19 research outputs found

    Adaptive and intelligent navigation of autonomous planetary rovers - A survey

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    The application of robotics and autonomous systems in space has increased dramatically. The ongoing Mars rover mission involving the Curiosity rover, along with the success of its predecessors, is a key milestone that showcases the existing capabilities of robotic technology. Nevertheless, there has still been a heavy reliance on human tele-operators to drive these systems. Reducing the reliance on human experts for navigational tasks on Mars remains a major challenge due to the harsh and complex nature of the Martian terrains. The development of a truly autonomous rover system with the capability to be effectively navigated in such environments requires intelligent and adaptive methods fitting for a system with limited resources. This paper surveys a representative selection of work applicable to autonomous planetary rover navigation, discussing some ongoing challenges and promising future research directions from the perspectives of the authors

    Poboljšani FastSLAM2.0 algoritam korištenjem ANFIS-a i PSO-a

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    FastSLAM2.0 is a framework for simultaneous localization of robot using a Rao-Blackwellized particle filter (RBPF). One of the problems of FastSLAM2.0 relates to the design of RBPF. The performance and quality of the estimation of RBPF depends heavily on the correct a priori knowledge of the process and measurement noise covariance matrices that are in most real-life applications unknown. On the other hand, an incorrect a priori knowledge may seriously degrade their performance. This paper presents an intelligent RBPF to solve this problem. In this method, two adaptive Neuro-Fuzzy inference systems (ANFIS) are used for tuning the process and measurement noise covariance matrices and for increasing acuuracy and consistency. In addition, we use particle swarm optimization (PSO) to optimize the performance of sampling. Experimental results demonstrate that the proposed algorithm is effective.FastSLAM2.0 je algoritam za istodobnu lokalizaciju robota i kartiranje prostora koji koristi Rao-Blackwell verziju čestičnog filtra (RBPF). Jedan od problema FastSLAM2.0 algoritma je u dizajnu samog RBPF-a. Performanse i kvaliteta estimacije RBPF-a značajno ovisi o apriori poznavanju procesa i matrica kovarijanci mjernog šuma koje su za većinu procesa iz stvarnog svijeta nepoznate. S druge strane pogrešno pretpostavka može značajno narušiti performanse. Ovaj rad predstavlja inteligentnu verziju RBPF-a koja rješava ovaj problem. Predstavljena metoda koristi dva adaptivna neizrazito-neuronska sustava (ANFIS) za podešavanje matrica kovarijanci procesnog i mjernog šuma čime se povećava točnost i konzistencija RBPF algoritma. Također koristi se i optimizacija roja čestica (PSO) za optimiziranje performansi otipkavanja. Eksperimentalni rezultati pokazuju efikasnost predloženog algoritma

    Learning data-derived vehicle motion models for use in localisation and mapping

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    Various solutions to the Simultaneous Localisation and Mapping (SLAM) problem have been proposed over the last 20 years. In particular, extending the fundamental solution of the SLAM problem has attracted a great deal of attention. Most extensions address shortcomings such as data association, computational complexity and improving predictions of a vehicle’s state. However, nearly all SLAM implementations still depend on analytical models to provide estimates for state transitions. Learning data-derived non-analytical models for use during localisation and mapping provides an alternative that could significantly improve estimates and increase the flexibility of models. A methodology to learn motion models without knowledge of the higher-order dynamics is therefore proposed using tapped delay-line neural networks (TDL-NN). Incorporating the learned Nth-order Markov model into a recursive Bayesian estimator requires that the learned model be assumed independent of the transitional model, forming a black box estimator. Both real-world and simulated training data were evaluated, along with changes to the input data’s format, to determine the best vehicle motion predictor. Furthermore, an evaluation methodology is defined to asses how well the models could learn each motion type. A comprehensive analysis of the one-forward prediction using various statistical measures was used to determine the most appropriate metric. The methodology evaluated the predictions at different levels of depth, providing supplementary information on the type of motions that are learnable. Outcomes of the experiments revealed that inherently learning a vehicle’s dynamics cannot be achieved using TDL-NNs. Currently the best that such an approach can learn is the delta between the vehicle’s states. Consequently, modifications are required to the learning algorithms as well as the input data’s format that will force the strategies to learn the higher-order dynamics.Dissertation (MEng)--University of Pretoria, 2018.Electrical, Electronic and Computer EngineeringMEngUnrestricte

    Global Localization based on Evolutionary Optimization Algorithms for Indoor and Underground Environments

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    Mención Internacional en el título de doctorA fully autonomous robot is defined by its capability to sense, understand and move within the environment to perform a specific task. These qualities are included within the concept of navigation. However, among them, a basic transcendent one is localization, the capacity of the system to know its position regarding its surroundings. Therefore, the localization issue could be defined as searching the robot’s coordinates and rotation angles within a known environment. In this thesis, the particular case of Global Localization is addressed, when no information about the initial position is known, and the robot relies only on its sensors. This work aims to develop several tools that allow the system to locate in the two most usual geometric map representations: occupancy maps and Point Clouds. The former divides the dimensional space into equally-sized cells coded with a binary value distinguishing between free and occupied space. Point Clouds define obstacles and environment features as a sparse set of points in the space, commonly measured through a laser sensor. In this work, various algorithms are presented to search for that position through laser measurements only, in contrast with more usual methods that combine external information with motion information of the robot, odometry. Therefore, the system is capable of finding its own position in indoor environments, with no necessity of external positioning and without the influence of the uncertainty that motion sensors typically induce. Our solution is addressed by implementing various stochastic optimization algorithms or Meta-heuristics, specifically those bio-inspired or commonly known as Evolutionary Algorithms. Inspired by natural phenomena, these algorithms are based on the evolution of a series of particles or population members towards a solution through the optimization of a cost or fitness function that defines the problem. The implemented algorithms are Differential Evolution, Particle Swarm Optimization, and Invasive Weed Optimization, which try to mimic the behavior of evolution through mutation, the movement of swarms or flocks of animals, and the colonizing behavior of invasive species of plants respectively. The different implementations address the necessity to parameterize these algorithms for a wide search space as a complete three-dimensional map, with exploratory behavior and the convergence conditions that terminate the search. The process is a recursive optimum estimation search, so the solution is unknown. These implementations address the optimum localization search procedure by comparing the laser measurements from the real position with the one obtained from each candidate particle in the known map. The cost function evaluates this similarity between real and estimated measurements and, therefore, is the function that defines the problem to optimize. The common approach in localization or mapping using laser sensors is to establish the mean square error or the absolute error between laser measurements as an optimization function. In this work, a different perspective is introduced by benefiting from statistical distance or divergences, utilized to describe the similarity between probability distributions. By modeling the laser sensor as a probability distribution over the measured distance, the algorithm can benefit from the asymmetries provided by these divergences to favor or penalize different situations. Hence, how the laser scans differ and not only how much can be evaluated. The results obtained in different maps, simulated and real, prove that the Global Localization issue is successfully solved through these methods, both in position and orientation. The implementation of divergence-based weighted cost functions provides great robustness and accuracy to the localization filters and optimal response before different sources and noise levels from sensor measurements, the environment, or the presence of obstacles that are not registered in the map.Lo que define a un robot completamente autónomo es su capacidad para percibir el entorno, comprenderlo y poder desplazarse en ´el para realizar las tareas encomendadas. Estas cualidades se engloban dentro del concepto de la navegación, pero entre todas ellas la más básica y de la que dependen en buena parte el resto es la localización, la capacidad del sistema de conocer su posición respecto al entorno que lo rodea. De esta forma el problema de la localización se podría definir como la búsqueda de las coordenadas de posición y los ángulos de orientación de un robot móvil dentro de un entorno conocido. En esta tesis se aborda el caso particular de la localización global, cuando no existe información inicial alguna y el sistema depende únicamente de sus sensores. El objetivo de este trabajo es el desarrollo de varias herramientas que permitan que el sistema encuentre la localización en la que se encuentra respecto a los dos tipos de mapa más comúnmente utilizados para representar el entorno: los mapas de ocupación y las nubes de puntos. Los primeros subdividen el espacio en celdas de igual tamaño cuyo valor se define de forma binaria entre espacio libre y ocupado. Las nubes de puntos definen los obstáculos como una serie dispersa de puntos en el espacio comúnmente medidos a través de un láser. En este trabajo se presentan varios algoritmos para la búsqueda de esa posición utilizando únicamente las medidas de este sensor láser, en contraste con los métodos más habituales que combinan información externa con información propia del movimiento del robot, la odometría. De esta forma el sistema es capaz de encontrar su posición en entornos interiores sin depender de posicionamiento externo y sin verse influenciado por la deriva típica que inducen los sensores de movimiento. La solución se afronta mediante la implementación de varios tipos de algoritmos estocásticos de optimización o Meta-heurísticas, en concreto entre los denominados bio-inspirados o comúnmente conocidos como Algoritmos Evolutivos. Estos algoritmos, inspirados en varios fenómenos de la naturaleza, se basan en la evolución de una serie de partículas o población hacia una solución en base a la optimización de una función de coste que define el problema. Los algoritmos implementados en este trabajo son Differential Evolution, Particle Swarm Optimization e Invasive Weed Optimization, que tratan de imitar el comportamiento de la evolución por mutación, el movimiento de enjambres o bandas de animales y la colonización por parte de especies invasivas de plantas respectivamente. Las distintas implementaciones abordan la necesidad de parametrizar estos algoritmos para un espacio de búsqueda muy amplio como es un mapa completo, con la necesidad de que su comportamiento sea muy exploratorio, así como las condiciones de convergencia que definen el fin de la búsqueda ya que al ser un proceso recursivo de estimación la solución no es conocida. Estos algoritmos plantean la forma de buscar la localización ´optima del robot mediante la comparación de las medidas del láser en la posición real con lo esperado en la posición de cada una de esas partículas teniendo en cuenta el mapa conocido. La función de coste evalúa esa semejanza entre las medidas reales y estimadas y por tanto, es la función que define el problema. Las funciones típicamente utilizadas tanto en mapeado como localización mediante el uso de sensores láser de distancia son el error cuadrático medio o el error absoluto entre distancia estimada y real. En este trabajo se presenta una perspectiva diferente, aprovechando las distancias estadísticas o divergencias, utilizadas para establecer la semejanza entre distribuciones probabilísticas. Modelando el sensor como una distribución de probabilidad entorno a la medida aportada por el láser, se puede aprovechar la asimetría de esas divergencias para favorecer o penalizar distintas situaciones. De esta forma se evalúa como difieren las medias y no solo cuanto. Los resultados obtenidos en distintos mapas tanto simulados como reales demuestran que el problema de la localización se resuelve con éxito mediante estos métodos tanto respecto al error de estimación de la posición como de la orientación del robot. El uso de las divergencias y su implementación en una función de coste ponderada proporciona gran robustez y precisión al filtro de localización y gran respuesta ante diferentes fuentes y niveles de ruido, tanto de la propia medida del sensor, del ambiente y de obstáculos no modelados en el mapa del entorno.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Fabio Bonsignorio.- Secretario: María Dolores Blanco Rojas.- Vocal: Alberto Brunete Gonzále

    Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

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    Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

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    The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

    Modelado, control y optimización de un sistema multi-robot autónomo para transporte inteligente

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    La robótica es el área de la ingeniería dedicada al diseño, construcción y control de máquinas capaces de resolver problemas a los humanos. Es una rama interdisciplinar que, aunando los conocimientos de diversas ciencias, ingenierías y tecnologías, busca imitar o extender las acciones y capacidades de las personas. Son incontables las labores en las que los robots demuestran un desempeño mayor que los humanos, como bien es consciente el sector industrial. Su velocidad, precisión y repetibilidad en tareas monótonas están un orden de magnitud por encima de las habilidades humanas. Sin embargo, la habilidad de establecer vínculos entre ellos, coordinarse e integrarse en su entorno de manera eficaz sigue siendo una tarea pendiente. Con esta finalidad, el presente trabajo expone los problemas diversos e interrelacionados entre sí que se deben abordar para conseguir aunar los esfuerzos de múltiples robots para conseguir un objetivo común. Estos retos comienzan con la percepción del entorno y la manera en la que se modela. Mediante dispositivos tipo lidar y el mapeado y localización simultáneos (SLAM) se consigue una descripción fiel del mundo que rodea al robot. Aun así, también se manifiesta en el trabajo las limitaciones que sufren estos dispositivos. La construcción de un mapa es la base sobre la que se asienta una navegación autónoma eficiente. Los algoritmos de navegación continúan estudiándose desde los inicios de la robótica móvil, sin embargo, cada uno de ellos ofrece un enfoque distinto que se adecua a cada caso. Por ello, se han llevado a cabo pruebas para comprobar cuales permiten desplazarse en un menor tiempo para su implementación en el robot. Una vez tiene la capacidad de navegar por sí mismo, el siguiente gran paso es otorgarle la habilidad de sortear obstáculos imprevistos. En el mundo real, y teniendo en mente la aplicación de estos robots en almacenes o invernaderos, estos podrán ser tanto estáticos como dinámicos. Aunando estos dos requerimientos, se ha demostrado que los algoritmos de navegación basados en bandas elásticas temporales ofrecen una gran velocidad y robustez en la navegación autónoma a la vez que muestran gran habilidad en el sorteo de obstáculos. Esto permite una navegación multi-agente en un mismo espacio físico, ya que se detectan mutuamente como impedimentos en su trayectoria y la modifican para no colisionar. Este control se ha implementado a nivel local, para asegurar una mayor tolerancia a fallos en caso de problemas de comunicación con el servidor central. Finalmente, la cooperación de la que se hablaba al inicio de este resumen se alcanza mediante un reparto eficiente de las tareas a realizar. El enfoque adoptado está basado en un esquema de subasta y licitador, en el que cada tarea es ejecutada por aquel robot que muestre una mayor predisposición para ello. De esta forma, se logra reducir a la mitad el tiempo requerido para llevar a cabo una serie de tareas de transporte cuando se duplica el número de robots en el sistema. Estos resultados se han comprobado tanto en simulación como con los robots físicos, validando así la implementación del sistema multi-robot. Abstract: Robotics is the branch of engineering devoted to design, construction and control of machines that can resolve human problems. It is an interdisciplinary research field which, combining various sciences, engineerings and technologies, aims to mimic or extend humans’ capacities. There are countless chores in which robots show a better performance than human beings, as the industrial sector is well aware of. Their speed, precision, and repeatability in monotonous jobs are in a superior order of magnitude. In contrast, their hability to establish links and coordinate between themselves and with their environment is still a pending task. In this regard, this work reveals the diverse and interrelated problems that arise when trying to merge cooperatively the efforts of a multi-agent system towards a common goal. These challenges begin with the perception of the world and the way to model it. With lidar-based simultaneous localization and mapping (SLAM), as for this project, a faithful description of the robots’ surroundings is accomplished. However, this devices present some limitations under certain conditions. Building a map is the essential pillar in which an efficient autonomous navigation bases its foundations. Navigation algorithms have been studied since the dawn of mobile robotics, but each of them offers a different approach that fits a particular requirement. For that reason, several experiments were carried out to determine the most suitable one in terms of time spent for its later implementation in the real robots. Once this demand is met, the next big step is to give the robot the hability to avoid unforeseen obstacles. In the real world, and minding the application of the robots in warehouses or greenhouses environments, these can be static or dynamic. Joining those two specifications, it has been shown that time elastic bands navigation algorithms are a great solution in means of speed, robustness in autonomous navigation as well as in the capability of obstacles avoidance. This allows multi-agent navigation in the same confined space, as they can detect each other and modify their trajectory to steer clear of the other’s. This control has been developed in a local way for a safer fault tolerance in case of communication issues with the central server. Finally, the cooperation this abstract started with, is achieved with an efficient distribution of tasks. This approach is based in a auctioneer and bidder scheme, in which each chore is executed by the most suited agent of the system. Therefore, the time required to perform these transport jobs is halved when the number of robots doubles. These results have been tested in simulation and real experiments, which validates the implementation of the multi-robot system

    A graph-theory-based C-space path planner for mobile robotic manipulators in close-proximity environments

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    In this thesis a novel guidance method for a 3-degree-of-freedom robotic manipulator arm in 3 dimensions for Improvised Explosive Device (IED) disposal has been developed. The work carried out in this thesis combines existing methods to develop a technique that delivers advantages taken from several other guidance techniques. These features are necessary for the IED disposal application. The work carried out in this thesis includes kinematic and dynamic modelling of robotic manipulators, T-space to C-space conversion, and path generation using Graph Theory to produce a guidance technique which can plan a safe path through a complex unknown environment. The method improves upon advantages given by other techniques in that it produces a suitable path in 3-dimensions in close-proximity environments in real time with no a priori knowledge of the environment, a necessary precursor to the application of this technique to IED disposal missions. To solve the problem of path planning, the thesis derives the kinematics and dynamics of a robotic arm in order to convert the Euclidean coordinates of measured environment data into C-space. Each dimension in C-space is one control input of the arm. The Euclidean start and end locations of the manipulator end effector are translated into C-space. A three-dimensional path is generated between them using Dijkstra’s Algorithm. The technique allows for a single path to be generated to guide the entire arm through the environment, rather than multiple paths to guide each component through the environment. The robotic arm parameters are modelled as a quasi-linear parameter varying system. As such it requires gain scheduling control, thus allowing compensation of the non-linearities in the system. A Genetic Algorithm is applied to tune a set of PID controllers for the dynamic model of the manipulator arm so that the generated path can then be followed using a conventional path-following algorithm. The technique proposed in this thesis is validated using numerical simulations in order to determine its advantages and limitations
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