable control of a fleet of autonomous agents by utilizing the available communication resources (such as transmitted Power and bandwidth) as effectively as possible. We present an efficient bit loading algorithm that applies to both subband coding and single-user multicarrier communication system. The goal is to effect an optimal distribution of B bits among N subchannels (subbands) to achieve a minimum transmitted power (distortion error variance) for multicarrier (subband coding) systems. All the algorithms in literature, except a few (which provides a suboptimal solution), have run times that increase with B. By contrast, we provide an algorithm that solves the aforementioned problems exactly and with a complexity (given by O(N log(N)),) which is dependent only on N. Bit loading in multi-user multicarrier systems not only involves the distribution of bit rates across the subchannels but also the assignment of these subchannels to different users. The motivation for studying suboptimal bit allocation is underscored by implicit and explicit claims made in some of the papers which present suboptimal bit loading algorithms, without a formal proof, that the underlying problem is NP-hard. Consequently, for no other reason than the sake of completeness, we present a proof for NP-hardness of the multiuser multicarrier bit loading problem, thereby formally justifying the search for suboptimal solutions. There has been a growing interest in the area of cooperative control of networks of mobile autonomous agents. Applications for such a set up include organization of large sensor networks, air traffic control, achieving and maintaining formations of unmanned vehicles operating under- water, air traffic control etc. As in Abel et al, our goal is to devise control laws that, require minimal information exchange between the agents and minimal knowledge on the part of each agent of the overall formation objective, are fault tolerant, scalable, and easily reconfigurable in the face of the loss or arrival of an agent, and the loss of a communication link. A major drawback of the control law proposed in Abel et al is that it assumes all agents can exchange information at will. This is fine if agents acquire each others state information through straightforward sensing. If however, state information is exchanged through broadcast commu- nication, this assumption is highly unrealistic. By modifying the control law presented in Abel et al, we devise a scheme that allows for a sharing of the resource, which is the communication channel, but also achieves the desired formation stably. Accordingly we modify the control law presented in [23] to be compatible with networks constrained by MAC protocols.

Soft robots are inherently safer than traditional robots due to their compliance and high power density ratio resulting in lower accidental impact forces. Thus they are a natural option for human-robot interaction. This thesis specifically looked at human-robot co-manipulation which is defined as a human and a robot working together to move an object too large or awkward to be safely maneuvered by a single agent. To better understand how humans communicate while co-manipulating an object, this work looked at haptic interaction of human-human dyadic co-manipulation trials and studied some of the trends found in that interaction. These trends point to ways robots can effectively work with human partners in the future. Before successful human-robot co-manipulation with large-scale soft robots can be achieved, low-level joint angle control is needed. Low-level model predictive control of soft robot joints requires a sufficiently accurate model of the system. This thesis introduces a recursive Newton-Euler method for deriving the dynamics that is sufficiently accurate and accounts for flexible joints in an intuitive way. This model has been shown to be accurate to a median absolute error of 3.15 degrees for a three-link three-joint six degree of freedom soft robot arm. Once a sufficiently accurate model was developed, a gain-based evolutionary model predictive control (MPC) technique was formulated based on a previous evolutionary MPC technique. This new method is referred to as model evolutionary gain-based predictive control or MEGa-PC. This control law is compared to nonlinear evolutionary model predictive control (NEMPC). The new technique allows intentionally decreasing the control frequency to 10 Hz while maintaining control of the system. This is proven to help MPC solve more difficult problems by having the ability to extend the control horizon. This new controller is also demonstrated to work well on a three-joint three-link soft robot arm. Although complete physical human-robot co-manipulation is outside the scope of this thesis, this thesis covers three main building blocks for physical human and soft robot co-manipulation: human-human haptic communication, soft robot modeling, and model evolutionary gain-based predictive control.

We present a motion control framework which allows a group of robots to work together to decide upon their motions by minimizing a collective cost without any central computing component or any one agent performing a large portion of the computation. When developing distributed control algorithms, care must be taken to respect the limited computational capacity of each agent as well as respect the information and communication constraints of the network. To address these issues, we develop a distributed, behavior-based model predictive control (MPC) framework which alleviates the computational difficulties present in many distributed MPC frameworks, while respecting the communication and information constraints of the network. In developing the multi-agent control framework, we make three contributions. First, we develop a distributed optimization technique which respects the dynamic communication restraints of the network, converges to a collective minimum of the cost, and has transients suitable for robot motion control. Second, we develop a behavior-based MPC framework to control the motion of a single-agent and apply the framework to robot navigation. The third contribution is to combine the concepts of distributed optimization and behavior-based MPC to develop the mentioned multi-agent behavior-based MPC algorithm suitable for multi-robot motion control.

<div>In this thesis, we focus on the problem of estimating an unknown quantity of interest, when the information required to do so is dispersed over a network of agents. In particular, each agent in the network receives sequential observations generated by the unknown quantity, and the collective goal of the network is to eventually learn this quantity by means of appropriately crafted information diffusion rules. The abstraction described above can be used to model a variety of problems ranging from environmental monitoring of a dynamical process using autonomous robot teams, to statistical inference using a network of processors, to social learning in groups of individuals. The limited information content of each agent, coupled with dynamically changing networks, the possibility of adversarial attacks, and constraints imposed by the communication channels, introduce various unique challenges in addressing such problems. We contribute towards systematically resolving some of these challenges.</div><div><br></div><div>In the first part of this thesis, we focus on tracking the state of a dynamical process, and develop a distributed observer for the most general class of LTI systems, linear measurement models, and time-invariant graphs. To do so, we introduce the notion of a multi-sensor observable decomposition - a generalization of the Kalman observable canonical decomposition for a single sensor. We then consider a scenario where certain agents in the network are compromised based on the classical Byzantine adversary model. For this worst-case adversarial setting, we identify certain fundamental necessary conditions that are a blend of system- and network-theoretic requirements. We then develop an attack-resilient, provably-correct, fully distributed state estimation algorithm. Finally, by drawing connections to the concept of age-of-information for characterizing information freshness, we show how our framework can be extended to handle a broad class of time-varying graphs. Notably, in each of the cases above, our proposed algorithms guarantee exponential convergence at any desired convergence rate.</div><div><br></div><div>In the second part of the thesis, we turn our attention to the problem of distributed hypothesis testing/inference, where each agent receives a stream of stochastic signals generated by an unknown static state that belongs to a finite set of hypotheses. To enable each agent to uniquely identify the true state, we develop a novel distributed learning rule that employs a min-protocol for data-aggregation, as opposed to the large body of existing techniques that rely on "belief-averaging". We establish consistency of our rule under minimal requirements on the observation model and the network structure, and prove that it guarantees exponentially fast convergence to the truth with probability 1. Most importantly, we establish that the learning rate of our algorithm is network-independent, and a strict improvement over all existing approaches. We also develop a simple variant of our learning algorithm that can account for misbehaving agents. As the final contribution of this work, we develop communication-efficient rules for distributed hypothesis testing. Specifically, we draw on ideas from event-triggered control to reduce the number of communication rounds, and employ an adaptive quantization scheme that guarantees exponentially fast learning almost surely, even when just 1 bit is used to encode each hypothesis. </div>

No description available.

Thesis (MScEng (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: This work considers the control of reconfigurable assembly systems using a welding assembly system as a case study. The assembly system consists of a pallet magazine, a feeding system, an inspection and removal system, a welding system and a conveyor. The aim of the work is to compare PC and PLC as controllers, as well as to compare two different approaches to reconfigurable control. The control system of the pallet magazine was developed using a PC and a PLC. The PC control was programmed using Visual C#, while the PLC was programmed in Ladder Logic using Siemens S-300 STEP7. The two controllers were compared based on the attributes that measure the quality of a controller's software, which include its capability, availability, usability and adaptability. The approaches to reconfigurable control considered were the agent-based methodology and the IEC 61499 distributed control methodology, both of which were applied to the feeding system. The agent-based control system was implemented using the JADE agent platform, while the IEC 61499 distributed control system was implemented using the FBDK software kit. These two methods were compared based on the characteristics of a reconfigurable system, which include the system's modularity, integrability, convertibility, diagnosability, customization and scalability. The result obtained in comparing the PC to the PLC shows that the PLC performs better in terms of capability, availability and usability, while the PC performs better in terms of adaptability. Also, the result of the comparison between the agent-based control system and the IEC 61499 distributed control system shows that the agent-based control system performs better in terms of integrability, diagnosability and scalability, while the IEC 61499 distributed control system performs better in terms of modularity and customization. They are, however, on a par in terms of convertibility. / AFRIKAANSE OPSOMMING: Hierdie werk beskou die beheer van herkonfigureerbare monteringstelsels met 'n sweismonteringstelsel as gevallestudie. Die monteringstelsel bestaan uit 'n paletmagasyn, 'n voerstelsel, 'n inspeksie-en- verwyderingstelsel, 'n sweisstelsel en 'n voerband. Die mikpunt van die werk is om persoonlike rekenaars (PCs) en programmeerbare-logikabeheerders (PLCs) as beheerders te vergelyk, asook om twee verskillende benaderings tot herkonfigureerbare beheer te vergelyk. Die beheerstelsel van die paletmagasyn is ontwikkel met 'n PC en 'n PLC. Die PC-beheer is in Visual C# geprogrammeer, terwyl die PLC in leerlogika met Siemens S-300 STEP7 geprogrammeer is. Die twee beheerders is vergelyk in terme van die eienskappe wat die kwaliteit van 'n beheerder se sagteware meet en sluit in vermoë, beskikbaarheid, bruikbaarheid en aanpasbaarheid. Die benaderings tot herkonfigureerbare beheer wat oorweeg is, is die agent-gebaseerde metodologie en die IEC 61499 verspreide-beheermetodologie. Beide is op die voerstelsel toegepas. Die agent-gebaseerde beheerstelsel is geïmplementeer met behulp van die JADE agent-platform, terwyl die IEC 61499 verspreide stelsel geïmplementeer is met behulp van die FBDK sagteware-stel. Hierdie twee metodes se vergelyking is gebaseer op die eienskappe van 'n herkonfigureerbare stelsel, waarby die stelsel se modulariteit, integreerbaarheid, diagnoseerbaarheid, pasmaakbaarheid en skaleerbaarheid ingesluit is. Die resultate wat in die vergelyking tussen die PC en PLC verkry is, toon dat die PLC beter vaar in terme van vermoë, beskikbaarheid en bruikbaarheid, terwyl die PC beter vaar in terme van aanpasbaarheid. Die resultaat van die vergelyking tussen die agent-gebaseerde beheerstelsel en die IEC 61499 verspreide beheerstelsel wys dat die agent-gebaseerde beheerstelsel beter vaar in terme van integreerbaarheid, diagnoseerbaarheid en skaleerbaarheid, terwyl die IEC 61499 verspreide beheerstelsel beter vaar in terme van modulariteit en pasmaakbaarheid. Hulle is egter vergelykbaar in terme van omskepbaarheid.

Swarm robotics for music is a relatively new way to explore algorithmic composition as well as new modes of human robot interaction. This work outlines a strategy for making music with a robotic swarm constrained by acoustic sound, rhythmic music using sequencers, motion causing changes in the music, and finally human and swarm interaction. Two novel simulation programs are created in this thesis: the first is a multi-agent simulation designed to explore suitable parameters for motion to music mappings as well as parameters for real time interaction. The second is a boid-based robotic swarm simulation that adheres to the constraints established, using derived parameters from the multi-agent simulation: orientation, number of neighbors, and speed. In addition, five interaction modes are created that vary along an axis of direct and indirect forms of human control over the swarm motion. The mappings and interaction modes of the swarm robot simulation are evaluated in a user study involving music technology students. The purpose of the study is to determine the legibility of the motion to musical mappings and evaluate user preferences for the mappings and modes of interaction in problem solving and in open-ended contexts. The findings suggest that typical users of a swarm robot system do not necessarily prefer more inherently legible mappings in open-ended contexts. Users prefer direct and intermediate modes of interaction in problem solving scenarios, but favor intermediate modes of interaction in open-ended ones. The results from this study will be used in the design and development of a new swarm robotic system for music that can be used in both contexts.

Orientadores: Celso Pascoli Bottura / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-11T03:59:13Z (GMT). No. of bitstreams: 1 Costa_EliezerArantesda_D.pdf: 4514909 bytes, checksum: 426e6a3cf7c672569a8fd38adb1148a2 (MD5) Previous issue date: 2008 / Resumo: O objetivo desta pesquisa é o desenvolvimento de um novo modelo conceitual para representação, de uma forma integrada, das mais diversas situações de conflito de interesses, como uma base para análise e projeto de controle de sistemas hierárquicos multiagentes, e para o aprimoramento da metodologia de capacitação de executivos para a administração estratégica desses conflitos. O método utilizado para esta pesquisa foi um investigação das condicionantes análogas presentes nos quatro jogos clássicos da Teoria dos Jogos ¿ Nash, Stackelberg, Pareto e Minimax ¿ buscando, entre eles, as características semelhantes e as peculiaridades que realmente os fazem semelhantes e distintos entre si. Desta investigação, foram identificadas duas dimensões diferenciadoras, que possibilitaram a concepção e a construção de uma matriz para representação desses jogos, de uma forma integrada. O modelo conceitual resultante desta pesquisa fornece um esquema analítico abrangente, inspirado na teoria dos jogos, e é usado para explicar, descrever, interpretar e prever os comportamentos dos diversos agentes autônomos envolvidos em situações de conflito de interesses e, em certos casos, prescrever decisões mais adequadas. A Matriz de Jogos Estratégicos (MJE), proposta e utilizada neste trabalho, estabelece um quadro de referência conceitual, mapeando seis diferentes tipos de jogos. Nela, os pressupostos dos modelos clássicos de jogos citados, e outros em casos-limite, são usados de forma integrada e complementar. São tratados, na MJE, jogos competitivos e cooperativos, jogos equilibrados e não-equilibrados, levando em conta tanto a postura concorrencial de cada jogador como o seu pressuposto de relação-de-forças. A MJE contempla de forma inovativa o tratamento de múltiplos subjogos estratégicos simultâneos entre os agentes envolvidos. A aplicação dos conceitos da MJE a complexos sistemas ¿ hierárquicos ou não ¿, com múltiplos agentes inteligentes interativos autônomos, provê uma metodologia de utilidade para análise e projeto de estratégias de controle. Parte importante deste estudo é a realização de experimentos exploratórios com propósito pedagógico. Esses jogos de empresa, realizados via computador, indicam que os participantes ampliam sua percepção para compreender os diversos jogos a jogar, e sua habilidade para atuar em cada um deles. Este uso da MJE leva cada participante a analisar situações de conflito de interesses e a melhor escolher suas decisões estratégicas: Através de um processo dinâmico interativo de tentativa e erro, ele ou ela acaba aprendendo a tomar melhores decisões, levando em conta as possíveis decisões dos demais agentes envolvidos bem como sua avaliação das conseqüências de suas escolhas. Palavras-chave: Matriz de Jogos Estratégicos; teoria dos jogos; jogos estratégicos; jogos de empresas; controle multiagente; gestão de conflitos; planejamento estratégico; gestão estratégica; simulação de empresas; treinamento gerencial; jogos hierárquicos / Abstract: The objective of the current research is the development of a new conceptual model aiming to represent, in an integrated manner, the many situations of conflict of interests as a basis for analysis and design of hierarchical multiagent systems control and for the improvement of the methodology for betterment of the managers¿ skills to deal with the strategic management of such conflicts. The investigation method used was a comparative analysis of the unique characteristics of classical games from Game Theory ¿ Nash, Stackelberg, Pareto, and Minimax ¿ searching, among them, their commonalities and differentiations. This investigation identified two distinct dimensions that enabled the conception and construction of a matrix to represent, in a integrated form, those four games mentioned above. The resulting conceptual model provides a comprehensive analytical scheme, inspired in the theory of games, and is used to explain, describe, interpret and forecast behaviors of autonomous agents involved in situations of conflict of interests and, in some cases, to prescribe the more adequate decisions. The Strategic Games Matrix (SGM) proposed and used in this study establishes a conceptual reference framework mapping six different types of games. In it, the assumptions for classic game models, among others for limitcases, are used in an integrated and complementary manner. The SGM deals with both competitive and cooperative games, as well as balanced and unbalanced ones, taking into consideration both the players' competitive postures and the powerratio assumed by each one. The SGM contemplates in an innovative way the treatment of multiple simultaneous strategic sub-games among the agents involved. The application of the SGM concepts to complex systems ¿ hierarchical or not ¿, with multiple autonomous intelligent interactive agents, provides a methodology of utility for analysis and design of their control strategies. An important part of this study is the exploratory experiments with pedagogical purpose. Such business games, played in a computer, indicate that the participants increase their perception to understand the various games to play, and their ability to act at each one of them. This use of the SGM leads each participant to analyze conflict of interests¿ situations and to improve its strategic decisions: Through an interactive dynamic process of trial and error he/she ends up learning how to make better decisions taking into consideration the likely decisions of the other agents involved as well as her/his evaluation of the consequences of their choices. Keywords: Strategic Games Matrix; game theory; strategic games; business games; multi-agent control; conflict management; strategic planning; strategic management; business simulation; managers training; hierarchical games / Doutorado / Automação / Doutor em Engenharia Elétrica

Smart cities embrace cutting-edge technologies to improve transportation efficiency and safety. With the rollout of 5G and an ever-growing network of connected infrastructure sensors, real-time road condition awareness is becoming a reality. However, this progress brings new challenges. The communication and vast amounts of data generated by autonomous vehicles and the connected infrastructure must be navigated. Furthermore, different levels of autonomous driving (ranging from 0 to 5) are rolled out gradually and human-driven vehicles will continue to share the roads with autonomous vehicles for some time. In this work, we apply a data-driven control scheme called Learning Model Predictive Control (LMPC) to three different smart city scenarios of increasing complexity. Given a successful execution of a scenario, LMPC uses the trajectory data from previous executions to improve the performance of subsequent executions while guaranteeing safety and recursive feasibility. Furthermore, the performance from one execution to another is guaranteed to be non-decreasing. For our three smart-city scenarios, we apply a minimum time objective and start with a single vehicle in a two-lane intersection. Then, we add an obstacle on the lane of the ego vehicle, and lastly, we add oncoming traffic. We find that LMPC gives us improved traffic efficiency with shorter travel. However, we find that LMPC may not be suitable for real-time training in smart city scenarios. Thus, we conclude that this approach is suitable for simulator-driven, offline, training on any trajectory data that might be generated from autonomous vehicles and the infrastructure sensors in future smart cities. Over time, this can be used to construct large data sets of optimal trajectories which are available for the connected vehicles in most urban scenarios. / Smarta städer använder modern teknik för att förbättra transporteffektiviteten och säkerheten. Med införandet av 5G och ett allt större nätverk av uppkopplade sensorsystem för infrastruktur blir realtidsmedvetenhet om vägförhållandena en verklighet. Denna utveckling medför nya utmaningar. Kommunikationen mellan autonoma fordon och uppkopplade sensorsystem ger upphov till stora mängder data som måste hanteras. Dessutom kommer fordon med olika autocnominivåer (från 0 till 5) att behöva dela gatorna tillsammans med människostyrda fordon samtidigt under en tid. I detta arbete tillämpar vi en datadriven reglermetod som heter Learning Model Predictive Control (LMPC) på tre olika scenarier i en smart stad med ökande komplexitet. LMPC utnyttjar data från en tidigare lyckad körning av ett visst scenario för att förbättra prestandan på efterföljande körningar samtidigt som säkerheten och rekursiv genomförbarhet garanteras. Vidare garanteras att prestandan från en körning till en annan inte minskar. För våra tre scenarier är målet att minimerar restiden och börjar med ett enda fordon i en tvåfilig korsning. Sedan lägger vi till ett hinder på högra filen och till sist lägger vi till mötande trafik. Vi finner att LMPC ger oss förbättrad trafikeffektivitet med kortare restid. Vi finner dock att LMPC må vara mindre lämplig för realtids scenarier. Således drar vi slutsatsen att denna metod är lämplig för optimering i simulatorer, offline, på data som kan genereras från autonoma fordon och sensorsystemet i infrastrukturen. Så småningom kan vår metod användas för att konstruera stora dataset av optimala trajektorier som är tillgängliga för uppkopplade fordon i framtidens smarta städer.