Indoor Localization of Mobile Robots with Wireless Sensor Network Based on Ultra Wideband using Experimental Measurements of Time Difference of Arrival

This paper presents investigations into wireless localization techniques for mobile robots operating in indoor environments. Localization systems can guide robots to perform different tasks such as monitoring children or elderly people, aid mobility of the visually impaired and localize mobile objects or packages in warehouses. They are essential for localization of robots operating in re-mote places that are inaccessible or hazardous to humans. Currently, ultra wide band (UWB) in indoor environments provides an accuracy of 24 mm under line of sight (LOS) or non-line of sight (NLOS) conditions in a working range of 160 m indoors. The work presented in this paper carries out experimental validation of localization algorithms using mobile robots and UWB signals. These are measured in LOS and NLOS environments. The measurements are performed with the UWB radio PulsON 410 (P410) and mobile robots (AmigoBot) with maximum travel-ling speed of 1 m/s and equipped with an on-board computer, sonar, odometer, camera and inertial navigation system. Experimental results obtained for the system show positioning errors of less than 55 mm

Short-Range Super-Resolution Feature Extraction of Complex Edged Contours for Object Recognition by Ultra-Wideband Radar

This thesis contributes to the field of short-range ultra-wideband (UWB) Radar. In particular, an object recognition approach performed by a bi-static UWB Radar has been investigated in this thesis. The investigated objects consist of simple canonical and some polygonal complex objects which are scanned on a circular track at about 1 m distance. Geometrical features, texture features and moment based features are extracted from the Radar data to carry out the recognition. Yet, the precise temporal evolution is subject to massive distortions, mainly caused by severe interference conditions and transient effects of the hardware. Thus, super-resolution algorithms have been developed which go far beyond the classical bandwidth given resolution and asked for research on various fields: (i) An innovative wavefront extraction algorithm with polarimetric diversity exploitation has been developed to separate pulses which overlap almost the whole pulse duration; (ii) a highly precise feature extraction algorithm has been developed which localises significant scattering centres by processing the previously extracted wavefronts; (iii) a novel UWB object recognition algorithm has been developed to classify and discriminate the resulting microwave images. When scanning objects from all sides, exceptional recognition of objects was achieved by a minimum mean squared error classifier. Further improvement in recognition was obtained, especially at severly restricted tracks, by the application of Bayes theory which constitutes a superior classifier to the above. In addition to the main field of research, a novel stereoscopic 3D UWB imaging algorithm, based on a spatially spanned synthetic aperture in conjunction with ellipsoidal shaped wavefronts, has been developed. The ultimate test of any model and system is an experimental validation. Consequently in this thesis, all developed algorithms and the object recognition as a whole system are experimentally validated within an elaborate measurement campaign

Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

Nowadays, the availability of the location information becomes a key factor in today’s communications systems for allowing location based services. In outdoor scenarios, the Mobile Terminal (MT) position is obtained with high accuracy thanks to the Global Positioning System (GPS) or to the standalone cellular systems. However, the main problem of GPS or cellular systems resides in the indoor environment and in scenarios with deep shadowing effect where the satellite or cellular signals are broken. In this paper, we will present a review over different technologies and concepts used to improve indoor localization. Additionally, we will discuss different applications based on different localization approaches. Finally, comprehensive challenges in terms of accuracy, cost, complexity, security, scalability, etc. are presente

Indoor Localisation of Scooters from Ubiquitous Cost-Effective Sensors: Combining Wi-Fi, Smartphone and Wheel Encoders

Indoor localisation of people and objects has been a focus of research studies for several decades because of its great advantage to several applications. Accuracy has always been a challenge because of the uncertainty of the employed sensors. Several technologies have been proposed and researched, however, accuracy still represents an issue. Today, several sensor technologies can be found in indoor environments, some of which are economical and powerful, such as Wi-Fi. Meanwhile, Smartphones are typically present indoors because of the people that carry them along, while moving about within rooms and buildings. Furthermore, vehicles such as mobility scooters can also be present indoor to support people with mobility impairments, which may be equipped with low-cost sensors, such as wheel encoders. This thesis investigates the localisation of mobility scooters operating indoor. This represents a specific topic as most of today's indoor localisation systems are for pedestrians. Furthermore, accurate indoor localisation of those scooters is challenging because of the type of motion and specific behaviour. The thesis focuses on improving localisation accuracy for mobility scooters and on the use of already available indoor sensors. It proposes a combined use of Wi-Fi, Smartphone IMU and wheel encoders, which represents a cost-effective energy-efficient solution. A method has been devised and a system has been developed, which has been experimented on different environment settings. The outcome of the experiments are presented and carefully analysed in the thesis. The outcome of several trials demonstrates the potential of the proposed solutions in reducing positional errors significantly when compared to the state-of-the-art in the same area. The proposed combination demonstrated an error range of 0.35m - 1.35m, which can be acceptable in several applications, such as some related to assisted living. 3 As the proposed system capitalizes on the use of ubiquitous technologies, it opens up to a potential quick take up from the market, therefore being of great benefit for the target audience

Wireless indoor localisation within the 5G internet of radio light

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonNumerous applications can be enhanced by accurate and efficient indoor localisation using wireless sensor networks, however trade-offs often exist between these two parameters. In this thesis, realworld and simulation data is used to examine the hybrid millimeter wave and Visible Light Communications (VLC) architecture of the 5G Internet of Radio Light (IoRL) Horizon 2020 project. Consequently, relevant localisation challenges within Visible Light Positioning (VLP) and asynchronous sampling networks are identified, and more accurate and efficient solutions are developed. Currently, VLP relies strongly on the assumed Lambertian properties of light sources. However, in practice, not all lights are Lambertian. To support the widespread deployment of VLC technology in numerous environments, measurements from non-Lambertian sources are analysed to provide new insights into the limitations of existing VLP techniques. Subsequently, a novel VLP calibration technique is proposed, and results indicate a 59% accuracy improvement against existing methods. This solution enables high accuracy centimetre level VLP to be achieved with non- Lambertian sources. Asynchronous sampling of range-based measurements is known to impact localisation performance negatively. Various Asynchronous Sampling Localisation Techniques (ASLT) exist to mitigate these effects. While effective at improving positioning performance, the exact suitability of such solutions is not evident due to their additional processes, subsequent complexity, and increased costs. As such, extensive simulations are conducted to study the effectiveness of ASLT under variable sampling latencies, sensor measurement noise, and target trajectories. Findings highlight the computational demand of existing ASLT and motivate the development of a novel solution. The proposed Kalman Extrapolated Least Squares (KELS) method achieves optimal localisation performance with a significant energy reduction of over 50% when compared to current leading ASLT. The work in this thesis demonstrates both the capability for high performance VLP from non- Lambertian sources as well as the potential for energy efficient localisation for sequentially sampled range measurements.Horizon 202

Collaborative autonomy in heterogeneous multi-robot systems

As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition. This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems. Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Indoor Localization Using Channel State Information with Regression Artificial Neural Network

RÉSUMÉ Dans cette recherche, les informations sur l'état du canal (CSI) sont utilisées pour localiser les stations mobiles dans un environnement intérieur. À cette fin, deux ordinateurs portables équipés de la carte Intel Wireless Wi-Fi Wireless Link 5300 disponible dans le commerce sont utilisés. Les informations CSI sont collectées en établissant une connexion sans fil entre deux machines de plus de 200, 70 et 52 points de référence (RP) aux sixième, cinquième et troisième étages respectivement, dans l’immeuble Lassonde de Polytechnique Montréal servant de banc d’essai expérimental. Différentes approches de localisation sont étudiées et comparées les unes aux autres en termes de précision de localisation. Dans la première approche, les CSI collectés alimentent directement le réseau de neurones artificiels (RNA) en tant que caractéristiques d’entrée et le RNA appris est utilisé en tant qu’algorithme de correspondance du modèle afin de prédire la position de l’utilisateur. La deuxième approche consiste à appliquer à l’entrée de RNA les paramètres pertinents du canal extrait représentant le nombre réduit d’entités à l’entrée de RNA. Enfin, une exploration est effectuée pour trouver la meilleure configuration de couches cachées et de facteurs d'étalement pour les réseaux Perceptron multicouche (MLP) et Réseaux de neurones à régression générale (GRNN), respectivement.----------ABSTRACT In this research, the Channel State Information (CSI) is leveraged to locate mobile stations in an indoor environment. For this purpose, two laptops equipped with the off-the-shelf Intel Wi-Fi Wireless Link 5300 (NIC card) are used. CSI information is collected by establishing a wireless connection between two machines over 200, 70 and 52 reference points (RP) on sixth, fifth, and third floors respectively, in Lassonde building of Polytechnique Montreal as the experimental testbed. Different geolocation approaches are investigated and compared with each other in terms of location accuracy and precision. In the first approach, the collected CSIs are directly fed to the artificial neural network (ANN) as input features and the learned ANN is used as the patternmatching algorithm in order to predict the user’s location. The second approach consists in applying at the input of the ANN the extracted channel relevant parameters representing the reduced number of features at the input of ANN. Finally, exploration is performed to find the best configuration of hidden layers and spread factors for Multilayer Perceptron (MLPs) and General Regression Neural Networks (GRNNs), respectively