Ferromagnetic Mass Localization in Check Point Configuration Using a Levenberg Marquardt Algorithm

A detection and tracking algorithm for ferromagnetic objects based on a two stage Levenberg Marquardt Algorithm (LMA) is presented. The procedure is applied to localization and magnetic moment estimation of ferromagnetic objects moving in the vicinity of an array of two to four 3-axis magnetometers arranged as a check point configuration. The algorithms first stage provides an estimation of the target trajectory and moment that are further refined using a second iteration where only the position vector is taken as unknown. The whole procedure is fast enough to provide satisfactory results within a few seconds after the target has been detected. Tests were conducted in Soreq NRC assessing various check point scenarios and targets. The results obtained from this experiment show good localization performance and good convivial with “noisy” environment. Small targets can be localized with good accuracy using either a vertical “doorway” two to four sensors configuration or ground level two to four sensors configuration. The calculated trajectory was not affected by nearby magnetic interference such as moving vehicles or a combat soldier inspecting the gateway

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Exploration autonome et efficiente de chantiers miniers souterrains inconnus avec un drone filaire

Abstract: Underground mining stopes are often mapped using a sensor located at the end of a pole that the operator introduces into the stope from a secure area. The sensor emits laser beams that provide the distance to a detected wall, thus creating a 3D map. This produces shadow zones and a low point density on the distant walls. To address these challenges, a research team from the Université de Sherbrooke is designing a tethered drone equipped with a rotating LiDAR for this mission, thus benefiting from several points of view. The wired transmission allows for unlimited flight time, shared computing, and real-time communication. For compatibility with the movement of the drone after tether entanglements, the excess length is integrated into an onboard spool, contributing to the drone payload. During manual piloting, the human factor causes problems in the perception and comprehension of a virtual 3D environment, as well as the execution of an optimal mission. This thesis focuses on autonomous navigation in two aspects: path planning and exploration. The system must compute a trajectory that maps the entire environment, minimizing the mission time and respecting the maximum onboard tether length. Path planning using a Rapidly-exploring Random Tree (RRT) quickly finds a feasible path, but the optimization is computationally expensive and the performance is variable and unpredictable. Exploration by the frontier method is representative of the space to be explored and the path can be optimized by solving a Traveling Salesman Problem (TSP) but existing techniques for a tethered drone only consider the 2D case and do not optimize the global path. To meet these challenges, this thesis presents two new algorithms. The first one, RRT-Rope, produces an equal or shorter path than existing algorithms in a significantly shorter computation time, up to 70% faster than the next best algorithm in a representative environment. A modified version of RRT-connect computes a feasible path, shortened with a deterministic technique that takes advantage of previously added intermediate nodes. The second algorithm, TAPE, is the first 3D cavity exploration method that focuses on minimizing mission time and unwound tether length. On average, the overall path is 4% longer than the method that solves the TSP, but the tether remains under the allowed length in 100% of the simulated cases, compared to 53% with the initial method. The approach uses a 2-level hierarchical architecture: global planning solves a TSP after frontier extraction, and local planning minimizes the path cost and tether length via a decision function. The integration of these two tools in the NetherDrone produces an intelligent system for autonomous exploration, with semi-autonomous features for operator interaction. This work opens the door to new navigation approaches in the field of inspection, mapping, and Search and Rescue missions.La cartographie des chantiers miniers souterrains est souvent réalisée à l’aide d’un capteur situé au bout d’une perche que l’opérateur introduit dans le chantier, depuis une zone sécurisée. Le capteur émet des faisceaux laser qui fournissent la distance à un mur détecté, créant ainsi une carte en 3D. Ceci produit des zones d’ombres et une faible densité de points sur les parois éloignées. Pour relever ces défis, une équipe de recherche de l’Université de Sherbrooke conçoit un drone filaire équipé d’un LiDAR rotatif pour cette mission, bénéficiant ainsi de plusieurs points de vue. La transmission filaire permet un temps de vol illimité, un partage de calcul et une communication en temps réel. Pour une compatibilité avec le mouvement du drone lors des coincements du fil, la longueur excédante est intégrée dans une bobine embarquée, qui contribue à la charge utile du drone. Lors d’un pilotage manuel, le facteur humain entraîne des problèmes de perception et compréhension d’un environnement 3D virtuel, et d’exécution d’une mission optimale. Cette thèse se concentre sur la navigation autonome sous deux aspects : la planification de trajectoire et l’exploration. Le système doit calculer une trajectoire qui cartographie l’environnement complet, en minimisant le temps de mission et en respectant la longueur maximale de fil embarquée. La planification de trajectoire à l’aide d’un Rapidly-exploring Random Tree (RRT) trouve rapidement un chemin réalisable, mais l’optimisation est coûteuse en calcul et la performance est variable et imprévisible. L’exploration par la méthode des frontières est représentative de l’espace à explorer et le chemin peut être optimisé en résolvant un Traveling Salesman Problem (TSP), mais les techniques existantes pour un drone filaire ne considèrent que le cas 2D et n’optimisent pas le chemin global. Pour relever ces défis, cette thèse présente deux nouveaux algorithmes. Le premier, RRT-Rope, produit un chemin égal ou plus court que les algorithmes existants en un temps de calcul jusqu’à 70% plus court que le deuxième meilleur algorithme dans un environnement représentatif. Une version modifiée de RRT-connect calcule un chemin réalisable, raccourci avec une technique déterministe qui tire profit des noeuds intermédiaires préalablement ajoutés. Le deuxième algorithme, TAPE, est la première méthode d’exploration de cavités en 3D qui minimise le temps de mission et la longueur du fil déroulé. En moyenne, le trajet global est 4% plus long que la méthode qui résout le TSP, mais le fil reste sous la longueur autorisée dans 100% des cas simulés, contre 53% avec la méthode initiale. L’approche utilise une architecture hiérarchique à 2 niveaux : la planification globale résout un TSP après extraction des frontières, et la planification locale minimise le coût du chemin et la longueur de fil via une fonction de décision. L’intégration de ces deux outils dans le NetherDrone produit un système intelligent pour l’exploration autonome, doté de fonctionnalités semi-autonomes pour une interaction avec l’opérateur. Les travaux réalisés ouvrent la porte à de nouvelles approches de navigation dans le domaine des missions d’inspection, de cartographie et de recherche et sauvetage

Simulation of a car-sharing transport system for urban mobility

An innovative car-sharing systems for urban areas is proposed. The proposed system is based on a fleet of Personal Intelligent City Accessible Vehicles (PICAVs). The following specific services are provided: instant access, open ended reservation and one way trips. All these features provide users with high flexibility, but create a problem of uneven distribution of vehicles among stations. Therefore, relocations must be performed. Different relocation procedures are proposed: in the first relocation scheme relocations are performed by users while in the other two vehicles relocate automatically thanks to their automation. In the first two management strategies vehicles can be accessed and returned only at stations while in the last one they can be accessed also along the roads. In order to provide transport managers with a useful tool to test the proposed systems in different realities, an object-oriented micro simulator has been developed. The simulation gives in output the transport system performance, in terms of distribution of user waiting times, and the transport system efficiency, which is inversely proportional to the fleet dimension and the number of relocation trips. A meta heuristic optimization algorithm has been developed to optimize the transport system’s characteristics. The optimization algorithm recalls the micro simulator to calculate the optimization’s input data. The micro simulator has been calibrated and validated, and afterwards applied to study two scenarios: Genoa historical city centre, Italy, and Barreiro old town, Portugal. Finally, a sensitivity analysis has been performed in order to study the performances of the system according to modifications of the demand, or of the fleet dimension or of the transport system characteristics

FracBot: Design of wireless underground sensor networks for mapping hydraulic fractures and determining reservoir parameters in unconventional systems

Wireless underground sensor networks (WUSNs) enable a wide variety of emerging applications that are not possible with current underground monitoring techniques, which require miniaturized wireless sensor systems for mapping hydraulic fractures, monitoring unconventional reservoirs and measuring other wellbore parameters. We call these devices FracBots (Fracture Robots), an extension of RFID (Radio Frequency IDentifcation) tags that realize WUSNs for mapping and characterization of hydraulic fractures in unconventional reservoirs. The objective of this thesis is to design fully integrated magnetic induction (MI)-based FracBots (WUSNs) that enable reliable and e fficient wireless communications in underground oil reservoirs for performing the in-situ monitoring of oil reservoirs. This is very crucial for determining the sweet spot of oil and natural gas reserves. To this end, we have contributed in four areas as follows: fi rst, we develop a novel cross-layer communication framework for MI-based FracBot networks in dynamically changing underground environments. The framework combines a joint selection of modulation, channel coding, power control and a geographic forwarding paradigm. Second, we develop a novel MI-based localization framework that exploits the unique properties of MI- eld to determine the locations of the randomly deployed FracBot nodes in oil reservoirs. Third, we develop an accurate energy framework of a linear FracBot network topology that generates feasible nodes' transmission rates and network topology while always guaranteeing su fficient energy. Then, we design, develop, and fabricate MI-based FracBot nodes. Finally, to validate the performance of our solutions in our produced prototype of FracBot nodes, we develop a physical MI-based WUSN testbed.Ph.D

Environment Search Planning Subject to High Robot Localization Uncertainty

As robots find applications in more complex roles, ranging from search and rescue to healthcare and services, they must be robust to greater levels of localization uncertainty and uncertainty about their environments. Without consideration for such uncertainties, robots will not be able to compensate accordingly, potentially leading to mission failure or injury to bystanders. This work addresses the task of searching a 2D area while reducing localization uncertainty. Wherein, the environment provides low uncertainty pose updates from beacons with a short range, covering only part of the environment. Otherwise the robot localizes using dead reckoning, relying on wheel encoder and yaw rate information from a gyroscope. As such, outside of the regions with position updates, there will be unconstrained localization error growth over time. The work contributes a Belief Markov Decision Process formulation for solving the search problem and evaluates the performance using Partially Observable Monte Carlo Planning (POMCP). Additionally, the work contributes an approximate Markov Decision Process formulation and reduced complexity state representation. The approximate problem is evaluated using value iteration. To provide a baseline, the Google OR-Tools package is used to solve the travelling salesman problem (TSP). Results are verified by simulating a differential drive robot in the Gazebo simulation environment. POMCP results indicate planning can be tuned to prioritize constraining uncertainty at the cost of increasing path length. The MDP formulation provides consistently lower uncertainty with minimal increases in path length over the TSP solution. Both formulations show improved coverage outcomes