84 research outputs found

    Combining reinforcement learning and conventional control to improve automatic guided vehicles tracking of complex trajectories

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    With the rapid growth of logistics transportation in the framework of Industry 4.0, automated guided vehicle (AGV) technologies have developed speedily. These systems present two coupled control problems: the control of the longitudinal velocity, essential to ensure the application requirements such as throughput and tag time, and the trajectory tracking control, necessary to ensure the proper accuracy in loading and unloading manoeuvres. When the paths are very short or have abrupt changes, the kinematic constraints play a restrictive role, and the tracking control becomes more challenging. In this case, advanced control strategies such as those based on intelligent techniques, including machine learning (ML) can be useful. Hence, in this work, we present an intelligent hybrid control scheme that combines reinforcement learning-based control (RLC) with conventional PI regulators to face both control problems simultaneously. On the one hand, PIs are used to control the speed of each wheel. On the other hand, the input reference of these regulators is calculated by the RLC in order to reduce the guiding error of the path tracking and to maintain the longitudinal speed. The latter is compared with a PID path following controller. The PID regulators have been tuned by genetic algorithms. The RLC allows the vehicle to learn how to improve the trajectory tracking in an adaptive way and thus, the AGV can face disturbances or unknown physical system parameters that may change due to friction and degradation of AGV mechanical components. Extensive simulation experiments of the proposed intelligent control strategy on a hybrid tricycle and differential AGV model, that considers the kinematics and the dynamics of the vehicle, prove the efficiency of the approach when following different demanding trajectories. The performance of the RL tracking controller in comparison with the optimized PID gives errors around 70% smaller, and the average maximum error is also 48% lower.Open access funding enabled and organized by Projekt DEAL

    Navigation and Control of Automated Guided Vehicle using Fuzzy Inference System and Neural Network Technique

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    Automatic motion planning and navigation is the primary task of an Automated Guided Vehicle (AGV) or mobile robot. All such navigation systems consist of a data collection system, a decision making system and a hardware control system. Artificial Intelligence based decision making systems have become increasingly more successful as they are capable of handling large complex calculations and have a good performance under unpredictable and imprecise environments. This research focuses on developing Fuzzy Logic and Neural Network based implementations for the navigation of an AGV by using heading angle and obstacle distances as inputs to generate the velocity and steering angle as output. The Gaussian, Triangular and Trapezoidal membership functions for the Fuzzy Inference System and the Feed forward back propagation were developed, modelled and simulated on MATLAB. The reserach presents an evaluation of the four different decision making systems and a study has been conducted to compare their performances. The hardware control for an AGV should be robust and precise. For practical implementation a prototype, that functions via DC servo motors and a gear systems, was constructed and installed on a commercial vehicle

    Fuzzy Controllers

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    Trying to meet the requirements in the field, present book treats different fuzzy control architectures both in terms of the theoretical design and in terms of comparative validation studies in various applications, numerically simulated or experimentally developed. Through the subject matter and through the inter and multidisciplinary content, this book is addressed mainly to the researchers, doctoral students and students interested in developing new applications of intelligent control, but also to the people who want to become familiar with the control concepts based on fuzzy techniques. Bibliographic resources used to perform the work includes books and articles of present interest in the field, published in prestigious journals and publishing houses, and websites dedicated to various applications of fuzzy control. Its structure and the presented studies include the book in the category of those who make a direct connection between theoretical developments and practical applications, thereby constituting a real support for the specialists in artificial intelligence, modelling and control fields

    Immunity-Based Accommodation of Aircraft Subsystem Failures

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    This thesis presents the design, development, and flight-simulation testing of an artificial immune system (AIS) based approach for accommodation of different aircraft subsystem failures.;Failure accommodation is considered as part of a complex integrated AIS scheme that contains four major components: failure detection, identification, evaluation, and accommodation. The accommodation part consists of providing compensatory commands to the aircraft under specific abnormal conditions based on previous experience. In this research effort, the possibility of building an AIS allowing the extraction of pilot commands is investigated.;The proposed approach is based on structuring the self (nominal conditions) and the non-self (abnormal conditions) within the AIS paradigm, as sets of artificial memory cells (mimicking behavior of T-cells, B-cells, and antibodies) consisting of measurement strings, over pre-defined time windows. Each string is a set of features values at each sample time of the flight including pilot inputs, system states, and other variables. The accommodation algorithm relies on identifying the memory cell that is the most similar to the in-coming measurements. Once the best match is found, control commands corresponding to this match will be extracted from the memory and used for control purposes.;The proposed methodology is illustrated through simulation of simple maneuvers at nominal flight conditions, different actuators, and sensor failure conditions. Data for development and demonstration have been collected from West Virginia University 6-degrees-of-freedom motion-based flight simulator. The aircraft model used for this research represents a supersonic fighter which includes model following adaptive control laws based on non-linear dynamic inversion and artificial neural network augmentation.;The simulation results demonstrate the possibility of extracting pilot compensatory commands from the self/non-self structure and the capability of the AIS paradigm to address the problem of accommodating actuator and sensor malfunctions as a part of a comprehensive and integrated framework along with abnormal condition detection, identification, and evaluation

    Navigation of mobil robot using fuzzy logic controller

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    This chapter gives an overview of the research work reported in the thesis. First, the background of the research and the chosen problem domain are outlined. Then, the objectives of this research work are described. Finally, an outline of the thesis content is provided

    Validation of trajectory planning strategies for automated driving under cooperative, urban, and interurban scenarios.

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    149 p.En esta Tesis se estudia, diseña e implementa una arquitectura de control para vehículos automatizados de forma dual, que permite realizar pruebas en simulación y en vehículos reales con los mínimos cambios posibles. La arquitectura descansa sobre seis módulos: adquisición de información de sensores, percepción del entorno, comunicaciones e interacción con otros agentes, decisión de maniobras, control y actuación, además de la generación de mapas en el módulo de decisión, que utiliza puntos simples para la descripción de las estructuras de la ruta (rotondas, intersecciones, tramos rectos y cambios de carril)Tecnali

    Optimization and Mathematical Modelling for Path Planning of Co-operative Intra-logistics Automated Vehicles

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    Small indoor Autonomous Vehicles have revolutionized the operation of pick-pack-and-ship warehouses. The challenges for path planning and co-operation in this domain stem from uncontrolled environments including workspaces shared with humans and human-operated vehicles. Solutions are needed which scale up to the largest existing sites with thousands of vehicles and beyond. These challenges might be familiar to anyone modelling road traffic control with the introduction of Autonomous Vehicles, but key differences in the level of decision autonomy lead to different approaches to conflict-resolution. This thesis proposes a decomposition of site-wide conflict-free motion planning into individual shortest paths though a roadmap representing the free space across the site, zone-based speed optimization to resolve conflicts in the vicinity of one intersection and individual path optimization for local obstacles. In numerical tests the individual path optimization based on clothoid basis functions created paths traversable by different vehicle configurations (steering rate limit, lateral acceleration limit and wheelbase) only by choosing an appropriate maximum longitudinal speed. Using two clothoid segments per convex region was sufficient to reach any goal, and the problem could be solved reliably and quickly with sequential quadratic programming due to the approximate graph method used to determine a good sequence of obstacle-free regions to the local goal. A design for zone-based intersection management, obtained by minimizing a linear objective subject to quadratic constraints was refined by the addition of a messaging interface compatible with the path adaptations based on clothoids. A new approximation of the differential constraints was evaluated in a multi-agent simulation of an elementary intersection layout. The proposed FIFO ordering heuristic converted the problem into a linear program. Interior point methods either found a solution quickly or showed that the problem was infeasible, unlike a quadratic constraint formulation with ordering flexibility. Subsequent tests on more complex multi-lane intersection geometries showed the quadratic constraint formulation converged to significantly better solutions than FIFO at the cost of longer and unpredictable search time. Both effects were magnified as the number of vehicles increased. To properly address site-wide conflict-free motion planning, it is essential that the local solutions are compatible with each other at the zone boundaries. The intersection management design was refined with new boundary constraints to ensure compatibility and smooth transitions without the need for a backup system. In numerical tests it was found that the additional boundary constraints were sufficient to ensure smooth transitions on an idealized map including two intersections

    Feasible, Robust and Reliable Automation and Control for Autonomous Systems

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    The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

    A Novel Obstacle Avoidance Approach For Nonholonomic Ground Vehicle Autonomy

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    Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2012Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2012Bu çalışmada, holonom olmayan bir kara taşıtı için, “Boşluğu Takip Et” (BTE) isimli yeni bir engelden kaçma ve çarpışma önleme metodu geliştirilmiştir. Bu metod, probleme yeni bir çözüm getirmektedir ve diğer metodlara göre çeşitli avantajlara sahiptir. Geliştirilen metodun, benzer metodlarla yapılan karşılaştırılmalar sonucunda, daha güvenli güzergahlarla sonuçlandığı gösterilmiştir. Ayrıca BTE, yapay potansiyel alanlar (YPA) metodu ve bu tabanda çalışan diğer tüm metodların ortak problemi olan lokal minimum probleminden bağımsızdır. BTE’nin bir diğer özelliği, aracın holonom olmayan kısıtlarını ve sensörlerin görüş açısı kısıtlarını da göz önünde bulundurabilmesidir. BTE’nin tamamen reaktif yapısı sayesinde, yalnızca durağan engellerden değil, hareketli engellerden de rahatlıkla sıyrıldığı da tez içerisinde gösterilmiştir. Son olarak, sadece bir ayar parametresine sahip olduğu için, kullanımı da oldukça kolaydır. Engelden kaçınmak için, yalnızca aracın yönelim açısının belirlenmesinin yetmeyeceği düşüncesinden hareketle, aracın engelli bir ortamda hız planlaması için de yeni bir metod geliştirilmiştir. İki adet bulanık çıkarım sisteminin (BÇS) tasarlanmasıyla oluşturulan bu yeni yapı, engellerin oluşturduğu risk durumuna ve aracın yönelim açısına bağlı olarak çalışır. Planlanan hızın takip edilmesi için de yine bulanık mantık kullanılarak yeni bir alt seviye hız kontrolörü tasarlanmıştır. Tasarlanan tüm metodlar, literatürdeki bezerleriyle simülasyon ortamında karşılaştırılmış ve sonuçları gösterilmiştir. Geliştirilen her üç yeni metod, tam otonom kara taşıtı (OKT) üzerinde deneysel olarak da test edilerek sonuçların başarılı olduğu gösterilmiştir. Simülasyonlarda kullanılan araç modelleri ve deneysel düzeneğin tasarımı da tez içerisinde ayrı bölümler halinde anlatılmıştır.In this study, a new obstacle avoidance algorithm “Follow the Gap Method” (FGM) is designed for nonholonomic ground vehicle autonomy. The proposed method brings a new solution to the problem and has several advantages compared to previous methods. Fisrstly, the FGM results in safer trajectories than other compared approaches. This new method is free from local minima which is a big problem for Artificial Potential Fields (APF) and similar methods. Taking into consideration the field of view and the nonholonomic constraints of the vehicle is another advantage of the FGM. Through the purely reactive nature of the FGM, it is shown that not only the static but also the dynamic obstacles are avoided. Besides these, it is easy to tune the algorithm with only one tuning parameter. Vehicle speed is as important as the appropriate steering angle for obstacle avoidance. From this view point, a new speed planning method is designed for the vehicle. Two fuzzy inference systems operate depending on the danger level of the obstacles and the steering angle. In order to track the speed commands from the speed planner, a new low level speed controller is designed based on fuzzy rules. All designed methods are simulated and compared with other methods in literature. The designed methods are also tested experimentally using the real unmanned ground vehicle (UGV) platform and it is shown that experimental results are successful too. The used models for the simulations and designed experimental platform are illustrated in separated sections throughout the thesis.DoktoraPh
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