The frequent occurrences of natural and technological disasters have incurred grave loss of life and damage to property. For mitigating the miserable aftermaths, multi-robot teleoperation systems have been developed and deployed to cooperate with human rescuers in post-earthquake scenarios, and to sample, monitor and clean pollutants in marine environments. With a bidirectional communication channel, human users can deliver commands/requests to guide the motions of the remote robots, and can receive visual/audio feedback to supervise the status of the remote environment, throughout multi-robot teleoperation. Furthermore, the remote robots can send force feedback to human operators to improve their situational awareness and task performance. This way, a closed-loop multi-robot teleoperation system becomes bilateral in which coordinated robots physically interact and exchange energy with human users, and hence needs to be rendered passive for safe human-robot interaction. Beyond guaranteeing closed-loop passivity, the control of a bilateral multi-robot teleoperation system faces two challenging problems: preserving the communication connectivity of the remote robots; and dispatching the teleoperation authority to multiple human users. Because wireless transmission of radio/acoustic signals between the remote robots is constrained by their distances, bilateral multi-robot teleoperation control must coordinate the motions of the remote robots appropriately so as to maintain their communication network connected. Further, multiple human users can send possibly conflicting teleoperation commands to the remote robots, a distributed authority dispatch algorithm is thus needed for the remote robot network to recognize and follow the most urgent user commands at runtime. This thesis develops an energy shaping strategy to preserve the connectivity of the remote robots, and to dispatch control authority over the remote robots to human users, during bilateral multi-robot teleoperation. Chapter 1 introduces the application background of multi-robot teleoperation as well as the state-of-the-art development in related research areas. In Chapter 2, a dynamic interconnection and damping strategy is proposed to reduce and constrain the position error between the local and remote robots to any prescribed bound during bilateral teleoperation. Chapter 3 derives a gradient plus damping control from a bounded potential function and then unifies it into an indirect coupling framework to preserve all communication links of an autonomous multi-robot system with time-varying delays and bounded actuation. On these bases, Chapter 4 develops a dynamic feedforward-feedback passivation strategy to preserve all communication links and thus the connectivity of the tree network of the remote robots while rendering the bilateral multi-robot teleoperation close loop passive. Specifically, by blending the sliding variable in Chapter 2 with the bounded potential function in Chapter 3, the dynamic passivation strategy decomposes the dynamics of the remote robots into a power-preserving interconnection of two subsystems, and regulates the energy behaviour of each subsystem to preserve the tree communication connectivity of the remote robots. To handle time-varying communication delays, the strategy further transforms the communication channels between the local and remote robots into a dynamic controller for passivating bilateral teleoperation. Superior to existing controls, the strategy using a bounded potential function can circumvent numerical instability, reduce noise sensitivity and facilitate future extensions to accommodate robot actuator saturation. On the other side, Chapter 5 designs a distributed and exponentially convergent winners-take-all authority dispatch algorithm that activates the teleoperation of only human users with the most urgent requests in real time. After formulating the problem as a constrained quadratic program, we employ an exact penalty function method to construct a distributed primal-dual dynamical system that can solve the problem at an exponential rate. Because the equilibrium of the system changes with user requests, we then interconnect the dynamical system with physical robot dynamics in a power-preserving way, and passivate closed-loop multi-robot teleoperation using multiple storage functions from a switched system perspective. Finally, Chapter 6 provides some conclusive remarks and two problems regarding connectivity preservation and authority dispatch for future study. / Graduate

The evolution of cloud computing has finally started to affect robotics. Indeed, there have been several real-time cloud applications making their way into robotics as of late. Inherent benefits of cloud robotics include providing virtually infinite computational power and enabling collaboration of a multitude of connected devices. However, its drawbacks include higher latency and overall higher energy consumption. Moreover, local devices in proximity incur higher latency when communicating among themselves via the cloud. At the same time, the cloud is a single point of failure in the network. Fog Computing is an extension of the cloud computing paradigm providing data, compute, storage and application services to end-users on a so-called edge layer. Distinguishing characteristics are its support for mobility and dense geographical distribution. We propose to study the implications of applying fog computing concepts in robotics by developing a middle-ware solution for Robotic Fog Computing Cluster solution for enabling adaptive distributed computation in heterogeneous multi-robot systems interacting with the Internet of Things (IoT). The developed middle-ware has a modular plug-in architecture based on micro-services and facilitates communication of IOT devices with the multi-robot systems. In addition, the developed middle-ware solutions support different load balancing or task allocation algorithms. In particular, we establish that we can enhance the performance of distributed system by decreasing overall system latency by using already established multi-criteria decision-making algorithms like TOPSIS and TODIM with naive Q-learning and with Neural Network based Q-learning. / Master of Science

Multi-robot systems leverage the numbers and characteristics of different robots to accomplish an overall mission. Efficient task allocation and motion planning of multi-robot teams are essential to ensure each robot's actions contribute to the overall mission while avoiding conflict with each other. The original contribution of this thesis is an optimized, efficient, and multi-factor task allocation algorithm to comprise the main component of a task coordination framework (TCF), with motion planning as a secondary component. This algorithm determines which robot performs which tasks and in what order. It presents a novel solution to the multiple robot task allocation problem (MRTA) as an extension of the multiple travelling salesmen (MTSP) problem. This extension to the MTSP considers operational factors representing physical limitations, the suitability of each robot, and inter-task dependencies. The task allocation algorithm calculates an optimized distribution of tasks such that a global objective function is minimized to simultaneously reduce total cost and ensure an even distribution of tasks among the agents. Once an optimized distribution of tasks is calculated, the motion planning component calculates collision-free velocities to drive the robots to their goal poses to facilitate task execution in a shared environment. The proposed TCF was implemented on teams of unmanned air vehicles (UAVs) and unmanned ground vehicles (UGVs). Test cases considered scenarios where the UAVs executed aerial observation tasks while UGVs executed simulated patrol and delivery tasks. The solutions were tested using real-life robots as a proof of concept and to validate simulations. The robots' kinematic and computer vision models were combined with the task coordination framework to facilitate the implementation. Large-scale simulations involving greater numbers of robots operating in a larger area were also conducted to demonstrate the task coordination framework's versatility and efficacy.

Multi-robot systems have recently witnessed a swell in interest in the past few years because of their various applications such as agricultural autonomy, medical robotics, industrial and commercial automation and, search and rescue. In this thesis, we particularly investigate the behavior of multi-robot systems with respect to stable topology control in asymmetric interaction settings. From theoretical perspective, we first classify stable topologies, and identify the conditions under which we can determine whether a topology is stable or not. Then, we design a limited fields-of-view (FOV) controller for robots that use sensors like cameras for coordination which induce asymmetric robot to robot interactions. Finally, we conduct a rigorous theoretical analysis to qualitatively determine which interactions are suitable for stable directed topology control of multi-robot systems with asymmetric interactions. In this regard, we solve an optimal topology selection problem to determine the topology with the best interactions based on a suitable metric that represents the quality of interaction. Further, we solve this optimal problem distributively and validate the distributed optimization formulation with extensive simulations.  For experimental purposes, we developed a portable multi-robot testbed which enables us to conduct multi-robot topology control experiments in both indoor and outdoor settings and validate our theoretical findings. Therefore, the contribution of this thesis is two fold: i) We provide rigorous theoretical analysis of  stable coordination of multi-robot systems with directed graphs, demonstrating the graph structures that induce stability for a broad class of coordination objectives; ii) We develop a testbed that enables validating multi-robot topology control in both indoor and outdoor settings. / Doctor of Philosophy / In this thesis, we address the problem of collaborative tasks in a multi-robot system where we investigate how interactions within members of the multi-robot system can induce instability. We conduct rigorous theoretical analysis and identify when the system will be unstable and hence classify interactions that will lead to stable multi-robot coordination. Our theoretical analysis tries to emulate realistic interactions in a multi-robot system such as limited interactions (blind spots) that exist when on-board cameras are used to detect and track other robots in the vicinity. So we study how these limited interactions induce instability in the multi-robot system. To verify our theoretical analysis experimentally,  we developed a portable multi-robot testbed that enables us to test our theory on stable coordination of multi-robot system with a team of Unmanned Aerial Vehicles (UAVs) in both indoor and outdoor settings. With this feature of the testbed we are able to investigate the difference in the multi-robot system behavior when tested in controlled indoor environments versus an uncontrolled outdoor environment. Ultimately, the motivation behind this thesis is to emulate realistic conditions for multi-robot cooperation and investigate suitable conditions for them to work in a stable and safe manner. Therefore, our contribution is twofold ; i) We provide rigorous theoretical analysis that enables stable coordination of multi-robot systems with limited interactions induced by sensor capabilities such as cameras; ii) We developed a testbed that enables testing of our theoretical contribution with a team of real robots in realistic environmental conditions.

O desenvolvimento de arquiteturas de controle para múltiplos robôs em ambientes dinâmicos tem sido tema de pesquisas na área de robótica. A complexidade deste tema varia de acordo com as necessidades exigidas da equipe de robôs. Em geral, espera-se que os robôs colaborem uns com os outros na execução de uma tarefa. Além disso, cada robô deve ser capaz de planejar trajetórias e replanejá-las em caso de situações inesperadas. No presente trabalho, propomos uma Arquitetura de Controle Inteligente para múltiplos robôs denominada ACIn. Para esta finalidade, foram investigadas algumas técnicas utilizadas para o controle inteligente de robôs, tais como, Redes Neurais Artificiais, Campos Potenciais e Campos Potenciais baseados em Problema do Valor de Contorno (PVC). Tais técnicas, normalmente utilizadas para um único robô, foram adaptadas para tornar possível o controle de múltiplos robôs sob arquitetura ACIn. Uma outra contribuição deste trabalho refere-se ao aperfeiçoamento da técnica de Campos Potenciais baseada PVC denominada Campos Potenciais Localmente Orientados (CPLO). Este aperfeiçoamento foi proposto para suprir a deficiência das técnicas baseadas em PVC quando estas são aplicadas em ambientes com múltiplos robôs. Além disso, um Sistema Baseado em Regras (SBR) também foi proposto como parte integrante da arquitetura ACIn. O objetivo do SBR é caracterizar a funcionalidade de cada robô para uma determinada tarefa. Isto se faz necessário para que o comportamento dos integrantes da equipe de robôs não seja competitivo e sim colaborativo. Por fim, através dos experimentos utilizando o simulador oficial de futebol de robôs da FIRA, observou-se que a arquitetura de controle inteligente (ACIn) implementada com a técnica de planejamento CPLO e SBR propostos, mostrou-se robusta e eficiente no controle de múltiplos robôs / In this work, an intelligent control architecture for multi-robots denominated ACIn was proposed. With this objective, some techniques considered intelligent were studied for the planning of trajectories, such as Artificial Neural Networks, Potential Fields and Potential Fields based on the boundary value problem (BVP). Such techniques, normally used for a single robot, were adapted to function with multi-robots inside the ACIn architecture. Another contribution of this work refers to the improvement of the Potential Fields based on the boundary value problem (BVP) technique. This improvement was proposed to supply the drawback of the BVP based techniques when they are applied to multi robots environments. Besides, a Rule Based System (RBS) was also proposed as part of the ACIn architecture. The objective of the RBS is to characterize the functionality of each robot for a determined task. This is necessary for the behavior of the equip members not to be competitive, but collaborative. Finally, it was observed through the experiments with the robot soccer simulated environment, that our intelligent control architecture (ACIn) proposal, integrating planning and RBS for the control of multi-robots was satisfactory

Neste trabalho foi desenvolvido um estudo comparativo entre duas estratégias básicas para a combinação de mapas parciais baseados em marcos para sistemas multirobô: a estratégia por associação de marcos e a estratégia por distância relativa entre os robôs (também conhecida por rendez-vous). O ambiente simulado corresponde a um entorno plano povoado de árvores que são mapeadas por uma equipe de dois robôs móveis equipados com sensores laser para medir a largura e localização de cada _arvore (marco). Os mapas parciais são estimados usando o algoritmo FastSLAM. Além do estudo comparativo propõe-se também um algoritmo alternativo de localização e mapeamento simultâneos para multirrobôs cooperativos, utilizando as observações entre os robôs não só para o cálculo da transformação de coordenadas, mas também no desenvolvimento de um processo seqüencial para atualizar o alinhamento entre os mapas, explorando de forma mais eficiente as observações entre robôs. Os experimentos realizados demonstraram que o algoritmo proposto pode conduzir a resultados significativamente melhores em termos de precisão quando comparado com a abordagem de combinação de mapas tradicional (usando distância relativa entre os robôs). / In this text a comparative survey between the two basic strategies used to combine partial landmark based maps in multi-robot systems, data association and inter-robot observations (known as rendezvous), is presented. The simulated environment is a at place populated by trees, which are going to be mapped by a two-mobile robot team equipped with laser range finders in order to measure every tree (landmark) location and width. Partial maps are estimated using the algorithm FastSLAM. Besides the comparative study it is also proposed an alternative algorithm for Simultaneous Localization and Mapping (SLAM) in multi-robot cooperative systems. It uses observations between robots (detections) not only for calculating the coordinate transformation but also in the development of a sequential process for updating the alignment between maps, exploiting in a more efficient way the inter-robot observations. The experiments showed that the algorithm can lead to significantly better results in terms of accuracy when compared with the traditional approach of combining maps (using the relative distance between robots).

O desenvolvimento de arquiteturas de controle para múltiplos robôs em ambientes dinâmicos tem sido tema de pesquisas na área de robótica. A complexidade deste tema varia de acordo com as necessidades exigidas da equipe de robôs. Em geral, espera-se que os robôs colaborem uns com os outros na execução de uma tarefa. Além disso, cada robô deve ser capaz de planejar trajetórias e replanejá-las em caso de situações inesperadas. No presente trabalho, propomos uma Arquitetura de Controle Inteligente para múltiplos robôs denominada ACIn. Para esta finalidade, foram investigadas algumas técnicas utilizadas para o controle inteligente de robôs, tais como, Redes Neurais Artificiais, Campos Potenciais e Campos Potenciais baseados em Problema do Valor de Contorno (PVC). Tais técnicas, normalmente utilizadas para um único robô, foram adaptadas para tornar possível o controle de múltiplos robôs sob arquitetura ACIn. Uma outra contribuição deste trabalho refere-se ao aperfeiçoamento da técnica de Campos Potenciais baseada PVC denominada Campos Potenciais Localmente Orientados (CPLO). Este aperfeiçoamento foi proposto para suprir a deficiência das técnicas baseadas em PVC quando estas são aplicadas em ambientes com múltiplos robôs. Além disso, um Sistema Baseado em Regras (SBR) também foi proposto como parte integrante da arquitetura ACIn. O objetivo do SBR é caracterizar a funcionalidade de cada robô para uma determinada tarefa. Isto se faz necessário para que o comportamento dos integrantes da equipe de robôs não seja competitivo e sim colaborativo. Por fim, através dos experimentos utilizando o simulador oficial de futebol de robôs da FIRA, observou-se que a arquitetura de controle inteligente (ACIn) implementada com a técnica de planejamento CPLO e SBR propostos, mostrou-se robusta e eficiente no controle de múltiplos robôs / In this work, an intelligent control architecture for multi-robots denominated ACIn was proposed. With this objective, some techniques considered intelligent were studied for the planning of trajectories, such as Artificial Neural Networks, Potential Fields and Potential Fields based on the boundary value problem (BVP). Such techniques, normally used for a single robot, were adapted to function with multi-robots inside the ACIn architecture. Another contribution of this work refers to the improvement of the Potential Fields based on the boundary value problem (BVP) technique. This improvement was proposed to supply the drawback of the BVP based techniques when they are applied to multi robots environments. Besides, a Rule Based System (RBS) was also proposed as part of the ACIn architecture. The objective of the RBS is to characterize the functionality of each robot for a determined task. This is necessary for the behavior of the equip members not to be competitive, but collaborative. Finally, it was observed through the experiments with the robot soccer simulated environment, that our intelligent control architecture (ACIn) proposal, integrating planning and RBS for the control of multi-robots was satisfactory

A Robótica tem avançando significativamente nas últimas décadas, chegando a apresentar produtos comerciais, como robôs aspiradores de pó e quadricópteros. Com a integração cada vez maior de robôs em nossa sociedade, mostra-se necessário o desenvolvimento de métodos de interação entre pessoas e robôs para gerenciar o convívio e trabalho mútuo. Existem alguns trabalhos na literatura que consideram o posicionamento socialmente aceitável de um robô, acompanhando um indivíduo, mas não consideram o caso de uma equipe de robôs navegando com uma pessoa considerando aspectos de proxêmica. Nesta tese, são propostas várias estratégias de acompanhamento de um humano por um time de robôs social, que são bioinspiradas por serem baseadas em técnicas de inteligencia coletiva e comportamento social. Experimentos simulados são apresentados visando comparar as técnicas propostas em diversos cenários, destacando-se as vantagens e desvantagens de cada uma delas. Experimentos reais permitiram uma análise da percepção das pessoas em interagir com um ou mais robôs, demonstrando que nenhuma diferença na impressão dos indivíduos foi encontrada. / The field of Robotics have been advancing significantly on the last few decades, presenting commercial products like vacuum cleaning robots and autonomous quadcopter drones. With the increasing presence of robots in our routine, it is necessary to develop human-robot interaction schemes to manage their relationship. Works that deal with a single robot doing a socially acceptable human following behavior are available, but do not consider cases where a robot team walks with a human In this thesis, it is presented a solution for social navigation between a human and a robot team combining socially aware human following techniques with a multirobot escorting method, generating four bioinspired navigation strategies based on collective intelligence and social behavior. Experiments comparing these four strategies on a simulated environment in various scenarios highlighted advantages and disadvantages of each strategy. Moreover, an experiment with real robots was made to investigate the difference on perception of people when interacting with one or three robots, and no difference was found.

This research addresses the problem of allocating closely spaced targets to multiple autonomous underwater vehicles (AUV) in the presence of constant ocean currents. The main difficulty of this problem is that the non-holonomic vehicles are constrained to move along forward paths with bounded curvatures. The Dubins model is a simple but effective way to handle the kinematic characteristics of AUVs. It gives complete characterization of the optimal paths between two configurations for a vehicle with limited turning radius moving in a plane at constant speed. In the proposed algorithm, Dubins paths are modified to include ocean currents, resulting in paths defined by curves whose radius of curvature is not constant. To determine the time required to follow such paths, an approximate dynamic model of the AUV is queried due to the computational complexity of the full model. The lower order model is built from data obtained from sampling the full model. The full model is used in evaluating the final tour times of the sequences generated by the proposed algorithm to validate the results. The proposed algorithm solves the task allocation problem with market-based auctions that minimize the total travel time to complete the mission. The novelty of the research is the path cost calculation that combines a Dubins model, an AUV dynamic model, and a model of the ocean current. Simulations were conducted in Matlab to illustrate the performance of the proposed algorithm using various number of task points and AUVs. The task points were generated randomly and uniformly close together to highlight the necessity for considering the curvature constraints. For a sufficiently dense set of points, it becomes clear that the ordering of the Euclidean tours are not optimal in the case of the Dubins multiple travelling salesmen problem. This is due to the fact that there is little relationship between the Euclidean and Dubins metrics, especially when the Euclidean distances are small with respect to the turning radius. An algorithm for the Euclidean problem will tend to schedule very close points in a successive order, which can imply long maneuvers for the AUV. This is clearly demonstrated by the numerous loops that become problematic with dense sets of points. The algorithm proposed in this thesis does not rely on the Euclidean solution and therefore, even in the presence of ocean currents, can create paths that are feasible for curvature bound vehicles. Field tests were also conducted on an Iver2 AUV at the Avila Pier in California to validate the performance of the proposed algorithm in real world environments. Missions created based on the sequences generated by the proposed algorithm were conducted to observe the ability of an AUV to follow paths of bounded curvature in the presence of ocean currents. Results show that the proposed algorithm generated paths that were feasible for an AUV to track closely, even in the presence of ocean current.

This research addresses the problem of allocating closely spaced targets to multiple autonomous underwater vehicles (AUV) in the presence of constant ocean currents. The main difficulty of this problem is that the non-holonomic vehicles are constrained to move along forward paths with bounded curvatures. The Dubins model is a simple but effective way to handle the kinematic characteristics of AUVs. It gives complete characterization of the optimal paths between two configurations for a vehicle with limited turning radius moving in a plane at constant speed. In the proposed algorithm, Dubins paths are modified to include ocean currents, resulting in paths defined by curves whose radius of curvature is not constant. To determine the time required to follow such paths, an approximate dynamic model of the AUV is queried due to the computational complexity of the full model. The lower order model is built from data obtained from sampling the full model. The full model is used in evaluating the final tour times of the sequences generated by the proposed algorithm to validate the results. The proposed algorithm solves the task allocation problem with market-based auctions that minimize the total travel time to complete the mission. The novelty of the research is the path cost calculation that combines a Dubins model, an AUV dynamic model, and a model of the ocean current. Simulations were conducted in Matlab to illustrate the performance of the proposed algorithm using various number of task points and AUVs. The task points were generated randomly and uniformly close together to highlight the necessity for considering the curvature constraints. For a sufficiently dense set of points, it becomes clear that the ordering of the Euclidean tours are not optimal in the case of the Dubins multiple travelling salesmen problem. This is due to the fact that there is little relationship between the Euclidean and Dubins metrics, especially when the Euclidean distances are small with respect to the turning radius. An algorithm for the Euclidean problem will tend to schedule very close points in a successive order, which can imply long maneuvers for the AUV. This is clearly demonstrated by the numerous loops that become problematic with dense sets of points. The algorithm proposed in this thesis does not rely on the Euclidean solution and therefore, even in the presence of ocean currents, can create paths that are feasible for curvature bound vehicles. Field tests were also conducted on an Iver2 AUV at the Avila Pier in California to validate the performance of the proposed algorithm in real world environments. Missions created based on the sequences generated by the proposed algorithm were conducted to observe the ability of an AUV to follow paths of bounded curvature in the presence of ocean currents. Results show that the proposed algorithm generated paths that were feasible for an AUV to track closely, even in the presence of ocean current.