SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems

The availability of accurate localization is critical for multi-robot exploration strategies; noisy or inconsistent localization causes failure in meeting exploration objectives. We aim to achieve high localization accuracy with contemporary exploration map belief and vice versa without needing global localization information. This paper proposes a novel simultaneous exploration and localization (SEAL) approach, which uses Gaussian Processes (GP)-based information fusion for maximum exploration while performing communication graph optimization for relative localization. Both these cross-dependent objectives were integrated through the Rao-Blackwellization technique. Distributed linearized convex hull optimization is used to select the next-best unexplored region for distributed exploration. SEAL outperformed cutting-edge methods on exploration and localization performance in extensive ROS-Gazebo simulations, illustrating the practicality of the approach in real-world applications.Comment: Accepted to IROS 202

Communication for Teams of Networked Robots

There are a large class of problems, from search and rescue to environmental monitoring, that can benefit from teams of mobile robots in environments where there is no existing infrastructure for inter-agent communication. We seek to address the problems necessary for a team of small, low-power, low-cost robots to deploy in such a way that they can dynamically provide their own multi-hop communication network. To do so, we formulate a situational awareness problem statement that specifies both the physical task and end-to-end communication rates that must be maintained. In pursuit of a solution to this problem, we address topics ranging from the modeling of point-to-point wireless communication to mobility control for connectivity maintenance. Since our focus is on developing solutions to these problems that can be experimentally verified, we also detail the design and implantation of a decentralized testbed for multi-robot research. Experiments on this testbed allow us to determine data-driven models for point-to-point wireless channel prediction, test relative signal-strength-based localization methods, and to verify that our algorithms for mobility control maintain the desired instantaneous rates when routing through the wireless network. The tools we develop are integral to the fielding of teams of robots with robust wireless network capabilities

Propagation, Localization and Navigation in Tunnel-like Environments

La robótica de servicio, entendida como aquella destinada al uso de uno o varios robots con fines de, por ejemplo, vigilancia, rescate e inspecciones, ha ido tomando cada vez más relevancia en los últimos años. Debido a los grandes avances en las distintas áreas de la robótica, los robots han sido capaces de ejecutar satisfactoriamente tareas que resultan peligrosas o incluso imposibles para los humanos, en diversos entornos. Entre ellos, los entornos confinados como túneles, minas y tuberías, han atraído la atención en aplicaciones relacionadas con transporte ferroviario, redes vehiculares, búsqueda y rescate, y vigilancia, tanto en el ámbito civil como militar. En muchas tareas, la utilización de varios robots resulta más provechoso que utilizar sólo uno. Para cooperar, los robots deben intercambiar información sobre el entorno y su propio estado, por lo que la comunicación entre ellos resulta crucial. Debido a la imposibilidad de utilizar redes cableadas entre robots móviles, se despliegan redes inalámbricas. Para determinar la calidad de señal entre dos robots, inicialmente se utilizaban modelos de propagación basados únicamente en la distancia entre ellos. Sin embargo, estas predicciones sólo resultan útiles en exteriores y sin la presencia de obstáculos, que sólo componen una pequeña parte de los escenarios de la robótica de servicio. Mas aún, la naturaleza altamente multi-trayecto de la propagación electromagnética en túneles hace que éstos actúen como guías de onda para cierto rango de frecuencias, extendiendo considerablemente el alcance de comunicación en comparación con entornos exteriores. Sin embargo, la señal se ve afectada con profundos desvanecimientos (llamados fadings en inglés). Esto los convierte en un reto para la robótica que considera la comunicación entre robots como fundamental. Además, la naturaleza hostil de estos entornos, así como también la falta de características visuales y estructurales, dificultan la localización en estos escenarios, cuestión que resulta fundamental para ejecutar con éxito una tarea con un robot. Los métodos de localización utilizados en interiores, como aquellos basados en SLAM visual, resultan imprecisos por la falta de características distintivas para cámaras o lásers, mientras que los sensores utilizados en exteriores, como el GPS, no funcionan dentro de túneles o tuberías. En esta tesis abordamos problemas fundamentales para la robótica con el fin de proporcionar herramientas necesarias para la exploración con robots en entornos tipo túnel, manteniendo la conectividad de la red de comunicaciones formada por varios robots y una estación base. Para ello, primeramente caracterizamos, en términos de propagación, los dos escenarios tipo túnel más comunes: un túnel de hormigón y una tubería metálica. Hacemos énfasis en el fenómeno de los fadings, ya que son el problema más importante a considerar para mantener la comunicación. Posteriormente presentamos una estrategia de navegación para desplegar un equipo de robots en un túnel, lidiando con los fadings para mantener la conectividad de la red formada por los robots. Esta estrategia ha sido validada a través de numerosos experimentos realizados en un túnel real, el túnel de Somport. Luego, abordamos el problema de la localización, proponiendo e implementando una técnica que permite estimar la posición de un robot dentro de una tubería, basada en la periodicidad de los fadings. El método es validado a través de experimentos reales en tuberías de pequeña y grandes dimensiones. Finalmente, proponemos esquemas de diversidad espacial, de forma que se facilita la navegación mientras se mejora la localización.Deploying a team of robots for search and rescue, inspection, or surveillance, has increasingly gained attention in the last years. As a result of the advances in several areas of robotics, robots have been able to successfully execute tasks that are hazardous or even impossible for humans in a variety of scenarios, such as outdoors, indoors, or even underground. Among these scenarios, tunnel-like environments (such as tunnels, mines, or pipes) have attracted attention for train applications, vehicular networks, search and rescue, and even service and surveillance missions in both military and civilian contexts. In most of the tasks, utilizing a multi-robot team yields better results than a singlerobot system, as it makes the system more robust while reducing the time required to complete tasks. In order to cooperate, robots must exchange information about their current state and the surrounding environment, making communication between them a crucial task. However, due to the mobile nature of robots used for exploration, a wired architecture is not possible nor convenient. Instead, a wireless network is often deployed. Wireless propagation in tunnel-like environments, characterized for the presence of strong fading phenomena, differs from regular indoor and outdoor scenarios, posing multiple challenges for communication-aware robotics. In addition, accurate localization is a problem in environments such as tunnels or pipes. These environments generally lack distinctive visual and/or structural features and are longer than they are wide in shape. Standard indoor localization techniques do not perform well in pipelines or tunnels given the lack of exploitable features, while outdoor techniques (GPS in particular) do not work in these scenarios. In this thesis, we address basic robotics-related problems in order to provide some tools necessary for robotics exploration in tunnel-like scenarios under connectivity constraints. In the first part, we characterize, in terms of propagation, two of the most common tunnel-like environments: a pipe and a tunnel. We emphasize the spatial-fadings phenomena, as it is one of the most relevant issues to deal with, in a communications context. Secondly, we present a navigation strategy to deploy a team of robots for tunnel exploration, in particular maintaining network connectivity in the presence of these fadings. Several experiments conducted in a tunnel allow us to validate the connectivity maintenance of the system. Next, we address the localization problem and propose a technique that uses the periodicity of the fadings to estimate the position of the robots from the base station. The method is validated in small-scale and large-scale pipes. Finally, we propose spatial diversity schemes in order to ease the navigation while improving the localization

Evolutionary Robot Swarms Under Real-World Constraints

Tese de doutoramento em Engenharia Electrotécnica e de Computadores, na especialidade de Automação e Robótica, apresentada ao Departamento de Engenharia Electrotécnica e de Computadores da Faculdade de Ciências e Tecnologia da Universidade de CoimbraNas últimas décadas, vários cientistas e engenheiros têm vindo a estudar as estratégias provenientes da natureza. Dentro das arquiteturas biológicas, as sociedades que vivem em enxames revelam que agentes simplistas, tais como formigas ou pássaros, são capazes de realizar tarefas complexas usufruindo de mecanismos de cooperação. Estes sistemas abrangem todas as condições necessárias para a sobrevivência, incorporando comportamentos de cooperação, competição e adaptação. Na “batalha” sem fim em prol do progresso dos mecanismos artificiais desenvolvidos pelo homem, a ciência conseguiu simular o primeiro comportamento em enxame no final dos anos oitenta. Desde então, muitas outras áreas, entre as quais a robótica, beneficiaram de mecanismos de tolerância a falhas inerentes da inteligência coletiva de enxames. A área de investigação deste estudo incide na robótica de enxame, consistindo num domínio particular dos sistemas robóticos cooperativos que incorpora os mecanismos de inteligência coletiva de enxames na robótica. Mais especificamente, propõe-se uma solução completa de robótica de enxames a ser aplicada em contexto real. Nesta ótica, as operações de busca e salvamento foram consideradas como o caso de estudo principal devido ao nível de complexidade associado às mesmas. Tais operações ocorrem tipicamente em cenários dinâmicos de elevadas dimensões, com condições adversas que colocam em causa a aplicabilidade dos sistemas robóticos cooperativos. Este estudo centra-se nestes problemas, procurando novos desafios que não podem ser ultrapassados através da simples adaptação da literatura da especialidade em algoritmos de enxame, planeamento, controlo e técnicas de tomada de decisão. As contribuições deste trabalho sustentam-se em torno da extensão do método Particle Swarm Optimization (PSO) aplicado a sistemas robóticos cooperativos, denominado de Robotic Darwinian Particle Swarm Optimization (RDPSO). O RDPSO consiste numa arquitetura robótica de enxame distribuída que beneficia do particionamento dinâmico da população de robôs utilizando mecanismos evolucionários de exclusão social baseados na sobrevivência do mais forte de Darwin. No entanto, apesar de estar assente no caso de estudo do RDPSO, a aplicabilidade dos conceitos aqui propostos não se encontra restrita ao mesmo, visto que todos os algoritmos parametrizáveis de enxame de robôs podem beneficiar de uma abordagem idêntica. Os fundamentos em torno do RDPSO são introduzidos, focando-se na dinâmica dos robôs, nos constrangimentos introduzidos pelos obstáculos e pela comunicação, e nas suas propriedades evolucionárias. Considerando a colocação inicial dos robôs no ambiente como algo fundamental para aplicar sistemas de enxames em aplicações reais, é assim introduzida uma estratégia de colocação de robôs realista. Para tal, a população de robôs é dividida de forma hierárquica, em que são utilizadas plataformas mais robustas para colocar as plataformas de enxame no cenário de forma autónoma. Após a colocação dos robôs no cenário, é apresentada uma estratégia para permitir a criação e manutenção de uma rede de comunicação móvel ad hoc com tolerância a falhas. Esta estratégia não considera somente a distância entre robôs, mas também a qualidade do nível de sinal rádio frequência, redefinindo assim a sua aplicabilidade em cenários reais. Os aspetos anteriormente mencionados estão sujeitos a uma análise detalhada do sistema de comunicação inerente ao algoritmo, para atingir uma implementação mais escalável do RDPSO a cenários de elevada complexidade. Esta elevada complexidade inerente à dinâmica dos cenários motivaram a ultimar o desenvolvimento do RDPSO, integrando para o efeito um mecanismo adaptativo baseado em informação contextual (e.g., nível de atividade do grupo). Face a estas considerações, o presente estudo pode contribuir para expandir o estado-da-arte em robótica de enxame com algoritmos inovadores aplicados em contexto real. Neste sentido, todos os métodos propostos foram extensivamente validados e comparados com alternativas, tanto em simulação como com robôs reais. Para além disso, e dadas as limitações destes (e.g., número limitado de robôs, cenários de dimensões limitadas, constrangimentos reais limitados), este trabalho contribui ainda para um maior aprofundamento do estado-da-arte, onde se propõe um modelo macroscópico capaz de capturar a dinâmica inerente ao RDPSO e, até certo ponto, estimar analiticamente o desempenho coletivo dos robôs perante determinada tarefa. Em suma, esta investigação pode ter aplicabilidade prática ao colmatar a lacuna que se faz sentir no âmbito das estratégias de enxames de robôs em contexto real e, em particular, em cenários de busca e salvamento.Over the past decades, many scientists and engineers have been studying nature’s best and time-tested patterns and strategies. Within the existing biological architectures, swarm societies revealed that relatively unsophisticated agents with limited capabilities, such as ants or birds, were able to cooperatively accomplish complex tasks necessary for their survival. Those simplistic systems embrace all the conditions necessary to survive, thus embodying cooperative, competitive and adaptive behaviours. In the never-ending battle to advance artificial manmade mechanisms, computer scientists simulated the first swarm behaviour designed to mimic the flocking behaviour of birds in the late eighties. Ever since, many other fields, such as robotics, have benefited from the fault-tolerant mechanism inherent to swarm intelligence. The area of research presented in this Ph.D. Thesis focuses on swarm robotics, which is a particular domain of multi-robot systems (MRS) that embodies the mechanisms of swarm intelligence into robotics. More specifically, this Thesis proposes a complete swarm robotic solution that can be applied to real-world missions. Although the proposed methods do not depend on any particular application, search and rescue (SaR) operations were considered as the main case study due to their inherent level of complexity. Such operations often occur in highly dynamic and large scenarios, with harsh and faulty conditions, that pose several problems to MRS applicability. This Thesis focuses on these problems raising new challenges that cannot be handled appropriately by simple adaptation of state-of-the-art swarm algorithms, planning, control and decision-making techniques. The contributions of this Thesis revolve around an extension of the Particle Swarm Optimization (PSO) to MRS, denoted as Robotic Darwinian Particle Swarm Optimization (RDPSO). The RDPSO is a distributed swarm robotic architecture that benefits from the dynamical partitioning of the whole swarm of robots by means of an evolutionary social exclusion mechanism based on Darwin’s survival-of-the-fittest. Nevertheless, although currently applied solely to the RDPSO case study, the applicability of all concepts herein proposed is not restricted to it, since all parameterized swarm robotic algorithms may benefit from a similar approach The RDPSO is then proposed and used to devise the applicability of novel approaches. The fundamentals around the RDPSO are introduced by focusing on robots’ dynamics, obstacle avoidance, communication constraints and its evolutionary properties. Afterwards, taking the initial deployment of robots within the environment as a basis for applying swarm robotics systems into real-world applications, the development of a realistic deployment strategy is proposed. For that end, the population of robots is hierarchically divided, wherein larger support platforms autonomously deploy smaller exploring platforms in the scenario, while considering communication constraints and obstacles. After the deployment, a way of ensuring a fault-tolerant multi-hop mobile ad hoc communication network (MANET) is introduced to explicitly exchange information needed in a collaborative realworld task execution. Such strategy not only considers the maximum communication range between robots, but also the minimum signal quality, thus refining the applicability to real-world context. This is naturally followed by a deep analysis of the RDPSO communication system, describing the dynamics of the communication data packet structure shared between teammates. Such procedure is a first step to achieving a more scalable implementation by optimizing the communication procedure between robots. The highly dynamic characteristics of real-world applications motivated us to ultimate the RDPSO development with an adaptive strategy based on a set of context-based evaluation metrics. This thesis contributes to the state-of-the-art in swarm robotics with novel algorithms for realworld applications. All of the proposed approaches have been extensively validated in benchmarking tasks, in simulation, and with real robots. On top of that, and due to the limitations inherent to those (e.g., number of robots, scenario dimensions, real-world constraints), this Thesis further contributes to the state-of-the-art by proposing a macroscopic model able to capture the RDPSO dynamics and, to some extent, analytically estimate the collective performance of robots under a certain task. It is the author’s expectation that this Ph.D. Thesis may shed some light into bridging the reality gap inherent to the applicability of swarm strategies to real-world scenarios, and in particular to SaR operations.FCT - SFRH/BD /73382/201

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words