Gaussian Processes for Machine Learning in Robotics

Mención Internacional en el título de doctorNowadays, machine learning is widely used in robotics for a variety of tasks such as perception, control, planning, and decision making. Machine learning involves learning, reasoning, and acting based on the data. This is achieved by constructing computer programs that process the data, extract useful information or features, make predictions to infer unknown properties, and suggest actions to take or decisions to make. This computer program corresponds to a mathematical model of the data that describes the relationship between the variables that represent the observed data and properties of interest. The aforementioned model is learned based on the available training data, which is accomplished using a learning algorithm capable of automatically adjusting the parameters of the model to agree with the data. Therefore, the architecture of the model needs to be selected accordingly, which is not a trivial task and usually depends on the machine-learning engineer’s insights and past experience. The number of parameters to be tuned varies significantly with the selected machine learning model, ranging from two or three parameters for Gaussian processes (GP) to hundreds of thousands for artificial neural networks. However, as more complex and novel robotic applications emerge, data complexity increases and prior experience may be insufficient to define adequate mathematical models. In addition, traditional machine learning methods are prone to problems such as overfitting, which can lead to inaccurate predictions and catastrophic failures in critical applications. These methods provide probabilistic distributions as model outputs, allowing for estimating the uncertainty associated with predictions and making more informed decisions. That is, they provide a mean and variance for the model responses. This thesis focuses on the application of machine learning solutions based on Gaussian processes to various problems in robotics, with the aim of improving current methods and providing a new perspective. Key areas such as trajectory planning for unmanned aerial vehicles (UAVs), motion planning for robotic manipulators and model identification of nonlinear systems are addressed. In the field of path planning for UAVs, algorithms based on Gaussian processes that allow for more efficient planning and energy savings in exploration missions have been developed. These algorithms are compared with traditional analytical approaches, demonstrating their superiority in terms of efficiency when using machine learning. Area coverage and linear coverage algorithms with UAV formations are presented, as well as a sea surface search algorithm. Finally, these algorithms are compared with a new method that uses Gaussian processes to perform probabilistic predictions and optimise trajectory planning, resulting in improved performance and reduced energy consumption. Regarding motion planning for robotic manipulators, an approach based on Gaussian process models that provides a significant reduction in computational times is proposed. A Gaussian process model is used to approximate the configuration space of a robot, which provides valuable information to avoid collisions and improve safety in dynamic environments. This approach is compared to conventional collision checking methods and its effectiveness in terms of computational time and accuracy is demonstrated. In this application, the variance provides information about dangerous zones for the manipulator. In terms of creating models of non-linear systems, Gaussian processes also offer significant advantages. This approach is applied to a soft robotic arm system and UAV energy consumption models, where experimental data is used to train Gaussian process models that capture the relationships between system inputs and outputs. The results show accurate identification of system parameters and the ability to make reliable future predictions. In summary, this thesis presents a variety of applications of Gaussian processes in robotics, from trajectory and motion planning to model identification. These machine learning-based solutions provide probabilistic predictions and improve the ability of robots to perform tasks safely and efficiently. Gaussian processes are positioned as a powerful tool to address current challenges in robotics and open up new possibilities in the field.El aprendizaje automático ha revolucionado el campo de la robótica al ofrecer una amplia gama de aplicaciones en áreas como la percepción, el control, la planificación y la toma de decisiones. Este enfoque implica desarrollar programas informáticos que pueden procesar datos, extraer información valiosa, realizar predicciones y ofrecer recomendaciones o sugerencias de acciones. Estos programas se basan en modelos matemáticos que capturan las relaciones entre las variables que representan los datos observados y las propiedades que se desean analizar. Los modelos se entrenan utilizando algoritmos de optimización que ajustan automáticamente los parámetros para lograr un rendimiento óptimo. Sin embargo, a medida que surgen aplicaciones robóticas más complejas y novedosas, la complejidad de los datos aumenta y la experiencia previa puede resultar insuficiente para definir modelos matemáticos adecuados. Además, los métodos de aprendizaje automático tradicionales son propensos a problemas como el sobreajuste, lo que puede llevar a predicciones inexactas y fallos catastróficos en aplicaciones críticas. Para superar estos desafíos, los métodos probabilísticos de aprendizaje automático, como los procesos gaussianos, han ganado popularidad. Estos métodos ofrecen distribuciones probabilísticas como salidas del modelo, lo que permite estimar la incertidumbre asociada a las predicciones y tomar decisiones más informadas. Esto es, proporcionan una media y una varianza para las respuestas del modelo. Esta tesis se centra en la aplicación de soluciones de aprendizaje automático basadas en procesos gaussianos a diversos problemas en robótica, con el objetivo de mejorar los métodos actuales y proporcionar una nueva perspectiva. Se abordan áreas clave como la planificación de trayectorias para vehículos aéreos no tripulados (UAVs), la planificación de movimientos para manipuladores robóticos y la identificación de modelos de sistemas no lineales. En el campo de la planificación de trayectorias para UAVs, se han desarrollado algoritmos basados en procesos gaussianos que permiten una planificación más eficiente y un ahorro de energía en misiones de exploración. Estos algoritmos se comparan con los enfoques analíticos tradicionales, demostrando su superioridad en términos de eficiencia al utilizar el aprendizaje automático. Se presentan algoritmos de recubrimiento de áreas y recubrimiento lineal con formaciones de UAVs, así como un algoritmo de búsqueda en superficies marinas. Finalmente, estos algoritmos se comparan con un nuevo método que utiliza procesos gaussianos para realizar predicciones probabilísticas y optimizar la planificación de trayectorias, lo que resulta en un rendimiento mejorado y una reducción del consumo de energía. En cuanto a la planificación de movimientos para manipuladores robóticos, se propone un enfoque basado en modelos gaussianos que permite una reducción significativa en los tiempos de cálculo. Se utiliza un modelo de procesos gaussianos para aproximar el espacio de configuraciones de un robot, lo que proporciona información valiosa para evitar colisiones y mejorar la seguridad en entornos dinámicos. Este enfoque se compara con los métodos convencionales de planificación de movimientos y se demuestra su eficacia en términos de tiempo de cálculo y precisión de los movimientos. En esta aplicación, la varianza proporciona información sobre zonas peligrosas para el manipulador. En cuanto a la identificación de modelos de sistemas no lineales, los procesos gaussianos también ofrecen ventajas significativas. Este enfoque se aplica a un sistema de brazo robótico blando y a modelos de consumo energético de UAVs, donde se utilizan datos experimentales para entrenar un modelo de proceso gaussiano que captura las relaciones entre las entradas y las salidas del sistema. Los resultados muestran una identificación precisa de los parámetros del sistema y la capacidad de realizar predicciones futuras confiables. En resumen, esta tesis presenta una variedad de aplicaciones de procesos gaussianos en robótica, desde la planificación de trayectorias y movimientos hasta la identificación de modelos. Estas soluciones basadas en aprendizaje automático ofrecen predicciones probabilísticas y mejoran la capacidad de los robots para realizar tareas de manera segura y eficiente. Los procesos gaussianos se posicionan como una herramienta poderosa para abordar los desafíos actuales en robótica y abrir nuevas posibilidades en el campo.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Juan Jesús Romero Cardalda.- Secretaria: María Dolores Blanco Rojas.- Vocal: Giuseppe Carbon

Implementación de algoritmos de planificación de trayectorias para robots móviles en entornos complejos

La planificación de trayectorias en robótica es un problema que ha recibido especial atención en los últimos años, debido a que los robots comienzan a estar muy presentes en la industria y en los hogares. Aunque estos robots pueden ser muy diferentes unos de otros, el problema de obtener trayectorias de un punto a otro del espacio evitando obstáculos es similar en todos ellos, ya sea un robot aspirador doméstico, como una Roomba, o un robot de rescate en entornos peligrosos. Con la finalidad de aportar soluciones a este problema, este trabajo fin de grado tiene como objetivo principal la implementación y el estudio de diferentes algoritmos de planificación para obtener trayectorias válidas. Para ello es necesario disponer de (i) un modelo del entorno sobre el cual realizar la tarea de planificación, (ii) un origen y (iii) un destino. En el TFG se comparan tres algoritmos con diferentes características y comportamientos. Por un lado, se ha estudiado el algoritmo A*, basado en búsqueda en grafos y muy utilizado en la actualidad. Por otro lado, como alternativas con menor coste de computación que el A*, se han estudiado el algoritmo basado en muestreo RRT, y una modificación anytime-optimal del mismo, el RRT*. Con la finalidad de evaluar los diferentes algoritmos de planificación propuestos, se realizan ensayos en entornos de diferente tamaño y complejidad y se comparan los resultados obtenidos. Estos ensayos nos permiten observar las ventajas y desventajas de los algoritmos estudiados, pudiendo elegir el algoritmo idóneo para cada situación. Por último, en el TFG se realiza una implementación en un robot real mediante la plataforma ROS.<br /

FM2 path planner for UAV applications with curvature constraints: a comparative analysis with other planning approaches

This paper studies the Fast Marching Square (FM2) method as a competitive path planner for UAV applications. The approach fulfills trajectory curvature constraints together with a significantly reduced computation time, which makes it overperform with respect to other planning methods of the literature based on optimization. A comparative analysis is presented to demonstrate how the FM2 approach can easily adapt its performance thanks to the introduction of two parameters, saturation a and exponent b, that allow a flexible configuration of the paths in terms of curvature restrictions, among others. The main contributions of the method are twofold: first, a feasible path is directly obtained without the need of a later optimization process to accomplish curvature restrictions; second, the computation speed is significantly increased, up to 220 times faster than other optimization-based methods such as, for instance, Dubins, Euler–Mumford Elastica and Reeds–Shepp. Simulation results are given to demonstrate the superiority of the method when used for UAV applications in comparison with the three previously mentioned methods.This research was funded by the EUROPEAN COMMISSION: Innovation and Networks Executive Agency (INEA), grant number 861696-LABYRINTH

Coverage strategy for target location in marine environments using fixed-wing UAVs

In this paper, we propose a coverage method for the search of lost targets or debris on the ocean surface. The OSCAR data set is used to determine the marine currents and the differential evolution genetic filter is used to optimize the sweep direction of the lawnmower coverage and get the sweep angle for the maximum probability of containment. The position of the target is determined by a particle filter, where the particles are moved by the ocean currents and the final probabilistic distribution is obtained by fitting the particle positions to a Gaussian probability distribution. The differential evolution algorithm is then used to optimize the sweep direction that covers the highest probability of containment cells before the less probable ones. The algorithm is tested with a variety of parameters of the differential evolution algorithm and compared to other popular optimization algorithms.This research was funded by the European Commission: Innovation and Networks Executive Agency (INEA), through the European H2020 LABYRINTH project. Grant agreement H2020-MG-2019-TwoStages-861696

Multi UAV coverage path planning in urban environments

This article belongs to the Special Issue Efficient Planning and Mapping for Multi-Robot Systems.Coverage path planning (CPP) is a field of study which objective is to find a path that covers every point of a certain area of interest. Recently, the use of Unmanned Aerial Vehicles (UAVs) has become more proficient in various applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. The aim of this paper is to design efficient coverage path planning collision-avoidance capable algorithms for single or multi UAV systems in cluttered urban environments. Two algorithms are developed and explored: one of them plans paths to cover a target zone delimited by a given perimeter with predefined coverage height and bandwidth, using a boustrophedon flight pattern, while the other proposed algorithm follows a set of predefined viewpoints, calculating a smooth path that ensures that the UAVs pass over the objectives. Both algorithms have been developed for a scalable number of UAVs, which fly in a triangular deformable leader-follower formation with the leader at its front. In the case of an even number of UAVs, there is no leader at the front of the formation and a virtual leader is used to plan the paths of the followers. The presented algorithms also have collision avoidance capabilities, powered by the Fast Marching Square algorithm. These algorithms are tested in various simulated urban and cluttered environments, and they prove capable of providing safe and smooth paths for the UAV formation in urban environments.This research was funded by the EUROPEAN COMMISSION: Innovation and Networks Executive Agency (INEA), through the European H2020 LABYRINTH project. Grant agreement H2020-MG-2019-TwoStages-861696

Path planning and collision risk management strategy for multi-UAV systems in 3D environments

This article belongs to the Special Issue Smooth Motion Planning for Autonomous VehiclesMulti-UAV systems are attracting, especially in the last decade, the attention of researchers and companies of very different fields due to the great interest in developing systems capable of operating in a coordinated manner in complex scenarios and to cover and speed up applications that can be dangerous or tedious for people: search and rescue tasks, inspection of facilities, delivery of goods, surveillance, etc. Inspired by these needs, this work aims to design, implement and analyze a trajectory planning and collision avoidance strategy for multi-UAV systems in 3D environments. For this purpose, a study of the existing techniques for both problems is carried out and an innovative strategy based on Fast Marching Square¿for the planning phase¿and a simple priority-based speed control¿as the method for conflict resolution¿is proposed, together with prevention measures designed to try to limit and reduce the greatest number of conflicting situations that may occur between vehicles while they carry out their missions in a simulated 3D urban environment. The performance of the algorithm is evaluated successfully on the basis of certain conveniently chosen statistical measures that are collected throughout the simulation runs.This research was funded by the EUROPEAN COMMISSION: Innovation and Networks Executive Agency (INEA), through the European H2020 LABYRINTH project. Grant agreement H2020-MG-2019-TwoStages-861696

Fast Marching Square-based planning and conflict management strategy for UAVs in large 3D environments

[Resumen] Los sistemas multi UAV se alzan en la actualidad como una solución potente a la hora de desempeñar y agilizar tareas que pueden resultar peligrosas o tediosas para las personas: tareas de búsqueda y rescate, inspección y vigilancia de instalaciones, entrega de mercancías, tareas de agricultura y conservación de la vida silvestre, etc. En este contexto, este trabajo propone una estrategia rápida de planicación de trayectorias en entornos 3D de grandes dimensiones para UAVs basada en Fast Marching Square y un sencillo y eficiente control de velocidad basado en prioridades como método de resolución de conflictos entre vehículos. El rendimiento del algoritmo se evalúa en base a ciertas medidas estadísticas recogidas convenientemente a lo largo de simulaciones.[Abstract] Multi-UAV systems are currently emerging as a powerful solution to perform and speed up tasks that can be dangerous or tedious for people: search and rescue tasks, inspection and surveillance of facilities, delivery of goods, agriculture and wildlife conservation tasks, etc. In this context, this work proposes a fast trajectory planning strategy in large 3D environments for UAVs based on Fast Marching Square and a simple and efficient priority-based speed control method for resolving conflicts between vehicles. The performance of the algorithm is evaluated based on certain statistical measures collected throughout simulations.Comunidad de Madrid; S2018/NMT-433