Una metodología de posicionamiento cooperativo diferencial para el posicionamiento de dispositivos múltiples

Introduction: This publication is the product of research developed within the research lines of the Advanced and Large-scale Computing (Cage) research group throughout 2018, which supports the work of a master’s degree in Systems Engineering at the Industrial University of Santander. Objetive: An approach to a cooperative positioning algorithm is described in this paper, where a set of devices exchange GPS satellite observables and distance estimations with nearby devices in order to increase their positioning accuracy. Methodology: Different scenarios are established where GPS receivers exchange satellite information, using different ionospheric correction models, with the purpose of evaluating which conditions potentially improve the position accuracy. Conclusions: The results show our approach yields increased accuracy when all receivers use the same ionospheric correction model. Moreover, it was observed that the noise levels and uncertainty usually due to factors related to distance from remote devices to the main receiver did not influence positioning improvement when the separation between receiver pairs was large. Originality: The proposed algorithm allows for exploitation of the nature of the problem without increasing complexity at the hardware and software level, and to offer a low-cost cooperative positioning solution alternative. Restrictions: The results presented in the document are based on the execution of the cooperative algorithm using Rinex files of gnss reference stations. So, for scenarios in which the separation distances between reference stations are very high, the error levels in cooperative positioning can be very large.Introducción: esta publicación es el producto de una investigación del grupo de investigación de computación avanzada y en gran escala (Cage) de la Universidad Industrial de Santander, a lo largo de 2018. Objetivo: Se propone un algoritmo de posicionamiento cooperativo en el que un conjunto de dispositivos intercambia observables satelitales, y estimaciones de distancia entre dispositivos GPS cercanos, con el objetivo de aumentar su precisión de posicionamiento. Metodología: se establecen escenarios donde los receptores de GPS intercambian información satelital, y utilizan diferentes modelos de corrección ionosférica con el fin de evaluar las condiciones en que es posible mejorar la precisión en posicionamiento. Conclusiones: El algoritmo propuesto produce una mayor precisión cuando todos los receptores emplean el mismo modelo de corrección ionosférica. Además, el nivel de incertidumbre en la medida de distancia entre dispositivos no presenta mayor influencia sobre la mejora de la precisión, cuando la separación entre receptores es muy grande. Originalidad: el algoritmo propuesto permite explotar la naturaleza del problema sin aumentar la complejidad a nivel de hardware y software, y se ofrece como una alternativa de solución de posicionamiento cooperativo de bajo costo. Limitación: Los resultados exponen la ejecución del algoritmo cooperativo utilizando archivos Rinex de estaciones de referencia gnss. Por lo tanto, para los escenarios en que la distancia de separación entre estaciones es muy alta, los niveles de error en posicionamiento pueden ser elevados

GUARDIANS final report

Emergencies in industrial warehouses are a major concern for firefghters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist fire fighters in searching a large warehouse. In this report we discuss the technology developed for a swarm of robots searching and assisting fire fighters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also one of the means to locate the robots and humans. Thus the robot swarm is able to locate itself and provide guidance information to the humans. Together with the re ghters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings

Hybrid inertial-manipulator based position tracking system for ultrasound imaging application

In medical field, ultrasound imaging is one of the imaging modalities that needs position tracking system (PTS) in enlarging field of view (FoV) of an image. The enlarged FoV will result easier scanning procedure, and produce more accurate and comprehensive results. To overcome the weakness of commercially available PTSs which suffer from interference and occlusion, many researchers proposed improved PTSs. However, the improved PTSs focused on the portability and compact design, neglecting the vertical scanning aspect which is also important in ultrasound imaging. Hence, this research presents the development of hybrid inertial-manipulator based PTS for 3-dimensional (3D) ultrasound imaging system which capable of measuring the horizontal and vertical scanning movements. The proposed PTS uses the combination of inertial measurement unit sensor and manipulator. The research involves design and evaluation processes for the PTS. Once the design process of the PTS is completed, forward kinematics is calculated using Denavit-Hartenberg conversion. The next step is to evaluate the accuracy and repeatability of the output of the designed PTS by comparing with five sets of reference trajectory of ABB robot. A comparison of the accuracy for the proposed PTS with three other available PTSs is done using the horizontal movement’s error. The experimental results showed high repeatability of position output reading of the designed PTS with standard deviation of 0.27 mm in all different movements and speeds. The proposed PTS is suitable to be used in ultrasound imaging as the error is less than 1.45 mm. Furthermore, the proposed PTS can measure the vertical scanning movement which is neglected in all the previous works, thus fulfilling the main objective of the research

Optimization based solutions for control and state estimation in non-holonomic mobile robots: stability, distributed control, and relative localization

Interest in designing, manufacturing, and using autonomous robots has been rapidly growing during the most recent decade. The main motivation for this interest is the wide range of potential applications these autonomous systems can serve in. The applications include, but are not limited to, area coverage, patrolling missions, perimeter surveillance, search and rescue missions, and situational awareness. In this thesis, the area of control and state estimation in non-holonomic mobile robots is tackled. Herein, optimization based solutions for control and state estimation are designed, analyzed, and implemented to such systems. One of the main motivations for considering such solutions is their ability of handling constrained and nonlinear systems such as non-holonomic mobile robots. Moreover, the recent developments in dynamic optimization algorithms as well as in computer processing facilitated the real-time implementation of such optimization based methods in embedded computer systems. Two control problems of a single non-holonomic mobile robot are considered first; these control problems are point stabilization (regulation) and path-following. Here, a model predictive control (MPC) scheme is used to fulfill these control tasks. More precisely, a special class of MPC is considered in which terminal constraints and costs are avoided. Such constraints and costs are traditionally used in the literature to guarantee the asymptotic stability of the closed loop system. In contrast, we use a recently developed stability criterion in which the closed loop asymptotic stability can be guaranteed by appropriately choosing the prediction horizon length of the MPC controller. This method is based on finite time controllability as well as bounds on the MPC value function. Afterwards, a regulation control of a multi-robot system (MRS) is considered. In this control problem, the objective is to stabilize a group of mobile robots to form a pattern. We achieve this task using a distributed model predictive control (DMPC) scheme based on a novel communication approach between the subsystems. This newly introduced method is based on the quantization of the robots’ operating region. Therefore, the proposed communication technique allows for exchanging data in the form of integers instead of floating-point numbers. Additionally, we introduce a differential communication scheme to achieve a further reduction in the communication load. Finally, a moving horizon estimation (MHE) design for the relative state estimation (relative localization) in an MRS is developed in this thesis. In this framework, robots with less payload/computational capacity, in a given MRS, are localized and tracked using robots fitted with high-accuracy sensory/computational means. More precisely, relative measurements between these two classes of robots are used to localize the less (computationally) powerful robotic members. As a complementary part of this study, the MHE localization scheme is combined with a centralized MPC controller to provide an algorithm capable of localizing and controlling an MRS based only on relative sensory measurements. The validity and the practicality of this algorithm are assessed by realtime laboratory experiments. The conducted study fills important gaps in the application area of autonomous navigation especially those associated with optimization based solutions. Both theoretical as well as practical contributions have been introduced in this research work. Moreover, this thesis constructs a foundation for using MPC without stabilizing constraints or costs in the area of non-holonomic mobile robots

Leader-assisted localization approach for a heterogeneous multi-robot system

This thesis presents the design, implementation, and validation of a novel leader assisted localization framework for a heterogeneous multi-robot system (MRS) with sensing and communication range constraints. It is assumed that the given heterogeneous MRS has a more powerful robot (or group of robots) with accurate self localization capabilities (leader robots) while the rest of the team (child robots), i.e. less powerful robots, is localized with the assistance of leader robots and inter-robot observation between teammates. This will eventually pose a condition that the child robots should be operated within the sensing and communication range of leader robots. The bounded navigation space therefore may require added algorithms to avoid inter-robot collisions and limit robots’ maneuverability. To address this limitation, first, the thesis introduces a novel distributed graph search and global pose composition algorithm to virtually enhance the leader robots’ sensing and communication range while avoiding possible double counting of common information. This allows child robots to navigate beyond the sensing and communication range of the leader robot, yet receive localization services from the leader robots. A time-delayed measurement update algorithm and a memory optimization approach are then integrated into the proposed localization framework. This eventually improves the robustness of the algorithm against the unknown processing and communication time-delays associated with the inter-robot data exchange network. Finally, a novel hierarchical sensor fusion architecture is introduced so that the proposed localization scheme can be implemented using inter-robot relative range and bearing measurements. The performance of the proposed localization framework is evaluated through a series of indoor experiments, a publicly available multi-robot localization and mapping data-set and a set of numerical simulations. The results illustrate that the proposed leader-assisted localization framework is capable of establishing accurate and nonoverconfident localization for the child robots even when the child robots operate beyond the sensing and communication boundaries of the leader robots

Decentralized collaborative localization in urban environments with inter-agent ranging and 3D-mapping-aided (3DMA) GNSS

In recent times, there has been an increasing number of applications of autonomous systems such as unmanned aerial vehicles (UAVs) and self-driving cars in urban environments for tasks such as transport, delivery, photography, surveillance and search and rescue. Global Navigation Satellite System (GNSS) navigation in urban environments is prone to error sources such as multipath and signal blockage due to buildings in the environment. However, if we consider several agents, then the localization capabilities may differ among the agents due to reasons such as heterogeneity of sensors, different view of the sky, the structure of buildings around agents etc. This variety can be leveraged to localize all the agents better if they cooperate with each other. Collaborative localization (CL) is a way to aid navigation in a multi-agent system. However, CL algorithms face challenges such as scalability, robustness to noisy sensor data and single points of failure, and operability despite limited inter-agent communication. Additionally, a 3D Map of the environment can be used to predict and account for the effects of multipath and signal-blockage. Shadow Matching has shown high potential in 3D-mapping-aided (3DMA) GNSS navigation for a single agent in urban environments, but it suffers from errors such as ambiguity which cannot be reliably mitigated without external sensing and integrated systems. Shadow Matching naturally compliments range-based GNSS algorithms and is ideally used alongside it. This integration, however, is not guaranteed to reduce ambiguity and may in fact increase it. In this thesis, we present three novel decentralized collaborative localization methods for navigation of multiple agents in an urban environment. The proposed frameworks are applicable to sparsely connected networks and information exchange is limited to only those agents which obtain relative inter-agent measurements. Furthermore, we allow the agents to carry out the updates asynchronously. First, we present an algorithm which allows for deep coupling of agents' GPS measurements with inter-agent ranging measurements. We build this work upon a decentralized extended Kalman filter based collaborative localization framework and take advantage of the variable visibility of the sky for different agents. We propose a methodology for relaying satellite information between agents to augment the set of visible satellites on each agent with virtual satellites, thereby providing more constraint equations to each agent. The proposed method is validated on real world dataset involving an aerial vehicle, ground agents, and several range-only sensors. Next, we incorporate the 3D city map and present a novel snapshot algorithm which couples 3DMA GNSS measurements with inter-agent ranging modality. We build this upon the Intelligent Urban Positioning (IUP) 3DMA algorithm which uses Shadow Matching (SM) and Likelihood-based 3DMA Ranging (LB-3DMAr) algorithms. The introduction of multiple agents equipped with ranging sensors in the framework enables ambiguity error mitigation (which is a source of error in both SM and LB-3DMAr) and further improves accuracy (by introducing an additional sensor measurement). This method works by constraining an agent's probability distribution using its neighbors in a discretized grid of position hypothesis. Finally, we extend the above method to a multi-epoch variant. Unlike the snapshot algorithm, this allows for a mechanism to account for temporal correlations between poses for each agent. This enables the proposed methodology to further improve ambiguity error mitigation and localization accuracy by preventing jump discontinuities at subsequent time steps and introducing an additional sensor measurement (in the form of pseudorange rate measurements). The method adapts the discretized Bayesian filter to associate temporal correlations between agent states. These snapshot and multi-epoch methods are validated on simulated datasets in an urban area of Champaign, Illinois, with multiple agents in a variety of scenarios. We demonstrate the improved performance in terms of positioning accuracy and ambiguity mitigation. We also analyze the impact of network connectivity, and size of network on positioning accuracy

Task Allocation and Collaborative Localisation in Multi-Robot Systems

To utilise multiple robots, it is fundamental to know what they should do, called task allocation, and to know where the robots are, called localisation. The order that tasks are completed in is often important, and makes task allocation difficult to solve (40 tasks have 1047 different ways of completing them). Algorithms in literature range from fast methods that provide reasonable allocations, to slower methods that can provide optimal allocations. These algorithms work well for systems with identical robots, but do not utilise robot differences for superior allocations when robots are non-identical. They also can not be applied to robots that can use different tools, where they must consider which tools to use for each task. Robot localisation is performed using sensors which are often assumed to always be available. This is not the case in GPS-denied environments such as tunnels, or on long-range missions where replacement sensors are not readily available. A promising method to overcome this is collaborative localisation, where robots observe one another to improve their location estimates. There has been little research on what robot properties make collaborative localisation most effective, or how to tune systems to make it as accurate as possible. Most task allocation algorithms do not consider localisation as part of the allocation process. If task allocation algorithms limited inter-robot distance, collaborative localisation can be performed during task completion. Such an algorithm could equally be used to ensure robots are within communication distance, and to quickly detect when a robot fails. While some algorithms for this exist in literature, they provide a weak guarantee of inter-robot distance, which is undesirable when applied to real robots. The aim of this thesis is to improve upon task allocation algorithms by increasing task allocation speed and efficiency, and supporting robot tool changes. Collaborative localisation parameters are analysed, and a task allocation algorithm that enables collaborative localisation on real robots is developed. This thesis includes a compendium of journal articles written by the author. The four articles forming the main body of the thesis discuss the multi-robot task allocation and localisation research during the author’s candidature. Two appendices are included, representing conference articles written by the author that directly relate to the thesis.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations