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.

Considered in this research is a framework for effective formation control of multirobot systems in dynamic environments. The basic formation control involves two important considerations: (1) Real-time trajectory generation algorithms for distributed control based on nominal agent models, and (2) robust tracking of reference trajectories under model uncertainties. Proposed is a two-layer hierarchical architecture for collectivemotion control ofmultirobot nonholonomic systems. It endows robotic systems with the ability to simultaneously deal with multiple tasks and achieve typical complex formation missions, such as collisionfree maneuvers in dynamic environments, tracking certain desired trajectories, forming suitable patterns or geometrical shapes, and/or varying the pattern when necessary. The study also addresses real-time formation tracking of reference trajectories under the presence of model uncertainties and proposes robust control laws such that over each time interval any tracking errors due to system uncertainties are driven down to zero prior to the commencement of the subsequent computation segment. By considering a class of nonlinear systems with favorable finite-time convergence characteristics, sufficient conditions for exponential finite-time stability are established and then applied to distributed formation tracking controls. This manifests in the settling time of the controlled system being finite and no longer than the predefined reference trajectory segment computing time interval, thus making tracking errors go to zero by the end of the time horizon over which a segment of the reference trajectory is generated. This way the next segment of the reference trajectory is properly initialized to go into the trajectory computation algorithm. Consequently this could lead to a guarantee of desired multi-robot motion evolution in spite of system uncertainties. To facilitate practical implementation, communication among multi-agent systems is considered to enable the construction of distributed formation control. Instead of requiring global communication among all robots, a distributed communication algorithm is employed to eliminate redundant data propagation, thus reducing energy consumption and improving network efficiency while maintaining connectivity to ensure the convergence of formation control.

Like computer architects, robot designers must address multiple, possibly competing, requirements by balancing trade-offs in terms of processing, memory, communication, and energy to satisfy design objectives. However, robot architects currently lack the design guidelines, organizing principles, rules of thumb, and tools that computer architects rely upon. This thesis takes a step in this direction, by analyzing the roles of heterogeneity and distribution in robot systems architecture. This thesis takes a systems architecture approach to the design of robot systems, and in particular, investigates the use of distributed, heterogeneous platforms to exploit locality in robot systems design. We show how multiple, distributed heterogeneous platforms can serve as general purpose robot systems for three distinct domains with different design objectives: increasing availability in a search and rescue mission, increasing flexibility and ease-of-use for a personal educational robot, and decreasing the computation and sensing resources necessary for navigation and foraging tasks.

Multi-robot systems are an established research area with a growing number of applications. Efficient coordination in such systems usually requires knowledge of robot positions and the global map. This work presents a novel map-merging algorithm for merging 3D point cloud maps in multi-robot systems, which produces the global map and estimates robot positions. The algorithm is based on feature- matching transformation estimation with a novel descriptor matching scheme and works solely on point cloud maps without any additional auxiliary information. The algorithm can work with different SLAM approaches and sensor types and it is applicable in heterogeneous multi-robot systems. The map-merging algorithm has been evaluated on real-world datasets captured by both aerial and ground-based robots with a variety of stereo rig cameras and active RGB-D cameras. It has been evaluated in both indoor and outdoor environments. The proposed algorithm was implemented as a ROS package and it is currently distributed in the ROS distribution. To the best of my knowledge, it is the first ROS package for map-merging of 3D maps.

abstract: Robotic technology is advancing to the point where it will soon be feasible to deploy massive populations, or swarms, of low-cost autonomous robots to collectively perform tasks over large domains and time scales. Many of these tasks will require the robots to allocate themselves around the boundaries of regions or features of interest and achieve target objectives that derive from their resulting spatial configurations, such as forming a connected communication network or acquiring sensor data around the entire boundary. We refer to this spatial allocation problem as boundary coverage. Possible swarm tasks that will involve boundary coverage include cooperative load manipulation for applications in construction, manufacturing, and disaster response. In this work, I address the challenges of controlling a swarm of resource-constrained robots to achieve boundary coverage, which I refer to as the problem of stochastic boundary coverage. I first examined an instance of this behavior in the biological phenomenon of group food retrieval by desert ants, and developed a hybrid dynamical system model of this process from experimental data. Subsequently, with the aid of collaborators, I used a continuum abstraction of swarm population dynamics, adapted from a modeling framework used in chemical kinetics, to derive stochastic robot control policies that drive a swarm to target steady-state allocations around multiple boundaries in a way that is robust to environmental variations. Next, I determined the statistical properties of the random graph that is formed by a group of robots, each with the same capabilities, that have attached to a boundary at random locations. I also computed the probability density functions (pdfs) of the robot positions and inter-robot distances for this case. I then extended this analysis to cases in which the robots have heterogeneous communication/sensing radii and attach to a boundary according to non-uniform, non-identical pdfs. I proved that these more general coverage strategies generate random graphs whose probability of connectivity is Sharp-P Hard to compute. Finally, I investigated possible approaches to validating our boundary coverage strategies in multi-robot simulations with realistic Wi-fi communication. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2016