Autonomous Swarm Navigation

Robotic swarm systems attract increasing attention in a wide variety of applications, where a multitude of self-organized robotic entities collectively accomplish sensing or exploration tasks. Compared to a single robot, a swarm system offers advantages in terms of exploration speed, robustness against single point of failures, and collective observations of spatio-temporal processes. Autonomous swarm navigation, including swarm self-localization, the localization of external sources, and swarm control, is essential for the success of an autonomous swarm application. However, as a newly emerging technology, a thorough study of autonomous swarm navigation is still missing. In this thesis, we systematically study swarm navigation systems, particularly emphasizing on their collective performance. The general theory of swarm navigation as well as an in-depth study on a specific swarm navigation system proposed for future Mars exploration missions are covered. Concerning swarm localization, a decentralized algorithm is proposed, which achieves a near-optimal performance with low complexity for a dense swarm network. Regarding swarm control, a position-aware swarm control concept is proposed. The swarm is aware of not only the position estimates and the estimation uncertainties of itself and the sources, but also the potential motions to enrich position information. As a result, the swarm actively adapts its formation to improve localization performance, without losing track of other objectives, such as goal approaching and collision avoidance. The autonomous swarm navigation concept described in this thesis is verified for a specific Mars swarm exploration system. More importantly, this concept is generally adaptable to an extensive range of swarm applications

Active Information Acquisition With Mobile Robots

The recent proliferation of sensors and robots has potential to transform fields as diverse as environmental monitoring, security and surveillance, localization and mapping, and structure inspection. One of the great technical challenges in these scenarios is to control the sensors and robots in order to extract accurate information about various physical phenomena autonomously. The goal of this dissertation is to provide a unified approach for active information acquisition with a team of sensing robots. We formulate a decision problem for maximizing relevant information measures, constrained by the motion capabilities and sensing modalities of the robots, and focus on the design of a scalable control strategy for the robot team. The first part of the dissertation studies the active information acquisition problem in the special case of linear Gaussian sensing and mobility models. We show that the classical principle of separation between estimation and control holds in this case. It enables us to reduce the original stochastic optimal control problem to a deterministic version and to provide an optimal centralized solution. Unfortunately, the complexity of obtaining the optimal solution scales exponentially with the length of the planning horizon and the number of robots. We develop approximation algorithms to manage the complexity in both of these factors and provide theoretical performance guarantees. Applications in gas concentration mapping, joint localization and vehicle tracking in sensor networks, and active multi-robot localization and mapping are presented. Coupled with linearization and model predictive control, our algorithms can even generate adaptive control policies for nonlinear sensing and mobility models. Linear Gaussian information seeking, however, cannot be applied directly in the presence of sensing nuisances such as missed detections, false alarms, and ambiguous data association or when some sensor observations are discrete (e.g., object classes, medical alarms) or, even worse, when the sensing and target models are entirely unknown. The second part of the dissertation considers these complications in the context of two applications: active localization from semantic observations (e.g, recognized objects) and radio signal source seeking. The complexity of the target inference problem forces us to resort to greedy planning of the sensor trajectories. Non-greedy closed-loop information acquisition with general discrete models is achieved in the final part of the dissertation via dynamic programming and Monte Carlo tree search algorithms. Applications in active object recognition and pose estimation are presented. The techniques developed in this thesis offer an effective and scalable approach for controlled information acquisition with multiple sensing robots and have broad applications to environmental monitoring, search and rescue, security and surveillance, localization and mapping, precision agriculture, and structure inspection

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

Opportunistic Angle of Arrival Estimation in Impaired Scenarios

This work if focused on the analysis and the development of Angle of Arrival (AoA) radio localization methods. The radio positioning system considered is constituted by a radio source and by a receiving array of antennas. The positioning algorithms treated in this work are designed to have a passive and opportunistic approach. The opportunistic attribute implies that the radio localization algorithms are designed to provide the AoA estimation with nearly-zero information on the transmitted signals. No training sequences or waveforms custom designed for localization are taken into account. The localization is termed passive since there is no collaboration between the transmitter and the receiver during the localization process. Then, the algorithms treated in this work are designed to eavesdrop already existing communication signals and to locate their radio source with nearly-zero knowledge of the signal and without the collaboration of the transmitting node. First of all, AoA radio localization algorithms can be classified in terms of involved signals (narrowband or broadband), antenna array pattern (L-shaped, circular, etc.), signal structure (sinusoidal, training sequences, etc.), Differential Time of Arrival (D-ToA) / Differential Phase of Arrival (D-PoA) and collaborative/non collaborative. Than, the most detrimental effects for radio communications are treated: the multipath (MP) channels and the impaired hardware. A geometric model for the MP is analysed and implemented to test the robustness of the proposed methods. The effects of MP on the received signals statistics from the AoA estimation point-of-view are discussed. The hardware impairments for the most common components are introduced and their effects in the AoA estimation process are analysed. Two novel algorithms that exploits the AoA from signal snapshots acquired sequentially with a time division approach are presented. The acquired signals are QAM waveforms eavesdropped from a pre-existing communication. The proposed methods, namely Constellation Statistical Pattern IDentification and Overlap (CSP-IDO) and Bidimensional CSP-IDO (BCID), exploit the probability density function (pdf) of the received signals to obtain the D-PoA. Both CSP-IDO and BCID use the statistical pattern of received signals exploiting the transmitter statistical signature. Since the presence of hardware impairments modify the statistical pattern of the received signals, CSP-IDO and BCID are able to exploit it to improve the performance with respect to (w.r.t.) the ideal case. Since the proposed methods can be used with a switched antenna architecture they are implementable with a reduced hardware contrariwise to synchronous methods like MUltiple SIgnal Classification (MUSIC) that are not applicable. Then, two iterative AoA estimation algorithms for the dynamic tracking of moving radio sources are implemented. Statistical methods, namely PF, are used to implement the iterative tracking of the AoA from D-PoA measures in two different scenarios: automotive and Unmanned Aerial Vehicle (UAV). The AoA tracking of an electric car signalling with a IEEE 802.11p-like standard is implemented using a test-bed and real measures elaborated with a the proposed Particle Swarm Adaptive Scattering (PSAS) algorithm. The tracking of a UAV moving in the 3D space is investigated emulating the UAV trajectory using the proposed Confined Area Random Aerial Trajectory Emulator (CARATE) algorithm

Systèmes de localisation en temps réel basés sur les réseaux de communication sans fil

Des techniques fiables de radiolocalisation s’avèrent indispensables au développement d’un grand nombre de nouveaux systèmes pertinents. Les techniques de localisation basées sur les réseaux de communication sans-fil (WNs) sont particulièrement adéquates aux espaces confinés et fortement urbanisés. Le présent projet de recherche s’intéresse aux systèmes de localisation en temps réel (RTLS) basés sur les technologies de communication sans-fil existantes. Deux nouvelles techniques de radiolocalisation alternatives sont proposées pour améliorer la précision de positionnement des nœuds sans-fil mobiles par rapport aux méthodes conventionnelles basées sur la puissance des signaux reçus (RSS). La première méthode de type géométrique propose une nouvelle métrique de compensation entre les puissances de signaux reçus par rapport à des paires de stations réceptrices fixes. L’avantage de cette technique est de réduire l’effet des variations du milieu de propagation et des puissances d’émission des signaux sur la précision de localisation. La même métrique est sélectionnée pour former les signatures utilisées pour créer la carte radio de l’environnement de localisation durant la phase hors-ligne dans la deuxième méthode de type analyse de situation. Durant la phase de localisation en temps réel, la technique d’acquisition comprimée (CS) est appliquée pour retrouver les positions des nœuds mobiles à partir d’un nombre réduit d’échantillons de signaux reçus en les comparant à la carte radio préétablie. Le calcul d’algèbre multilinéaire proposé dans ce travail permet l’utilisation de ce type de métrique ternaire, équivalemment la différence des temps d’arrivée (TDOA), pour calculer les positions des cibles selon la technique de CS. Les deux méthodes sont ensuite validées par des simulations et des expérimentations effectuées dans des environnements à deux et à trois dimensions. Les expériences ont été menées dans un bâtiment multi-étages (MFB) en utilisant l’infrastructure sans-fil existante pour retrouver conjointement la position et l’étage des cibles en utilisant les techniques proposées. Un exemple emblématique de l’application des RTLS dans les zones urbaines est celui des systèmes de transport intelligents (ITS) pour améliorer la sécurité routière. Ce projet s’intéresse également à la performance d’une application de sécurité des piétons au niveau des intersections routières. L’accomplissement d’un tel système repose sur l’échange fiable, sous des contraintes temporelles sévères, des données de positionnement géographique entre nœuds mobiles pour se tenir mutuellement informés de leurs présences et positions afin de prévenir les risques de collision. Ce projet mène une étude comparative entre deux architectures d’un système ITS permettant la communication entre piétons et véhicules, directement et via une unité de l’infrastructure, conformément aux standards de communication dans les réseaux ad hoc véhiculaires (VANETs)

Sensors and Systems for Indoor Positioning

This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications