Orientador: Fernando Jose Von Zuben / Acompanha CD-ROM / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-13T02:48:36Z (GMT). No. of bitstreams: 1 Cazangi_RenatoReder_D.pdf: 8716830 bytes, checksum: 272657e08f1aeb5622ebeb4412c49048 (MD5) Previous issue date: 2008 / Resumo: Novas técnicas de navegação autônoma de robôs móveis visam suprir a crescente demanda pelo emprego de robôs em diversos setores da sociedade e junto a uma ampla gama de tarefas. Os desafios envolvidos no desenvolvimento do sistema que controla o robô permitem afirmar que a inteligência embarcada em robôs atuais ainda encontra-se em um nível incipiente e limitado. Neste trabalho, cinco frentes de pesquisa complementares são propostas visando estudar, teórica e praticamente, aspectos fundamentais de projeto e implementação de controladores autônomos inteligentes para robótica móvel. Metodologias de computação bio-inspirada e de robótica evolutiva provêem os conceitos e ferramentas que fundamentam as cinco propostas, as quais são validadas com base em sistemas de navegação concebidos e aplicados a problemas relevantes da área. Uma série de simulações computacionais em ambientes virtuais e experimentos com robôs reais é realizada, permitindo medir o alcance das contribuições e apontar as principais frentes de atuação que se abrem como perspectivas futuras da pesquisa. / Abstract: Novel techniques for autonomous robot navigation aim at fulfilling the growing demand for mobile robots in multiple segments of society and in a plethora of tasks. The challenges involved in developing the system which controls the robot allow to say that the intelligence embedded in the current robots is found to be still incipient and limited. In this work, five complementary research fronts are proposed intending to study, theoretical and practically, aspects which are fundamental to the design and implementation of intelligent autonomous controllers for mobile robotics. Bio-inspired computing and evolutionary robotics methodologies provide the concepts and tools underlying the five proposals, which are validated through navigation systems devised and applied to important problems. Numerous real robot experiments as well as computational simulations taking place in virtual environments are carried out, allowing for the evaluation of contributions and also the discussion of future possibilities. / Doutorado / Engenharia de Computação / Doutor em Engenharia Elétrica

Orientador: Jose Raimundo de Oliveira / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e Computação / Made available in DSpace on 2018-08-08T12:33:26Z (GMT). No. of bitstreams: 1 Nascimento_JurandyAntoniodo_M.pdf: 1298680 bytes, checksum: eebec168aaccc2455ed7b9dad7ca1ab7 (MD5) Previous issue date: 2007 / Resumo: Devido ao crescente interesse em automação de rodovias e ao desenvolvimento e a viabilidade econômica de tecnologias de comunicação sem fio, surgiram nos últimos anos projetos para a comunicação entre veículos que possibilitam que dados de sensores locais, indicando situação de risco, sejam transmitidos para outros veículos de sua vizinhança para sinalizar a situação, ou mesmo para uma possível atuação no fluxo do tráfico local. É apresentada aqui uma infra-estrutura de comunicação sem fio que faz uso da especificação IEEE 802.15.4 para a comunicação entre veículos e que tem aplicações também em controle de sistemas, devido à sua característica versátil / Abstract: Due to the increasing interest on roads automation and to the development and the economic viability of wireless communication technologies, it has appeared in the last years projects to implement inter-vehicle communication links which makes possible that local sensors data, indicating risk situation, to be transmitted to others vehicles on its neighborhoods to signalize the situation or even to a possible actuation on the local traffic flow. It is presented here a wireless communication infrastructure that makes use of IEEE 802.15.4 specification for inter-vehicle communication and also for control systems applications due to its versatile characteristics / Mestrado / Engenharia de Computação / Mestre em Engenharia Elétrica

Orientador: Ely Carneiro de Paiva / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-22T23:12:16Z (GMT). No. of bitstreams: 1 Cordeiro_RafaeldeAngelis_M.pdf: 9321021 bytes, checksum: ff7bb113095dc020ed982e7d7c28311a (MD5) Previous issue date: 2013 / Resumo: Veículos terrestres autônomos tem recebido uma atenção especial dos estudos de robótica nos últimos anos. Suas aplicações incluem segurança na condução, exploração de locais inóspitos e automatização agrícola. O enfoque deste trabalho situa-se no projeto VERO, em parceria com o CTI, e tem por objetivo o desenvolvimento de aplicações de controle de trajetória para um veículo do tipo todo-terreno. Para tal, um modelo completo (dinâmico e tridimensional) é desenvolvido, com uma atenção especial para os modelos de interação entre solo e pneu, responsáveis pelas forças não lineares atuantes sobre o veículo. Em seguida, dois modelos reduzidos e linearizados são obtidos e estes são utilizados para a síntese de controladores LQR. Uma comparação entre os controladores é realizada e a resposta de um deles é detalhada para uma análise sobre a influência das características do modelo veicular sobre o controle do veículo. Por fim, três abordagens são propostas para melhorar a resposta obtida pelos controladores / Abstract: Autonomous ground vehicles have received special attention from robotics studies in past years. Their applications include advanced driver assistance systems (ADAS), exploration of inhospitable environments and harvest autonomous machines. In partnership with CTI, this master's thesis focuses in the development of path tracking controllers applied to off-road vehicles. In order to simulate vehicle characteristics, a complete three-dimensional nonlinear dynamic model was proposed with emphasis on tire-road interaction models, which are responsible for most of the vehicle's nonlinearities. In sequence, two vehicle reduced linear models are presented and applied to synthesize LQR controllers, whose results are compared. One of them was chosen to analyze the effect of vehicles's three-dimensional dynamics on path tracking control. Finally, three different approaches are proposed to enhance controllers performance / Mestrado / Planejamento de Sistemas Energeticos / Mestre em Engenharia Mecânica

Orientador: Fernando José Von Zuben / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-18T14:21:13Z (GMT). No. of bitstreams: 1 Azzolini_AlissonGusatti_M.pdf: 3567259 bytes, checksum: 633eb00350cdfa625d0e628fdf1f247e (MD5) Previous issue date: 2011 / Resumo: Há uma demanda crescente por soluções avançadas de navegação autônoma em robótica móvel. Apresenta-se então um sistema de síntese e aprendizagem de controladores com tal finalidade. Propõe-se um controlador probabilístico, consistindo no acoplamento de um processo de decisão de Markov parcialmente observável (POMDP) com um classificador logístico multinomial. A parametrização empregada para o POMDP inspira-se numa proposta anterior de controle de robô por meio de redes imunológicas artificiais, que mostrou apresentar flexibilidade e capacidade de representação de conhecimento na execução de tarefas desafiadoras de navegação autônoma. A aprendizagem dos parâmetros do classificador logístico é efetuada através de um algoritmo de aprendizagem por reforço baseado em gradiente de política, e os do POMDP, atráves de um algoritmo de maximização de verossimilhança. Três experimentos computacionais são efetuados, dois deles utilizando somente o classificador logístico, e o terceiro utilizando o acoplamento entre POMDP e classificador logístico. Os resultados permitem a constatação de pontos fortes e algumas deficiências das duas abordagens. O trabalho aponta também para uma potencial reinterpretação do controlador baseado em rede imunológica em termos de um modelo probabilístico similar ao proposto / Abstract: There is an increasing demand for advanced solutions in autonomous navigation of mobile robots. A system is presented for the synthesis and learning of controllers for such purpose. A probabilistic controller is proposed, consisting of the coupling of a partially observable Markov decision process (POMDP) with a multinomial logistic classifier. The parametrization used for the POMDP draws on an earlier proposal of robot control based on artificial immune networks, that has shown to present flexibility and knowledge representation capability in the execution of challenging autonomous navigation tasks. Learning the logistic classifier parameters is accomplished through a reinforcement learning algorithm based on policy gradient, while the POMDP parameters are learned by a likelihood maximization algorithm. Three computational experiments are performed, two of them using only the logistic classifier, and the third one using the coupling of a POMDP with a logistic classifier. The results show some strong points and drawbacks of both approaches. The work also points torwards a potential reinterpretation of the immune network based controller in terms of a probabilistic model similar to the one proposed / Mestrado / Engenharia de Computação / Mestre em Engenharia Elétrica

<p>Upon introducing semi- and fully automated vehicles on the road, drivers will be reluctant to focus on the traffic interaction and rely on the vehicles' decision-making. However, encountering pedestrians still poses a significant difficulty for modern automated driving technologies. Considering the high-level complexity in human behavior modeling to solve a real-world problem, deep-learning algorithms trained from naturalistic data have become promising solutions. Nevertheless, although developing such algorithms is achievable based on scene data collection and driver knowledge extraction, evaluation remains challenging due to the potential crash risks and limitations in acquiring ground-truth intention changes. </p> <p><br></p> <p>This study proposes a VR-based testing bed to evaluate real-time pedestrian intention algorithms as VR simulators are recognized for their affordability and adaptability in producing a variety of traffic situations, and it is more reliable to conduct human-factor research in autonomous cars. The pedestrian wears the head-mounted headset or uses the keyboard input and makes decisions in accordance with the circumstances. The simulator has added a credible and robust experience, essential for exhibiting the real-time behavior of the pedestrian. While crossing the road, there exists uncertainty associated with pedestrian intention. Our simulator will anticipate the crossing intention with consideration of the ambiguity of the pedestrian behavior. The case study has been performed over multiple subjects in several crossing conditions based on day-to-day life activities. It can be inferred from the study outcomes that the pedestrian intention can be precisely inferred using this VR-based simulator. However, depending on the speed of the car and the distance between the vehicle and the pedestrian, the accuracy of the prediction can differ considerably in some cases.</p>

In recent years, unmanned aerial vehicles (UAVs) have become a dominant force in the aerospace industry. Recent technological developments have moved these aircraft from remote operation roles to more active response missions. Of particular interest is the possibility of applying UAVs toward solving complex problems in long-endurance missions. Under that belief, the feasibility of utilizing UAVs for wildfire detection and response was investigated in a partnership that included NASA’s Aeronautics Research Mission Directorate and Science Mission Directorate, and the United States Forest Service. Under NASA’s Intelligent Mission Management (IMM) project, research was conducted to develop a mission architecture that would enable use of a high altitude UAV to search for reported wildfires with a separate low altitude UAV supporting ground assets. This research proposes a “straw man” concept incorporating both a High Altitude Long Endurance (HALE) UAV and a Low Altitude Short Endurance (LASE) UAV in a loosely coupled, low cost solution tailored towards wildfire response. This report identifies the communications architecture, algorithms, and required system configuration that meets the outlined goals of the IMM project by mitigating wildfires and addressing the United States Forest Service immediate needs. The end product is a defined parent-child framework capable of meeting all wildfire mission goals. The concept has been implemented in simulation, the results of which are presented in this report.

<p dir="ltr">The future of the global economy lies in space. As the economic and scientific benefits from space become more accessible and apparent to the public, the demand for more spacecrafts will only increase. However, simply using the current space architecture to sustain any major activities past low Earth orbit is infeasible. The limiting factor of relying on ground operators via the Deep Space Network will blunt future growth in cislunar space traffic as the bandwidth is insufficient to satisfy the needs of every spacecraft in this domain. For this reason, spacecrafts must begin to operate autonomously or semi-autonomously for operators to be able to manage more missions at a given time. This thesis focuses on the guidance and navigation policies that could help vehicles such as logistical or resupply spacecrafts perform their rendezvous autonomously. It is found that using GNSS signals and Moon-based optical navigation has the potential to help spacecrafts perform autonomous orbit determination in near-Moon trajectories. The estimations are high enough quality such that a stochastic controller can use this navigation solution to confidently guide the spacecraft to a target within a tolerance before proximity operations commence. As the reliance on the ground is shifted away, spacecrafts would be able to operate in greater numbers outside of Earth's lower orbits, greatly assisting humanity's presence in space. </p>

<p dir="ltr">Traffic safety and congestion are global concerns. Autonomous vehicles (AVs) are expected to enhance transportation safety and reduce congestion. However, achieving their full potential requires 100% market penetration, a challenging task. This study addresses key issues in mixed traffic environments, where human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs) coexist. A number of critical questions persist: 1) inadequate exploration of human errors (errors originating from non-CAV sources) in mixed traffic; 2): limited focus on information selection and learning efficiency in network-level rerouting, particularly in highly dynamic environments; 3) inadequacy of personalized element driver inputs in motion-planning frameworks; 4) lack of consideration of user privacy concerns.</p><p dir="ltr">With the goal of advancing the existing knowledge in this field and shedding light on these matters, this dissertation introduces multiple frameworks. These frameworks leverage connectivity and automation to improve safety and mobility in mixed traffic, addressing various research levels, including local-level and network-level safety enhancement, as well as network-level and global-level mobility enhancement. With optimization- and learning-based methods implemented (Model Predictive Control, Deep Neural Network, Deep Reinforcement Learning, Transformer model and Federated Learning), frameworks introduced in this dissertation are expected to help highway agencies and vehicle manufacturers improve the safety and efficiency of traffic flow in the mixed-traffic era. Our research findings revealed increased crash-avoidance rates in critical situations, enhanced accuracy in predicting lane changes, improved dynamic rerouting within urban areas, and the implementation of effective data-sharing mechanisms with a focus on user privacy. This research underscores the potential of connectivity and automation to significantly enhance mixed-traffic safety and mobility.</p>

<p dir="ltr">Path-tracking control is an integral part of motion planning in autonomous vehicles, in which the vehicle's lateral and longitudinal positions are controlled by a control system that will provide acceleration and steering angle commands to ensure accurate tracking of longitudinal and lateral movements in reference to a pre-defined trajectory. Extensive research has been conducted to address the growing need for efficient algorithms in this area. In this dissertation, a scenario and machine learning-based data-driven control approach is proposed for a path-tracking controller. Firstly, a Deep Reinforcement Learning model is developed to facilitate the control of longitudinal speed. A Deep Deterministic Policy Gradient algorithm is employed as the primary algorithm in training the reinforcement learning model. The main objective of this model is to maintain a safe distance from a lead vehicle (if present) or track a velocity set by the driver. Secondly, a lateral steering controller is developed using Neural Networks to control the steering angle of the vehicle with the main goal of following a reference trajectory. Then, a path-planning algorithm is developed using a hybrid A* planner. Finally, the longitudinal and lateral control models are coupled together to obtain a complete path-tracking controller that follows a path generated by the hybrid A* algorithm at a wide range of vehicle speeds. The state-of-the-art path-tracking controller is also built using Model Predictive Control and Stanley control to evaluate the performance of the proposed model. The results showed the effectiveness of both proposed models in the same scenario, in terms of velocity error, lateral yaw angle error, and lateral distance error. The results from the simulation show that the developed hybrid A* algorithm has good performance in comparison to the state-of-the-art path planning algorithms.</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>