Multi-robot task assignment (allocation) involves assigning robots to tasks in order to optimize the entire team’s performances. Until now, one of the most useful non-domain-specific ways to coordinate multi-robot systems is through task allocation mechanisms. This dissertation addresses the classic task assignment problems in which robots and tasks are eventually matched by forming a one-to-one mapping, and their overall performances (e.g., cost, utility, and risk) can be linearly summed. At a high level, this research emphasizes two facets of the multi-robot task assignment, including (1) novel extensions from classic assignment algorithms, and (2) completely newly designed task allocation methods with impressive new features. For the former, we first propose a strongly polynomial assignment sensitivity analysis algorithm as well as a means to measure the assignment uncertainties; after that we propose a novel method to address problems of multi-robot routing and formation morphing, the trajectories of which are obtained from projections of augmenting paths that reside in a new three-dimensional interpretation of embedded matching graphs. For the latter, we present two optimal assignment algorithms that are distributable and suitable for multi-robot task allocation problems: the first one is an anytime assignment algorithm that produces non-decreasing assignment solutions along a series of task-swapping operations, each of which updates the assignment configurations and thus can be interrupted at any moment; the second one is a new market-based algorithm with a novel pricing policy: in contrast to the buyers’ “selfish” bidding behaviors in conventional auction/market-based approaches, we employ a virtual merchant to strategically escalate market prices in order to reach a state of equilibrium that satisfies both the merchant and buyers. Both of these newly developed assignment algorithms have a strongly polynomial running time close to the benchmark algorithms but can be easily decentralized in terms of computation and communication.

Orientador: Prof. Dr. Wagner Tanaka Botelho / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Ciência da Computação, 2017. / Os avanços tecnológicos realizados na robótica móvel ao longo do tempo requereram o estudo e desenvolvimento de robôs cada vez mais autônomos e complexos, capazes de se adaptarem aos ambientes e condições que lhe são impostas. Contudo, dependendo do objetivo a alcancar, torna-se mais efetivo a utilização de uma maior quantidade de robos menores e mais simples, com capacidade cooperativa, resultando em um sistema escalavel e menos suscetývel a falhas gerais, denominado Sistema Multirrobotico (SMR). Tendo um SMR como objeto de estudo principal, este trabalho consiste no desenvolvimento de uma arquitetura multirrobotica descentralizada para o controle de posição e formação utilizando quadricopteros. A arquitetura é composta por n quadricopteros virtuais, implementados no software de simulação Gazebo e um quadricoptero real. O Robot Operating System (ROS) controla todos os quadricopteros, alem de gerenciar a comunicação entre os agentes roboticos. Um ponto importante é que, visando a diminuição dos custos do projeto, foi utilizado apenas um quadricoptero real, uma vez que somente um é necessário para validar a integração entre os mundos virtual e real. Para o controle de posição e formação foram propostos modelos matematicos que determinam as trajetorias dos n quadricopteros em formação linear, formação de figuras poligonais com troca de posição e formação de figuras poligonais com troca de posição e ponto de referencia movel. Nas simulações, foi possivel observar o deslocamento dos quadic'opteros em formação, validando os modelos matematicos. Por'em, no experimento real, a trajetoria no controle de formação foi parcialmente observada devido a alguns problemas apresentados na estrutura do quadricoptero e tambem por não possuir um sistema de sensoriamento no ambiente real. Apesar desses problemas, a integração entre os mundos virtual e real também foi validada. / The technological advances made in mobile robotics over time have required the study and development of robots that are increasingly autonomous and complex, capable of adapting to the environments and conditions that are imposed on them. However, depending on the goal to be achieved, it becomes more e.ective to use a larger number of smaller and simpler robots with cooperative capability, resulting in a scalable system that is less susceptible to general failures, called Multi-Robot Systems (MRS). Considering a MRS as the main study, the main target in this work is to develop a descentralized multi-robot architecture for position control using quadcopters. The architecture consists of n virtual quadcopters, implemented on the Gazebo simulation software and a real quadcopter. The Robot Operating System (ROS) controls all quadcopters as well as managing communication between them. In order to reduce the project costs, only one real quadcopter was used, since it is enough to validate the integration between the virtual and real worlds. The mathematical models were proposed to calculate the paths of the quadcopters in linear formation, formation of polygonal figures with rotation and formation of polygonal figures with rotation and mobile reference point. In the simulations, it was possible to observe the displacement of the quadcopters in formation, validating the mathematical models. However, in the real experiment, the trajectory in the formation control was partially observed due to some limitations presented on the quadcopter structure. Also, the sensing system was not available in the real environment. Despite these problems, the integration between the virtual and real worlds has also been validated.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-01-07T17:45:41Z No. of bitstreams: 1 claudiomaiaalvesjose.pdf: 3912964 bytes, checksum: 205414dab15935347aeda610f25295b9 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-01-25T16:53:03Z (GMT) No. of bitstreams: 1 claudiomaiaalvesjose.pdf: 3912964 bytes, checksum: 205414dab15935347aeda610f25295b9 (MD5) / Made available in DSpace on 2016-01-25T16:53:03Z (GMT). No. of bitstreams: 1 claudiomaiaalvesjose.pdf: 3912964 bytes, checksum: 205414dab15935347aeda610f25295b9 (MD5) Previous issue date: 2015-02-20 / Múltiplos robôs autônomos executam tarefas que necessitam de uma cooperação entre os mesmos. Este trabalho trata um problema de coordenação de robôs móveis terrestres, os quais estão divididos em dois grupos: os robôs de serviço e os robôs de recarga. Os múltiplos robôs de serviço devem percorrer caminhos fechados que se interceptam em determinados pontos. Um controlador de alto nível atua diretamente nas velocidades médias destes veículos, evitando possíveis colisões e garantindo a segurança em suas tarefas. Os caminhos são percorridos em ciclos, os quais devem ser comensuráveis e de caráter persistente, ou seja, executados num horizonte de planejamento “infinito”, o que ultrapassa as limitações de cargas de suas baterias. Para isso, é introduzido um grupo de robôs dedicados que atua no processo de troca das baterias, tarefa esta designada aos robôs de recarga. A estratégia utilizada para que todos os robôs de serviço sejam recarregados é baseada em grafo. A coordenação dos múltiplos robôs (serviço e recarga) é resolvida por meio de dois otimizadores, ambos implementados com o solver LINGO, integrados ao ROS (Robot Operating System) utilizando a linguagem C++. Um dos otimizadores coordena o movimento dos robôs de serviço com o objetivo de evitar colisões entre os mesmos. Os resultados gerados nesta primeira etapa de otimização, são utilizados para que os dois grupos robóticos estejam em sintonia durante o processo de recarga. Os caminhos percorridos pelos robôs de serviço são constituídos de pontos nos quais podem ocorrer o contato destes com os robôs de recarga. Desta maneira, a segunda parte da otimização consiste em determinar os caminhos ótimos a serem traçados pelos robôs de recarga. Este problema é resolvido por meio de um programa linear inteiro misto (MILP), o qual tem por objetivo minimizar o tempo global para a tarefa de recarga. / Autonomous multi-robots can be used to perform tasks that require cooperation between them. This work consists at the coordination problem of land mobile robots, divided into two groups: working robots and recharge robots. The working multi-robots must travel by closed paths that intersect each other. A high-level controller acts directly on average speeds of these vehicles, avoiding possible collisions and ensuring security in their tasks. The paths are traversed in cycles, which should be commensurate and persistents, executed on a infinite planning horizon, which overcomes the limitations of charges your batteries. For this is added one dedicated group of robots that operates in the battery exchange process. This group is called recharge robots. The strategy used to recharge all working robots is based on graph. The coordination of multiples robots is resolved through two optimizers, both implemented with LINGO solver, integrated with ROS (Robot Operating System) using the C++ language. One of optmizers coordinates the movement of working robots in order to avoid collisions between them. The results obtained in this first optimization step are used for the two robotic groups, became they must remain in line during the charging process. Thus, the second part of the optimization is to determine the optimum paths to be traced by the recharge robots. This problem is solved by a mixed integer linear program (MILP), with the objective of minimizing the overall time for recharging task.

With the promise to shape the future of industry, multi-agent robotic technologies have the potential to change many aspects of daily life. Over the coming decade, they are expected to impact transportation systems, military applications such as reconnaissance and surveillance, search-and-rescue operations, or space missions, as well as provide support to emergency first responders. Motivated by the latest developments in the field of robotics, this thesis contributes to the evolution of the future generation of multi-agent robotic systems as they become smarter, more accurate, and diversified in terms of applications. But in order to achieve these goals, the individual agents forming cooperative robotic systems need to be specialized in what they can accomplish, while ensuring accuracy and preserving the ability to perform diverse tasks. This thesis addresses the problem of task allocation in swarm robotics in the specific context where specialized capabilities of the individual agents are considered. Based on the assumption that each individual agent possesses specialized functional capabilities and that the expected tasks, which are distributed in the surrounding environment, impose specific requirements, the proposed task allocation mechanisms are formulated in two different spaces. First, a rudimentary form of the team members’ specialization is formulated as a cooperative control problem embedded in the agents’ dynamics control space. Second, an advanced formulation of agents’ specialization is defined to estimate the individual agents’ task allocation probabilities in a dedicated specialization space, which represents the core contribution of this thesis to the advancement and practice in the area of swarm robotics. The original task allocation process formulated in the specialization space evolves through four stages of development. First, a task features recognition stage is conceptually introduced to leverage the output of a sensing layer embedded in robotic agents to drive the proposed task allocation scheme. Second, a matching scheme is developed to best match each agent’s specialized capabilities with the corresponding detected tasks. At this stage, a general binary definition of agents’ specialization serves as the basis for task-agent association. Third, the task-agent matching scheme is expanded to an innovative probabilistic specialty-based task-agent allocation framework to generalize the concept and exploit the potential of agents’ specialization consideration. Fourth, the general framework is further refined with a modulated definition of the agents’ specialization based on their mechanical, physical structure, and embedded resources. The original framework is extended and a prioritization layer is also introduced to improve the system’s response to complex tasks that are characterized based on the recognition of multiple classes. Experimental validation of the proposed specialty-based task allocation approach is conducted in simulation and on real-world experiments, and the results are presented and discussed in light of potential applications to demonstrate the effectiveness and efficiency of the proposed framework.

The security of multi-robot systems is critical in various applications such as patrol, transportation, and search and rescue operations, where they face threats from adversaries attempting to gain control of the robots. These compromised robots are significant threats as they allow attackers to steer robots towards forbidden areas without being detected, potentially causing harm or compromising the mission. To address this problem, we propose a resilient planning, task assignment, and control framework. The proposed framework builds a multi-robot plan where robots are designed to get close enough to other robots according to a co-observation schedule, in order to mutually check for abnormal behaviors. For the first part of the thesis, we propose an optimal trajectory solver based on the alternating direction method of multipliers (ADMM) to generate multi-agent trajectories that satisfy spatio-temporal requirements introduced by the co-observation schedules. As part of the formulation, we provide a new reachability constraint to guarantee that, despite adversarial movement by the attacker, a compromised robot cannot reach forbidden areas between co-observations without being detected. In the second part of the thesis, to further enhance the system's performance, reliability, and robustness, we propose to deploy multiple robots on each route to form sub-teams. A new cross-trajectory co-observation scheme between sub-teams is introduced that preserves the optimal unsecured trajectories. The new planner ensures that at least one robot in each sub-team sticks to the planned trajectories, while sub-teams can constantly exchange robots during the task introducing additional co-observations that can secure originally unsecured routes. We show that the planning of cross-trajectory co-observations can be transformed into a network flow problem and solved using traditional linear program technique. In the final part of the thesis, we show that the introduction of sub-teams also improves the multi-robot system's robustness to unplanned situations, allowing servicing unplanned online events without breaking the security requirements. This is achieved by a distributed task assignment algorithm based on consensus ADMM which can handle tasks with different priorities. The assignment result and security requirements are formulated as spatio-temporal schedules and guaranteed through control barrier function (CBF) based controls.

The field of robotics has witnessed significant advancements in recent years, with robotic arms playing a pivotal role in various industrial and research applications. In large-scale manufacturing, manual labour has been replaced with robots due to their efficiency in time and cost. However, in order to replace human labour, the robots need to collaborate in a way that humans do. This master's thesis, conducted at the Cyber-physical Systems Lab (CPS-Lab) at Uppsala University, delves into the intricacies of cooperative interactions between two homogenous robotic arms powered by machine learning algorithms, aiming to explore their collective capabilities. The project will focus on implementing a multi-agent cart-pole experiment that will challenge the two robotic arms' cooperative abilities. First, the problem is simulated, and afterwards implemented in real life. The experiment will be evaluated by the performance of various tested machine learning algorithms. In the end, The simulation yielded poor results due to the complexity of the problem and the lack of proper hyperparameter tuning. The real life experiment failed instantly, caused by the robotic arms not being designed for this application, a large simulation gap, and latency in the controller design. Overall, the results show that the experiment was challenging for the robotic arms, but that it might be possible under different circumstances.

O contínuo aumento da complexidade no controle de sistemas robóticos, bem como a aplicação de grupos de robôs auxiliando ou substituindo seres humanos em atividades críticas tem gerado uma importante demanda por soluções mais robustas, flexíveis, e eficientes. O desenvolvimento convencional de algoritmos especializados, constituídos de sistemas baseados em regras e de autômatos usados para coordenar estes conjuntos físicos em um ambiente dinâmico é um desafio extremamente complexo. Diversos modelos de desenvolvimento existem, entretanto, muitos desafios da área da robótica móvel autônoma continuam em aberto. Esta tese se insere no contexto da busca por soluções inteligentes a serem aplicadas em robôs móveis autônomos com o objetivo de permitir a operação destes em ambientes dinâmicos. Buscamos, com a investigação e aplicação de estratégias inteligentes por meio de aprendizado de máquina no funcionamento dos robôs, a proposta de soluções originais que permitam uma nova visão sobre a operação de robôs móveis em três dos desafios da área da robótica móvel autônoma, que são: localização, navegação e operações com grupos de robôs. As pesquisas sobre localização e coordenação de grupos apresentam investigação e propostas originais, buscando estender o estado da arte, onde apresentam resultados inovadores. A parte sobre navegação tem como objetivo principal ser um elo entre os conceitos de localização e coordenação de grupos, sendo o foco o desenvolvimento de um veículo autônomo com maior implicação em avanços técnicos. Relacionado com a coordenação de grupos de robôs, fizemos a escolha de trabalhar sobre uma aplicação modelada como o problema de combate a incêndios florestais. Buscamos desenvolver um ambiente de simulação realístico, onde foram avaliadas quatro técnicas para busca de iii estratégias de formação do grupo: Algoritmos Genéticos, Otimização por Enxame de Partículas, Hill Climbing e (iv) Simulated Annealing. Com base nas diversas avaliações realizadas pudemos mostrar quais das técnicas e conjuntos de parâmetros permitem a obtenção de resultados mais acurados que os demais. Além disso, mostramos como uma heurística baseada em populações anteriores pode auxiliar na tolerância a falhas da operação. Relacionado com a tarefa de navegação, apresentamos o desenvolvimento de um veículo autônomo de grande porte funcional para ambientes externos. Buscamos aperfeiçoar uma arquitetura para navegação autônoma, baseada em visão monocular e com capacidade de seguir pontos esparsos de GPS. Mostramos como a simulação e os usos de robôs de pequeno porte auxiliaram no desenvolvimento do veículo de grande porte e apresentamos como as redes neurais podem ser aplicadas nos modelos de navegação autônoma. Na investigação sobre localização, mostramos um método utilizando informação obtida de redes sem fio para prover informação de localização para robôs móveis. As informações obtidas da rede sem fio são utilizadas para aprendizado da posição de um robô móvel por meio de uma rede neural. Diversas avaliações foram realizadas buscando entender o comportamento do sistema com diferentes números de pontos de acesso, com uso de filtros, com diferentes topologias. Os resultados mostram que o modelo usando redes sem fio pode ser um possível método prático e barato para localização de robôs móveis. Esta tese aborda temas relevantes e propostas originais relacionadas com os objetivos propostos, apresentando métodos que provenham autonomia na coordenação de grupos e nas atividades individuais dos mesmos. A busca por altos graus de eficiência na resolução de tarefas em ambientes dinâmicos ainda é um campo que carece de soluções e de um aprofundamento nas pesquisas. Sendo assim, esta pesquisa buscou agregar diversos avanços científicos na área de pesquisa de robôs móveis autônomos e coordenação de grupos, por meio da aplicação de estratégias inteligentes / The constant increasing of the complexity in the control of robotic systems, as well as the application of groups of robots assisting or replacing human beings in critical activities has generated a significant demand for more robust, flexible and efficient solutions. The conventional development of specialized algorithms consisted of rule-based systems and automatas, used to coordinate these physical sets in a dynamic environment is an extremely complex challenge. Although several models of development of robotic issues are currently in use, many challenges in the area remain open. This thesis is related to the search for intelligent strategies to be applied in autonomous mobile robots in order to allow practical operations in dynamic environments. We seek, with the investigation of intelligent strategies by means of the use of machine learning in the robots, to propose original solutions to allow contributions in three challenges of the robotic research area: localization, navigation and coordination of groups of robots. The investigations about localization and groups of robots show novel and original proposals, where we sought to extend the state of the art. The navigation part has as its major objective to be a link between the subjects of localization and navigation, being its aim to help the deployment of a autonomous vehicle implying in greater technical advances. Related to the robotic group coordination, we have made the choice to work on an application modeled as a wildfire combat operation. We have developed a simulation environment in which we have evaluated four techniques to obtain strategies for the group formation: genetic algorithms, particle swarm optimization, hill climbing and simulated annealing. The v results showed that we can have very different accuracy with different techniques and sets of parameters. Furthermore, we show how a heuristic based on the use of past populations can assist in fault tolerant operation. Related to the autonomous navigation task, we present the development of a large autonomous vehicle capable of operating in outdoor environments. We sought to optimize an architecture for autonomous navigation based on monocular vision and with the ability to follow scattered points of GPS.We show how the use of simulation and small robots could assist in the development of large vehicle. Furthermore, we show how neural networks can be applied as a controller to autonomous navigation systems. In the investigation about localization, we presented a method using wireless networks to provide information about localization to mobile robots. The information gathered by the wireless network is used as input in an artificial neural network which learns the position of the robot. Several evaluations were carried out in order to understand the behavior of the proposed system, as using different topologies, different numbers of access points and the use of filters. Results showed that the proposed system, using wireless networks and neural networks, may be a useful and easy to use solution for localization of mobile robots. This thesis has addressed original and relevant topics related to the proposed objectives, showing methods to allow degrees of autonomy in robotic operations. The search for higher degrees of efficiency in tasks solving in dynamic environments is still a field that lacks solutions. Therefore, this study sought to add several scientific contributions in the autonomous mobile robots research area and coordination of groups, by means of the application of intelligent strategies

Even with the advance of autonomous robotics and automation, many automated tasks still require human intervention or guidance to mediate uncertainties in the environment or to execute the complexities of a task that autonomous robots are not yet equipped to handle. As such, robot controllers are needed that utilize the strengths of both autonomous agents, adept at handling lower level control tasks, and humans, superior at handling higher-level cognitive tasks. To address this need, we develop a control theoretic framework that seeks to incorporate user commands such that user intention is preserved while an automated task is carried out by the controller. This is a novel approach in that system theoretic tools allow for analytic guarantees of feasibility and convergence to goal states which naturally lead to varying levels of autonomy. We develop a model predictive controller that takes human input, infers human intent, then applies a control that minimizes deviations from the intended human control while ensuring that the lower-level automated task is being completed. This control framework is then evaluated in a human operator study involving a shared control task with human guidance of a mobile robot for navigation. These theoretical and experimental results lay the foundation for applying this control method for human-robot cooperative control to actual human-robot tasks. Specifically, the control is applied to a Urban Search and Rescue robot task where the shared control of a quadruped rescue robot is needed to ensure static stability during human-guided leg placements in uneven terrain. This control framework is also extended to a multiple user and multiple agent system where the human operators control multiple agents such that the agents maintain a formation while allowing the human operators to manipulate the shape of the formation. User studies are also conducted to evaluate the control in multiple operator scenarios.

<p>This thesis consists of four independent papers concerningthe control of mobile robots in the context of obstacleavoidance and formation keeping.</p><p>The first paper describes a new theoreticallyv erifiableapproach to obstacle avoidance. It merges the ideas of twoprevious methods, with complementaryprop erties, byusing acombined control Lyapunov function (CLF) and model predictivecontrol (MPC) framework.</p><p>The second paper investigates the problem of moving a fixedformation of vehicles through a partiallykno wn environmentwith obstacles. Using an input to state (ISS) formulation theconcept of configuration space obstacles is generalized toleader follower formations. This generalization then makes itpossible to convert the problem into a standard single vehicleobstacle avoidance problem, such as the one considered in thefirst paper. The properties of goal convergence and safetyth uscarries over to the formation obstacle avoidance case.</p><p>In the third paper, coordination along trajectories of anonhomogenuos set of vehicles is considered. Byusing a controlLyapunov function approach, properties such as boundedformation error and finite completion time is shown.</p><p>Finally, the fourth paper applies a generalized version ofthe control in the third paper to translate,rotate and expanda formation. It is furthermore shown how a partial decouplingof formation keeping and formation mission can be achieved. Theapproach is then applied to a scenario of underwater vehiclesclimbing gradients in search for specific thermal/biologicalregions of interest. The sensor data fusion problem fordifferent formation configurations is investigated and anoptimal formation geometryis proposed.</p><p><b>Keywords:</b>Mobile Robots, Robot Control, ObstacleAvoidance, Multirobot System, Formation Control, NavigationFunction, Lyapunov Function, Model Predictive Control, RecedingHorizon Control, Gradient Climbing, Gradient Estimation.</p>

This thesis consists of four independent papers concerningthe control of mobile robots in the context of obstacleavoidance and formation keeping. The first paper describes a new theoreticallyv erifiableapproach to obstacle avoidance. It merges the ideas of twoprevious methods, with complementaryprop erties, byusing acombined control Lyapunov function (CLF) and model predictivecontrol (MPC) framework. The second paper investigates the problem of moving a fixedformation of vehicles through a partiallykno wn environmentwith obstacles. Using an input to state (ISS) formulation theconcept of configuration space obstacles is generalized toleader follower formations. This generalization then makes itpossible to convert the problem into a standard single vehicleobstacle avoidance problem, such as the one considered in thefirst paper. The properties of goal convergence and safetyth uscarries over to the formation obstacle avoidance case. In the third paper, coordination along trajectories of anonhomogenuos set of vehicles is considered. Byusing a controlLyapunov function approach, properties such as boundedformation error and finite completion time is shown. Finally, the fourth paper applies a generalized version ofthe control in the third paper to translate,rotate and expanda formation. It is furthermore shown how a partial decouplingof formation keeping and formation mission can be achieved. Theapproach is then applied to a scenario of underwater vehiclesclimbing gradients in search for specific thermal/biologicalregions of interest. The sensor data fusion problem fordifferent formation configurations is investigated and anoptimal formation geometryis proposed. Keywords:Mobile Robots, Robot Control, ObstacleAvoidance, Multirobot System, Formation Control, NavigationFunction, Lyapunov Function, Model Predictive Control, RecedingHorizon Control, Gradient Climbing, Gradient Estimation. / QC 20111121