Submitted by Fernando Souza (fernandoafsou@gmail.com) on 2017-08-21T13:50:24Z No. of bitstreams: 1 arquivototal.pdf: 2220137 bytes, checksum: 5d830e5d1ba6396c3e9ff56a19b08deb (MD5) / Made available in DSpace on 2017-08-21T13:50:24Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 2220137 bytes, checksum: 5d830e5d1ba6396c3e9ff56a19b08deb (MD5) Previous issue date: 2016-02-26 / Multi-Robot System (MRS) consisting of multiple interacting robots, each running a specific control strategy, which is not driven centrally. Technical challenges arise from the need to develop complex, software-intensive products that take the constraints of the physical world into account. Make tools, methodologies and teams from different fields can work together is not an easy task to accomplish. Co-simulation represents on technique of validation in heterogeneous systems. Its fundamental principle is to provide support to execute different simulators in a cooperative way. A known standard is the High Level Architecture (HLA) that is a pattern described in IEEE 1516 series and has been developed to provide a common architecture to distributed model and simulation. Using HLA, several simulators and real applications could be simulated together. That way, this work presents a project for Multi-Robot Systems (SMR) simulation using ROS co-simulation with a network simulator, the OMNeT++, using HLA. The main goal is make the simulations more realistic, where the data exchange will be performed by using a simulated network, as if we had real robots interacting through a conventional network. To this end, an interface was developed between ROS and OMNeT++ using HLA. Experiments demonstrate that the packet losses were correctly simulated, adding realism to simulations. / Sistemas Multi-Robôs (SMR) consistem em múltiplos robôs interagindo, cada um executando uma estratégia de controle específica, que não é conduzida de forma centralizada. Alguns desafios surgiram da necessidade de desenvolver produtos que levem o mundo real em consideração. Fazer com que ferramentas, metodologias e equipes de diferentes áreas possam trabalhar juntas não é uma tarefa simples de ser realizada. Cossimulação representa uma técnica para validação de sistemas heterogêneos. Seu princípio fundamental é prover suporte à execução de diferentes simuladores de forma cooperativa. Um dos padrões para tal é conhecido como High Level Architecture (HLA), que é um padrão descrito no IEEE 1516 e tem sido desenvolvido para dispor uma arquitetura para modelagem e simulação distribuídos. Utilizando HLA, vários simuladores e aplicações reais podem ser simulados juntos. Sendo assim, este trabalho apresenta um projeto para simulação de Sistemas Multi-Robôs (SMR) utilizando ROS cossimulado com um simulador de redes de computadores, o OMNeT++ através do HLA. Seu principal objetivo é tornar as simulações mais próximas da realidade, onde os dados irão ser trocados através de uma rede simulada, como se tivéssemos robôs reais interagindo através de uma rede convencional. Para tal, foi desenvolvida a interface entre o ambiente ROS e o OMNeT++ com o HLA. Experimentos demonstraram que a perda de pacotes foi simulada corretamente, adicionando ao ambiente mais realismo

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

Submitted by Geandra Rodrigues (geandrar@gmail.com) on 2018-01-08T11:00:22Z No. of bitstreams: 1 marinaborgesarantesdesouza.pdf: 4073445 bytes, checksum: 18982e159219bb019d11c3f3604e9f38 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-01-22T15:25:04Z (GMT) No. of bitstreams: 1 marinaborgesarantesdesouza.pdf: 4073445 bytes, checksum: 18982e159219bb019d11c3f3604e9f38 (MD5) / Made available in DSpace on 2018-01-22T15:25:04Z (GMT). No. of bitstreams: 1 marinaborgesarantesdesouza.pdf: 4073445 bytes, checksum: 18982e159219bb019d11c3f3604e9f38 (MD5) Previous issue date: 2017-08-28 / CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico / Levando em conta a crescente utilização de sistemas robóticos em várias situações da atualidade, métodos que coordenam as atividades dos robôs são essenciais para se obter movimentos sincronizados e livres de possibilidades de colisão. Uma forma de coordená-los é através de métodos de otimização. O presente trabalho enfoca uma abordagem baseada em Programação Não Linear para encontrar perfis de velocidade ótimos para robôs com caminhos previamente especificados. A metodologia é aplicada em modelos de robôs móveis e manipuladores robóticos. Apesar das diferenças construtivas, de forma geral e para fins de coordenação, é permitido compartilhar, para os dois casos, a mesma formulação de otimização, fundamentada na maximização do quadrado da diferença de tempo em que os robôs atingem um mesmo ponto de colisão. Não obstante o grande número de trabalhos encontrados na literatura que envolvem o assunto, abordagens relacionadas a uma modelagem não linear do problema são escassas. A vantagem do método encontra-se na facilidade de representar não linearidades do sistema, como limitações de velocidade, aceleração e torque dos robôs. Além disso, a complexidade de formulação e resolução é reduzida em comparação com outros métodos que envolvem o tratamento de variáveis inteiras. O método também engloba situações em que os robôs podem se colidir na forma de segmentos. Para tanto, determina-se uma abordagem baseada na inclusão de pontos fictícios para representar tais trechos. Os testes foram realizados em diferentes sistemas de robôs móveis e manipuladores e os resultados comprovaram a eficiência da metodologia proposta, encontrando perfis de velocidade ótimos que determinam trajetórias sem acidentes. Comparações com Programação Linear Inteira Mista, amplamente utilizada para esse fim, comprovaram a superioridade da técnica apresentada, com relação à complexidade e modelagem de não linearidades, aproximando o modelo a sistemas reais. / The use of robotic systems in various industrial and logistics situations is increasing. Methods that coordinate the activities of the robots are essential to obtain synchronized and free of collision possibilities movements. One manner to coordinate them is through optimization methods. This work focuses on an approach based on Nonlinear Programming to determine optimal velocity profiles of robots with previously specified paths. The methodology is applied to mobile robots and manipulators models. Despite the constructive differences, in general, the same optimization formulation can be shared for both cases. The arrangement is based on the maximization of the square of the time difference in which the robots reach the same point of collision. Notwithstanding a large number of papers found in the literature involving the subject, approaches related to a nonlinear modeling of the problem are scarce. The advantage of the method lies in the easiness of representing nonlinearities of the system, such as speed, acceleration and torque limitations of robots. In addition, the formulation and resolution complexity is reduced compared to other methods encompassing the processing of integer variables. The method also encompasses situations where robots may collide in the form of segments. In this sense, an approach based on the inclusion of fictitious points to represent such stretches is determined. The tests were realized in different mobile robot and manipulators systems. The results proved the efficiency of the proposed methodology, finding optimal speed profiles that determine trajectories without accidents. Comparisons with Mixed Integer Linear Programming, widely used for this purpose, proved the superiority of the proposed technique with respect to the complexity and modeling of nonlinearities, bringing the model closer to real systems.

In this thesis, we examine algorithms for the division of labour in a group of robot. The algorithms make no use of direct communication. Instead, they are based only on the interactions among the robots and between the group and the environment.<p><p>Division of labour is the mechanism that decides how many robots shall be used to perform a task. The efficiency of the group of robots depends in fact on the number of robots involved in a task. If too few robots are used to achieve a task, they might not be successful or might perform poorly. If too many robots are used, it might be a waste of resources. The number of robots to use might be decided a priori by the system designer. More interestingly, the group of robots might autonomously select how many and which robots to use. In this thesis, we study algorithms of the latter type.<p><p>The robotic literature offers already some solutions, but most of them use a form of direct communication between agents. Direct, or explicit, communication between the robots is usually considered a necessary condition for co-ordination. Recent studies have questioned this assumption. The claim is based on observations of animal colonies, e.g. ants and termites. They can effectively co-operate without directly communicating, but using indirect forms of communication like stigmergy. Because they do not rely on communication, such colonies show robust behaviours at group level, a condition that one wishes also for groups of robots. Algorithms for robot co-ordination without direct communication have been proposed in the last few years. They are interesting not only because they are a stimulating intellectual challenge, but also because they address a situation that might likely occur when using robots for real-world out-door applications. Unfortunately, they are still poorly studied.<p><p>This thesis helps the understanding and the development of such algorithms. We start from a specific case to learn its characteristics. Then we improve our understandings through comparisons with other solutions, and finally we port everything into another domain.<p><p>We first study an algorithm for division of labour that was inspired by ants' foraging. We test the algorithm in an application similar to ants' foraging: prey retrieval. We prove that the model used for ants' foraging can be effective also in real conditions. Our analysis allows us to understand the underlying mechanisms of the division of labour and to define some way of measuring it.<p><p>Using this knowledge, we continue by comparing the ant-inspired algorithm with similar solutions that can be found in the literature and by assessing their differences. In performing these comparisons, we take care of using a formal methodology that allows us to spare resources. Namely, we use concepts of experiment design to reduce the number of experiments with real robots, without losing significance in the results.<p><p>Finally, we apply and port what we previously learnt into another application: Sensor/Actor Networks (SANETs). We develop an architecture for division of labour that is based on the same mechanisms as the ants' foraging model. Although the individuals in the SANET can communicate, the communication channel might be overloaded. Therefore, the agents of a SANET shall be able to co-ordinate without accessing the communication channel. / Doctorat en sciences appliquées / info:eu-repo/semantics/nonPublished

<div>Self-organization in a multi-robot system is a spontaneous process where some form of overall order arises from local interactions between robots in an initially disordered system. Cooperative coordination strategies for self-organization promote teamwork to complete a task while increasing the total utility of the system. In this dissertation, we apply prosocial behavioral concepts such as altruism and cooperation in multi-robot systems and investigate their effects on overall system performance on given tasks. We stress the significance of this research in long-term applications involving minimal to no human supervision, where self-sustainability of the multi-robot group is of utmost importance for the success of the mission at hand and system re-usability in the future.</div><div><br></div><div>For part of the research, we take bio-inspiration of cooperation from the huddling behavior of Emperor Penguins in the Antarctic which allows them to share body heat and survive one of the harshest environments on Earth as a group. A cyclic energy sharing concept is proposed for a convoying structured multi-robot group inspired from penguin movement dynamics in a huddle with carefully placed induction coils to facilitate directional energy sharing with neighbors and a position shuffling algorithm, allowing long-term survival of the convoy as a group in the field. Simulation results validate that the cyclic process allows individuals an equal opportunity to be at the center of the group identified as the most energy conserving position, and as a result robot groups were able to travel over 4 times the distance during convoying with the proposed method without any robot failing as opposed to without the shuffling and energy sharing process. </div><div><br></div><div>An artificial potential based Adaptive Inter-agent Spacing (AIS) control law is also proposed for efficient energy distribution in an unstructured multi-robot group aimed at long-term survivability goals in the field. By design, as an altruistic behavior higher energy bearing robots are dispersed throughout the group based on their individual energy levels to counter skewed initial distributions for faster group energy equilibrium attainment. Inspired by multi-huddle merging and splitting behavior of Emperor Penguins, a clustering and sequential merging based systematic energy equilibrium attainment method is also proposed as a supplement to the AIS controller. The proposed system ensures that high energy bearing agents are not over crowded by low energy bearing agents. The AIS controller proposed for the unstructured energy sharing and distribution process yielded 55%, 42%, 23% and 33% performance improvements in equilibrium attainment convergence time for skewed, bi-modal, normal and random initial agent resource level distributions respectively on a 2D plane using the proposed energy distribution method over the control method of no adaptive spacing. Scalability analysis for both energy sharing concepts confirmed their application with consistently improved performances different sized groups of robots. Applicability of the AIS controller as a generalized resource distribution method under certain constraints is also discussed to establish its significance in various multi-robot applications.</div><div><br></div><div>A concept of group based survival from damaging directional external stimuli is also adapted from the Emperor Penguin huddling phenomenon where individuals on the damaging stimuli side continuously relocate to the leeward side of the group following the group boundary using Gaussian Processes Machine Learning based global health-loss rate minima estimations in a distributed manner. The method relies on cooperation from all robots where individuals take turns being sheltered by the group from the damaging external stimuli. The distributed global health loss rate minima estimation allowed the development of two settling conditions. The global health loss rate minima settling method yielded 12.6%, 5.3%, 16.7% and 14.2% improvement in average robot health over the control case of no relocation, while an optimized health loss rate minima settling method further improved on the global health loss rate settling method by 3.9%, 1.9%, 1.7% and 0.6% for robot group sizes 26, 35, 70 and 107 respectively.</div><div><br></div><div>As a direct application case study of collaboration in multi-robot systems, a distributed shape formation strategy is proposed where robots act as beacons to help neighbors settle in a prescribed formation by local signaling. The process is completely distributed in nature and does not require any external control due to the cooperation between robots. Beacon robots looking for a robot to settle as a neighbor and continue the shape formation process, generates a surface gradient throughout the formed shape that allow robots to determine the direction of the structure forming frontier along the dynamically changing structure surface and eventually reach the closest beacon. Simulation experiments validate complex shape formation in 2D and 3D using the proposed method. The importance of group collaboration is emphasized in this case study without which the shape formation process would not be possible, without a centralized control scheme directing individual agents to specific positions in the structure. </div><div> </div><div>As the final application case study, a collaborative multi-agent transportation strategy is proposed for unknown objects with irregular shape and uneven weight distribution. Although, the proposed system is robust to single robot object transportation, the proposed methodology of transport is focused on robots regulating their effort while pushing objects from an identified pushing location hoping other robots support the object moment on the other end of the center of mass to prevent unintended rotation and create an efficient path of the object to the goal. The design of the object transportation strategy takes cooperation cues from human behaviors when coordinating pushing of heavy objects from two ends. Collaboration is achieved when pushing agents can regulate their effort with one another to maintain an efficient path for the object towards the set goal. Numerous experiments of pushing simple shapes such as disks and rectangular boxes and complex arbitrary shapes with increasing number of robots validate the significance and effectiveness of the proposed method. Detailed robustness studies of changing weight of objects during transportation portrayed the importance of cooperation in multi-agent systems in countering unintended drift effects of the object and maintain a steady efficient path to the goal. </div><div><br></div><div>Each case study is presented independent of one another with the Penguin huddling based self-organizations in response to internal and external stimuli focused on fundamental self-organization methods, and the structure formation and object transportation strategies focused on cooperation in specific applications. All case studies are validated by relevant simulation and experiments to establish the effectiveness of altruistic and cooperative behaviors in multi-robot systems.</div>

La détection et le suivi de cibles sont des missions fréquentes pour la robotique mobile, que le contexte soit civil, industriel ou militaire. Ces applications constituent un domaine de choix pour la planification multirobot, et sont abordées par de multiples communautés selon différents points de vue. Nous proposons dans un premier temps une taxonomie commune qui permetde regrouper et de comparer les différentes approches de ces problèmes, afin de mieux les analyser et de mettre en évidence leurs lacunes respectives. En particulier, on note la faible représentativité des modèles exploités, peu expressifs : la plupart des algorithmes évoluent dans un monde en deux dimensions où les observations et le déplacement sont conditionnés par lesmêmes obstacles. Ces modèles éloignés de la réalité nous semblent trop restrictifs pour pleinement exploiter la synergie des équipes multirobot hétérogènes : nous proposons une organisation des différents modèles nécessaires, en explicitant une séparation claire entre modèles et algorithmes de planification. Cette organisation est concrétisée par une librairie qui structure lesmodèles disponibles et définit les requêtes nécessaires aux algorithmes de planification. Dans un second temps, nous proposons un ensemble d’algorithmes utilisant les modèles définis précédemment pour planifier des missions de patrouille de zones et de poursuite de cibles. Ces algorithmes s’appuient sur un formalisme mathématique rigoureux afin d’étudier l’impact des modèlessur les performances. Nous analysons notamment l’impact sur la complexité – c’est-à-dire en quoi des modèles plus élaborés impactent la complexité de résolution – et sur la qualité des solutions résultantes, indépendamment des modèles, selon des métriques usuelles. D’une manière plus générale, les modèles sont un lien essentiel entre l’Intelligence Artificielle et la Robotique : leur enrichissement et leur étude approfondie permettent d’exhiber des comportements plus efficaces pour la réussite des missions allouées aux robots. Cette thèse contribue à démontrer l’importance des modèles pour la planification et la conduite de mission multirobots. / Detecting, localizing or following targets is at the core of numerous robotic applications in industrial, civilian and military application contexts. Much work has been devoted in various research communities to planning for such problems, each community with a different standpoint. Our thesis first provides a unifying taxonomy to go beyond the frontiers of specific communities and specific problems, and to enlarge the scope of prior surveys. We review various work related to each class of problems identified in the taxonomy, highlighting the different approaches, models and results. This analysis specifically points out the lack of representativityof the exploited models, which are in vast majority only 2D single-layer models where motion and sensing are mixed up. We consider those unrealistic models as too restrictive to handle the full synergistic potential of an heterogeneous team of cooperative robots. In response to this statement, we suggest a new organisation of the necessary models, stating clearly the links and separation between models and planning algorithms. This has lead to the development of a C++ library that structures the available models and defines the requests required by the planning process. We then exploit this library through a set of algorithms tackling area patrolling and target tracking. These algorithms are supported by a sound formalism and we study the impact of the models on the observed performances, with an emphasis on the complexity and the quality of the resultingsolutions. As a more general consideration, models are an essential link between Artificial Intelligence and applied Robotics : improving their expressiveness and studying them rigorously are the keys leading toward better robot behaviours and successful robotic missions. This thesis help to show how important the models are for planning and other decision processes formulti-robot missions.

This project proposes an adaptation of the dRRT* algorithm, a samplingbased multi-robot planner, for real-time industrial automation scenarios. The main objectives include optimizing computationally expensive sections of the algorithm, solving partially specified multi-robot problems, and evaluating the performance of the resulting method in various industry-like scenarios. The proposed algorithm, called Fast-dRRT*, aims to achieve highquality collision-free paths within strict time constraints. To accomplish this, the project introduces modifications to the dRRT* algorithm, such as the utilization of pre-computed swept volumes for efficient collision detection. The performance of four multi-robot planners, namely dRRT, ao-dRRT, dRRT*, and Fast-dRRT*, is evaluated through experiments on toy scenarios and industrial use cases. The results show that the proposed Fast-dRRT* algorithm outperforms the other planners in terms of finding solutions within the given time limits. It demonstrates improved efficiency, speed, and resilience to increased search spaces and the number of robots. The algorithm’s performance is further evaluated in real-world scenarios, including automotive assembly lines and warehouse automation, where it consistently outperforms dRRT* in terms of search speed and total planning time. Additionally, the algorithm successfully handles partially specified multi-robot problems, optimizing the overall movements’ cost. The study concludes that Fast-dRRT* is a promising option for real-time planning in industrial automation, offering reduced computation time and feasible solutions to complex multi-robot motion planning problems. / Detta projekt föreslår en anpassning av dRRT*-algoritmen, en samplingsbaserad multirobotplanerare, för realtidsscenarier inom industriell automation.. De huvudsakliga målen inkluderar optimering av beräkningskrävande delar av algoritmen, lösning av delvis specificerade multirobotproblem och utvärdering av den resulterande metodens prestanda i olika industriliknande scenarier. Den föreslagna algoritmen, kallad Fast-dRRT*, syftar till att uppnå högkvalitativa kollisionsfria banor inom strikta tidsbegränsningar. För att uppnå detta introducerar projektet modifieringar av dRRT*-algoritmen, såsom användning av förberäknade svepta volymer för effektiv kollisionsdetektering. Prestandan hos fyra multirobotplanerare, nämligen dRRT, ao-dRRT, dRRT* och Fast-dRRT*, utvärderas genom experiment på leksaksscenarier och industriella användningsfall. Resultaten visar att den föreslagna Fast-dRRT*- algoritmen överträffar de andra planerarna när det gäller att hitta lösningar inom de givna tidsgränserna. Den visar förbättrad effektivitet, hastighet och motståndskraft mot ökade sökutrymmen och antalet robotar. Algoritmens prestanda utvärderas vidare i verkliga scenarier, inklusive monteringslinjer för bilar och lagerautomation, där den konsekvent överträffar dRRT* när det gäller sökhastighet och total planeringstid. Dessutom hanterar algoritmen framgångsrikt delvis specificerade multirobotproblem och optimerar den totala rörelsekostnaden. Studien drar slutsatsen att Fast-dRRT* är ett lovande alternativ för realtidsplanering inom industriell automation, eftersom den erbjuder kortare beräkningstid och genomförbara lösningar på komplexa problem med rörelseplanering för multirobotar.

Space activities have been increasing dramatically in the past decades. As a result, the number of space debris has also increased significantly. Therefore, it is necessary to clean up and remove them to prevent a collision between space debris and spacecraft. In this thesis, we focus on load transportation using tethers, which connect multiple robots and loads together with lightweight cables. We propose a generalized framework to model and calculate the interaction force for the tethered multi-robot system. Then, we develop centralized and decentralized non-linear Model Predictive Control (MPC) controllers to complete a transportation task. Two simulators, a numerical and physical simulator, are presented and used to evaluate the performance of the controllers. The numerical simulator is used to verify the proposed model and evaluate the controllers for the ideal case. The physical simulator is then used to validate the performance of both centralized and decentralized controllers in real-time settings. Finally, we demonstrate how the proposed controllers perform in two and three-dimensional experiments. / Rymdaktiviteter har ökat dramatiskt under de senaste årtiondena. Som en följd av detta har mängden rymdskräp också ökat avsevärt. Därför är det nödvändigt att rensa upp och avlägsna detta skräp för att förhindra kollisioner mellan rymdskräp och rymdfarkoster. I denna rapport fokuserar vi på transporter av rymdobjekt som är sammanbundna via en lätt kabel. Vi föreslår en allmän metod för att modellera och beräkna interaktionskraften för det förenade multirobotsystemet. Sedan utvecklar vi centraliserad och decentraliserad icke-linjär modell-prediktiv reglering, MPC (eng. Model Predictive Control), för att uppnå transportuppgiften. Två simulatorer, en numerisk och en fysisk simulator, presenteras och används för att utvärdera styrsystemets prestanda. Den numeriska simuleringen används för att verifiera den föreslagna modellen och utforma styrsystemet för det idealiska fallet. Den fysiska simuleringen används sedan för att validera prestandan för både det centraliserade och decentraliserade styrsystem i realtid. Slutligen demonstrerar vi hur de föreslagna styrsystemen utför sig i tre- respektive två-dimensionella experiment.

For unmanned vehicles, autonomy means that the vehicle’s route can be planned and executed according to some pre-defined rules in the absence of human intervention. Autonomous vehicles (AVs) have become a common type of vehicle for various kinds of transport, for example autonomous forklifts within a warehouse environment. Volvo Autonomous Solution (VAS) works with autonomous vehicles in different areas. To better understand how different methods can be used for planning of autonomous vehicles, VAS initiated this project. To increase the efficiency of AVs, several problems can be examined. One such problem is the allocation problem, also called Multi-Robot Task Allocation, which aims to find out which vehicle should execute which task to achieve a global goal cooperatively. The AVs used by VAS handle Planning Missions (PMs). A PM is, for example, to move goods from a loading point to an unloading point. So, the problem examined in this study is how to assign PMs to vehicles in the most efficient way. The thesis also includes a collection of publications on the area. The problem is solved by using three methods: Mixed Integer Linear Programming (MILP), a Genetic Algorithm that was originally proposed for task assignment in a warehouse environment (GA – Warehouse), and a Genetic Algorithm that was initially proposed for train scheduling (GA – Train). With the MILP method, the problem has been formulated mathematically and the method guarantees an optimal solution. However, the major drawback of this approach is the large computational time required to retrieve a solution. The GA – Warehouse method has a quite simple allocation process but a more complicated path planning part and is, in its entirety, not as flexible as the other methods. The GA – Train method has a lower computational time and can consider many different aspects. All three methods generate similar solutions for the limited set of simple scenarios in this study, but an optimal solution can only be guaranteed by the MILP method. Regardless of which method is used, there is always a trade-off: a guarantee of the optimal solution at the expense of high computational time or a result where no optimal solution can be guaranteed but can be generated quickly. Which method to use depends on the context, what resources are available, and what requirements are placed on the solution. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

<p dir="ltr">The emergence of Autonomous Mobile Robots (AMR) signifies a pivotal shift in vehicle-based material handling systems, demonstrating their effectiveness across a broad spectrum of applications. Advancing beyond the traditional Automated Guided Vehicles (AGV), AMRs offer unprecedented flexibility in movement, liberated from electromagnetic guidance constraints. Their decentralized control architecture not only enables remarkable scalability but also fortifies system resilience through advanced conflict resolution mechanisms. Nevertheless, transitioning from AGV to AMR presents intricate challenges, chiefly due to the expanded complexity in path planning and task selection, compounded by the heightened potential for conflicts from their dynamic interaction capabilities. This dissertation confronts these challenges by fully leveraging the technological advancements of AMRs. A kinematic-enabled agent-based simulator was developed to replicate AMR system behavior, enabling detailed analysis of fleet dynamics and interactions within AMR intralogistics systems and their environments. Additionally, a comprehensive fleet management protocol was formulated to enhance the throughput of AMR-based intralogistics systems from an integrated perspective. A pivotal discovery of this research is the inadequacy of existing path planning protocols to provide reliable plans throughout their execution, leading to task allocation decisions based on inaccurate plan information and resulting in false optimality. In response, a novel machine learning enhanced probabilistic Multi-Robot Path Planning (MRPP) protocol was introduced to ensure the generation of dependable path plans, laying a solid foundation for task allocation decisions. The contributions of this dissertation, including the kinematic-enabled simulator, the fleet management protocol, and the MRPP protocol, are intended to pave the way for practical enhancements in autonomous vehicle-based material handling systems, fostering the development of solutions that are both innovative and applicable in industrial practices.<br></p>