MULTI-RATE VISUAL FEEDBACK ROBOT CONTROL

[EN] This thesis deals with two characteristic problems in visual feedback robot control: 1) sensor latency; 2) providing suitable trajectories for the robot and for the measurement in the image. All the approaches presented in this work are analyzed and implemented on a 6 DOF industrial robot manipulator or/and a wheeled robot. Focusing on the sensor latency problem, this thesis proposes the use of dual-rate high order holds within the control loop of robots. In this sense, the main contributions are: - Dual-rate high order holds based on primitive functions for robot control (Chapter 3): analysis of the system performance with and without the use of this multi-rate technique from non-conventional control. In addition, as consequence of the use of dual-rate holds, this work obtains and validates multi-rate controllers, especially dual-rate PIDs. - Asynchronous dual-rate high order holds based on primitive functions with time delay compensation (Chapter 3): generalization of asynchronous dual-rate high order holds incorporating an input signal time delay compensation component, improving thus the inter-sampling estimations computed by the hold. It is provided an analysis of the properties of such dual-rate holds with time delay compensation, comparing them with estimations obtained by the equivalent dual-rate holds without this compensation, as well as their implementation and validation within the control loop of a 6 DOF industrial robot manipulator. - Multi-rate nonlinear high order holds (Chapter 4): generalization of the concept of dual-rate high order holds with nonlinear estimation models, which include information about the plant to be controlled, the controller(s) and sensor(s) used, obtained from machine learning techniques. Thus, in order to obtain such a nonlinear hold, it is described a methodology non dependent of the machine technique used, although validated using artificial neural networks. Finally, an analysis of the properties of these new holds is carried out, comparing them with their equivalents based on primitive functions, as well as their implementation and validation within the control loop of an industrial robot manipulator and a wheeled robot. With respect to the problem of providing suitable trajectories for the robot and for the measurement in the image, this thesis presents the novel reference features filtering control strategy and its generalization from a multi-rate point of view. The main contributions in this regard are: - Reference features filtering control strategy (Chapter 5): a new control strategy is proposed to enlarge significantly the solution task reachability of robot visual feedback control. The main idea is to use optimal trajectories proposed by a non-linear EKF predictor-smoother (ERTS), based on Rauch-Tung-Striebel (RTS) algorithm, as new feature references for an underlying visual feedback controller. In this work it is provided both the description of the implementation algorithm and its implementation and validation utilizing an industrial robot manipulator. - Dual-rate Reference features filtering control strategy (Chapter 5): a generalization of the reference features filtering approach from a multi-rate point of view, and a dual Kalman-smoother step based on the relation of the sensor and controller frequencies of the reference filtering control strategy is provided, reducing the computational cost of the former algorithm, as well as addressing the problem of the sensor latency. The implementation algorithms, as well as its analysis, are described.[ES] La presente tesis propone soluciones para dos problemas característicos de los sistemas robóticos cuyo bucle de control se cierra únicamente empleando sensores de visión artificial: 1) la latencia del sensor; 2) la obtención de trayectorias factibles tanto para el robot así como para las medidas obtenidas en la imagen. Todos los métodos propuestos en este trabajo son analizados, validados e implementados utilizando brazo robot industrial de 6 grados de libertad y/o en un robot con ruedas. Atendiendo al problema de la latencia del sensor, esta tesis propone el uso de retenedores bi-frequencia de orden alto dentro de los lazos de control de robots. En este aspecto las principales contribuciones son: -Retenedores bi-frecuencia de orden alto basados en funciones primitivas dentro de lazos de control de robots (Capítulo 3): análisis del comportamiento del sistema con y sin el uso de esta técnica de control no convencional. Además, como consecuencia del empleo de los retenedores, obtención y validación de controladores multi-frequencia, concretamente de PIDs bi-frecuencia. -Retenedores bi-frecuencia asíncronos de orden alto basados en funciones primitivas con compensación de retardos (Capítulo 3): generalización de los retenedores bi-frecuencia asíncronos de orden alto incluyendo una componente de compensación del retardo en la señal de entrada, mejorando así las estimaciones inter-muestreo calculadas por el retenedor. Se proporciona un análisis de las propiedades de los retenedores con compensación del retardo, comparándolas con las obtenidas por sus predecesores sin compensación, así como su implementación y validación en un brazo robot de 6 grados de libertad. -Retenedores multi-frecuencia no lineales de orden alto (Capítulo 4): generalización del concepto de retenedor bi-frecuencia de orden alto con modelos de estimación no lineales, los cuales incluyen información tanto de la planta a controlar, como del controlador(es) y sensor(es) empleado(s), obtenida a partir de técnicas de aprendizaje. Así pues, para obtener dicho retenedor no lineal, se describe una metodología independiente de la herramienta de aprendizaje utilizada, aunque validada con el uso de redes neuronales artificiales. Finalmente se realiza un análisis de las propiedades de estos nuevos retenedores, comparándolos con sus predecesores basados en funciones primitivas, así como su implementación y validación en un brazo robot de 6 grados de libertad y en un robot móvil con ruedas. Por lo que respecta al problema de generación de trayectorias factibles para el robot y para la medida en la imagen, esta tesis propone la nueva estrategia de control basada en el filtrado de la referencia y su generalización desde el punto de vista multi-frecuencial. -Estrategia de control basada en el filtrado de la referencia (Capítulo 5): una nueva estrategia de control se propone para ampliar significativamente el espacio de soluciones de los sistemas robóticos realimentados con sensores de visión artificial. La principal idea es utilizar las trayectorias óptimas obtenidas por una trayectoria predicha por un filtro de Kalman seguido de un suavizado basado en el algoritmo Rauch-Tung-Striebel (RTS) como nuevas referencias para un controlador dado. En este trabajo se proporciona tanto la descripción del algoritmo como su implementación y validación empleando un brazo robótico industrial. -Estrategia de control bi-frecuencia basada en el filtrado de la referencia (Capítulo 5): generalización de la estrategia de control basada en filtrado de la referencia desde un punto de vista multi-frecuencial, con un filtro de Kalman multi-frecuencia y un Kalman-smoother dual basado en la relación existente entre las frecuencias del sensor y del controlador, reduciendo así el coste computacional del algoritmo y, al mismo tiempo, dando solución al problema de la latencia del sensor. La validación se realiza utilizando un barzo robot industria asi[CA] La present tesis proposa solucions per a dos problemes característics dels sistemes robòtics el els que el bucle de control es tanca únicament utilitzant sensors de visió artificial: 1) la latència del sensor; 2) l'obtenció de trajectòries factibles tant per al robot com per les mesures en la imatge. Tots els mètodes proposats en aquest treball son analitzats, validats e implementats utilitzant un braç robot industrial de 6 graus de llibertat i/o un robot amb rodes. Atenent al problema de la latència del sensor, esta tesis proposa l'ús de retenidors bi-freqüència d'ordre alt a dins del llaços de control de robots. Al respecte, les principals contribucions son: - Retenidors bi-freqüència d'ordre alt basats en funcions primitives a dintre dels llaços de control de robots (Capítol 3): anàlisis del comportament del sistema amb i sense l'ús d'aquesta tècnica de control no convencional. A més a més, com a conseqüència de l'ús dels retenidors, obtenció i validació de controladors multi-freqüència, concretament de PIDs bi-freqüència. - Retenidors bi-freqüència asíncrons d'ordre alt basats en funcions primitives amb compensació de retards (Capítol 3): generalització dels retenidors bi-freqüència asíncrons d'ordre alt inclouen una component de compensació del retràs en la senyal d'entrada al retenidor, millorant així les estimacions inter-mostreig calculades per el retenidor. Es proporciona un anàlisis de les propietats dels retenidors amb compensació del retràs, comparant-les amb les obtingudes per el seus predecessors sense la compensació, així com la seua implementació i validació en un braç robot industrial de 6 graus de llibertat. - Retenidors multi-freqüència no-lineals d'ordre alt (Capítol 4): generalització del concepte de retenidor bi-freqüència d'ordre alt amb models d'estimació no lineals, incloent informació tant de la planta a controlar, com del controlador(s) i sensor(s) utilitzat(s), obtenint-la a partir de tècniques d'aprenentatge. Així doncs, per obtindre el retenidor no lineal, es descriu una metodologia independent de la ferramenta d'aprenentatge utilitzada, però validada amb l'ús de rets neuronals artificials. Finalment es realitza un anàlisis de les propietats d'aquestos nous retenidors, comparant-los amb els seus predecessors basats amb funcions primitives, així com la seua implementació i validació amb un braç robot de 6 graus de llibertat i amb un robot mòbil de rodes. Per el que respecta al problema de generació de trajectòries factibles per al robot i per la mesura en la imatge, aquesta tesis proposa la nova estratègia de control basada amb el filtrat de la referència i la seua generalització des de el punt de vista multi-freqüència. - Estratègia de control basada amb el filtrat de la referència (Capítol 5): una nova estratègia de control es proposada per ampliar significativament l'espai de solucions dels sistemes robòtics realimentats amb sensors de visió artificial. La principal idea es la d'utilitzar les trajectòries optimes obtingudes per una trajectòria predita per un filtre de Kalman seguit d'un suavitzat basat en l'algoritme Rauch-Tung-Striebel (RTS) com noves referències per a un control donat. En aquest treball es proporciona tant la descripció del algoritme així com la seua implementació i validació utilitzant un braç robòtic industrial de 6 graus de llibertat. - Estratègia de control bi-freqüència basada en el filtrat (Capítol 5): generalització de l'estratègia de control basada am filtrat de la referència des de un punt de vista multi freqüència, amb un filtre de Kalman multi freqüència i un Kalman-Smoother dual basat amb la relació existent entre les freqüències del sensor i del controlador, reduint així el cost computacional de l'algoritme i, al mateix temps, donant solució al problema de la latència del sensor. L'algoritme d'implementació d'aquesta tècnica, així com la seua validaciSolanes Galbis, JE. (2015). MULTI-RATE VISUAL FEEDBACK ROBOT CONTROL [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/57951TESI

Intelligent and High-Performance Behavior Design of Autonomous Systems via Learning, Optimization and Control

Nowadays, great societal demands have rapidly boosted the development of autonomous systems that densely interact with humans in many application domains, from manufacturing to transportation and from workplaces to daily lives. The shift from isolated working environments to human-dominated space requires autonomous systems to be empowered to handle not only environmental uncertainties such as external vibrations but also interaction uncertainties arising from human behavior which is in nature probabilistic, causal but not strictly rational, internally hierarchical and socially compliant.This dissertation is concerned with the design of intelligent and high-performance behavior of such autonomous systems, leveraging the strength from control, optimization, learning, and cognitive science. The work consists of two parts. In Part I, the problem of high-level hybrid human-machine behavior design is addressed. The goal is to achieve safe, efficient and human-like interaction with people. A framework based on the theory of mind, utility theories and imitation learning is proposed to efficiently represent and learn the complicated behavior of humans. Built upon that, machine behaviors at three different levels - the perceptual level, the reasoning level, and the action level - are designed via imitation learning, optimization, and online adaptation, allowing the system to interpret, reason and behave as human, particularly when a variety of uncertainties exist. Applications to autonomous driving are considered throughout Part I. Part II is concerned with the design of high-performance low-level individual machine behavior in the presence of model uncertainties and external disturbances. Advanced control laws based on adaptation, iterative learning and the internal structures of uncertainties/disturbances are developed to assure that the high-level interactive behaviors can be reliably executed. Applications on robot manipulators and high-precision motion systems are discussed in this part

Real-time motion planning and decision-making for a group of differential drive robots under connectivity constraints using robust MPC and mixed-integer programming

This work is concerned with the problem of planning trajectories and assigning tasks for a Multi-Agent System (MAS) comprised of differential drive robots. We propose a multirate hierarchical control structure that employs a planner based on robust Model Predictive Control (MPC) with mixed-integer programming (MIP) encoding. The planner computes trajectories and assigns tasks for each element of the group in real-time, while also guaranteeing the communication network of the MAS to be robustly connected at all times. Additionally, we provide a data-based methodology to estimate the disturbances sets required by the robust MPC formulation. The results are demonstrated with experiments in two obstacle-filled scenariosComment: Submitted to Advanced Robotics special issue on Online Motion Planning and Model Predictive Contro

Multi-sensor multi-rate fusion estimation for networked systems: Advances and perspectives

National Natural Science Foundation of China under Grants 62103095, 61873058, 61873148 and 61933007; AHPU Youth Top-notch Talent Support Program of China under Grant 2018BJRC009; Natural Science Foundation of Anhui Province of China under Grant 2108085MA07; Royal Society of the UK; Alexander von Humboldt Foundation of Germany

WayFAST: Navigation with Predictive Traversability in the Field

We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonomously generate traversable paths in outdoor unstructured environments. Our key inspiration is that traction can be estimated for rolling robots using kinodynamic models. Using traction estimates provided by an online receding horizon estimator, we are able to train a traversability prediction neural network in a self-supervised manner, without requiring heuristics utilized by previous methods. We demonstrate the effectiveness of WayFAST through extensive field testing in varying environments, ranging from sandy dry beaches to forest canopies and snow covered grass fields. Our results clearly demonstrate that WayFAST can learn to avoid geometric obstacles as well as untraversable terrain, such as snow, which would be difficult to avoid with sensors that provide only geometric data, such as LiDAR. Furthermore, we show that our training pipeline based on online traction estimates is more data-efficient than other heuristic-based methods.Comment: Project website with code and videos: https://mateusgasparino.com/wayfast-traversability-navigation/ Published in the IEEE Robotics and Automation Letters (RA-L, 2022) Accepted for presentation in the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022

Asynchronous sensor fusion of GPS, IMU and CAN-based odometry for heavy-duty vehicles

In heavy-duty vehicles, multiple signals are available to estimate the vehicle's kinematics, such as Inertial Measurement Unit (IMU), Global Positioning System (GPS) and linear and angular speed readings from wheel tachometers on the internal Controller Area Network (CAN). These signals have different noise variance, bandwidth and sampling rate (being the latter, possibly, irregular). In this paper we present a non-linear sensor fusion algorithm allowing asynchronous sampling and non-causal smoothing. It is applied to achieve accuracy improvements when incorporating odometry measurements from CAN bus to standard GPS+IMU kinematic estimation, as well as the robustness against missing data. Our results show that this asynchronous multi-sensor (GPS+IMU+CAN-based odometry) fusion is advantageous in low-speed manoeuvres, improving accuracy and robustness to missing data, thanks to non-causal filtering. The proposed algorithm is based on Extended Kalman Filter and Smoother, with exponential discretization of continuous-time stochastic differential equations, in order to process measurements at arbitrary time instants; it can provide data to subsequent processing steps at arbitrary time instants, not necessarily coincident with the original measurement ones. Given the extra information available in the smoothing case, its estimation performance is less sensitive to the noise-variance parameter setting, compared to causal filtering. Working Matlab code is provided at the end of this work

On the Enhancement of the Localization of Autonomous Mobile Platforms

The focus of many industrial and research entities on achieving full robotic autonomy increased in the past few years. In order to achieve full robotic autonomy, a fundamental problem is the localization, which is the ability of a mobile platform to determine its position and orientation in the environment. In this thesis, several problems related to the localization of autonomous platforms are addressed, namely, visual odometry accuracy and robustness; uncertainty estimation in odometries; and accurate multi-sensor fusion-based localization. Beside localization, the control of mobile manipulators is also tackled in this thesis. First, a generic image processing pipeline is proposed which, when integrated with a feature-based Visual Odometry (VO), can enhance robustness, accuracy and reduce the accumulation of errors (drift) in the pose estimation. Afterwards, since odometries (e.g. wheel odometry, LiDAR odometry, or VO) suffer from drift errors due to integration, and because such errors need to be quantified in order to achieve accurate localization through multi-sensor fusion schemes (e.g. extended or unscented kalman filters). A covariance estimation algorithm is proposed, which estimates the uncertainty of odometry measurements using another sensor which does not rely on integration. Furthermore, optimization-based multi-sensor fusion techniques are known to achieve better localization results compared to filtering techniques, but with higher computational cost. Consequently, an efficient and generic multi-sensor fusion scheme, based on Moving Horizon Estimation (MHE), is developed. The proposed multi-sensor fusion scheme: is capable of operating with any number of sensors; and considers different sensors measurements rates, missing measurements, and outliers. Moreover, the proposed multi-sensor scheme is based on a multi-threading architecture, in order to reduce its computational cost, making it more feasible for practical applications. Finally, the main purpose of achieving accurate localization is navigation. Hence, the last part of this thesis focuses on developing a stabilization controller of a 10-DOF mobile manipulator based on Model Predictive Control (MPC). All of the aforementioned works are validated using numerical simulations; real data from: EU Long-term Dataset, KITTI Dataset, TUM Dataset; and/or experimental sequences using an omni-directional mobile robot. The results show the efficacy and importance of each part of the proposed work

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance