Target localization and autonomous navigation using wireless sensor networks -a pseudogradient algorithm approach

pre-printAutonomous mobile robots (AMRs) operating in unknown environments face twin challenges: 1) localization and 2) efficient directed navigation. This paper describes a two-tiered approach to solving these challenges: 1) by developing novel wireless-sensor-network (WSN)-based localization methods and 2) by using WSN-AMR interaction for navigation. The goal is to have an AMR travel from any point within a WSN-covered region to an identified target location without the aid of global sensing and position information. In this research, the target is reached as follows: 1) by producing a magnitude distribution within the WSN region that has a target-directed pseudogradient (PG) and 2) by having the WSN efficiently navigate the AMRs using the PG. This approach utilizes only the topology of the network and the received signal strength (RSS) among the sensor nodes to create the PG. This research shows that, even in the absence of global positioning information, AMRs can successfully navigate toward a target location using only the RSS in their local neighborhood to compute an optimal path. The utility of the proposed scheme is proved through extensive simulation and hardware experiments

Target Localization and Autonomous Navigation Using Wireless Sensor Networks-A Pseudogradient Algorithm Approach

Abstract-Autonomous mobile robots (AMRs) operating in unknown environments face twin challenges: 1) localization and 2) efficient directed navigation. This paper describes a two-tiered approach to solving these challenges: 1) by developing novel wireless-sensor-network (WSN)-based localization methods and 2) by using WSN-AMR interaction for navigation. The goal is to have an AMR travel from any point within a WSN-covered region to an identified target location without the aid of global sensing and position information. In this research, the target is reached as follows: 1) by producing a magnitude distribution within the WSN region that has a target-directed pseudogradient (PG) and 2) by having the WSN efficiently navigate the AMRs using the PG. This approach utilizes only the topology of the network and the received signal strength (RSS) among the sensor nodes to create the PG. This research shows that, even in the absence of global positioning information, AMRs can successfully navigate toward a target location using only the RSS in their local neighborhood to compute an optimal path. The utility of the proposed scheme is proved through extensive simulation and hardware experiments. Index Terms-Goal-directed navigation, pseudo topological gradient, wireless received signal strength (RSS), wireless-sensornetwork (WSN)-assisted target localization

Assisted Navigation Algorithm for Wireless Sensor Actuator and Robot Networks

Wireless Sensor, Actuator and Robot Networks (WSARNs) are made of mobile and static sensor nodes that interact in order to collaboratively perform specific tasks, such as supporting assisted navigation for mobile robotic nodes that carry out requested operations in hostile environments, where the human presence is impracticable. In this regard, it is worth noting that assisted navigation algorithms have a highly dynamic nature, and are implemented by sensor nodes that are characterized by limited transmission power and lean autonomy in terms of computing and memory capacity. This paper presents an improved version of the assisted navigation algorithm based on the concept of “credit field”. The main aim of the proposed algorithm is to reduce and balance the energy consumption among the static sensor nodes when running the algorithm to manage the presence of obstacles and adversary areas, thus extending the lifetime of WSARNs. The algorithm has been tested on a hybrid sensor network that employs Mica2 Motes as static sensor nodes and Lego Mindstorms robots integrated with a Stargate board developed by Crossbow as mobile nodes

Improving the Signal Propagation at 2.4 GHz Using Conductive Membranes

© 2017 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works[EN] When IEEE 802.11 at 2.4-GHz signal crosses different surfaces, it is generally reduced, but we have seen that it does not happen for all materials. Conductive membranes are able to transport electric charges when they are submerged into water with electrolytes, so we take profit of their features in order to know in which cases the received signal strength indicator (RSSI) can be improved. In order to achieve our goal, the RSSI is measured at different distances using different environments for the membranes, air, and water environment with different conductivities (distillated water, tap water, and salty water). Results show that different membranes environment produce different signal strengths. Moreover, they can be positive or negative depending on the environment of the membranes and the distance from the access point. In some cases, we registered an increase of more than 14 dBm of the signal when we were using those membranes.This work was supported in part by the "Ministerio de Ciencia e Innovacion," through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigacion Fundamental," project TEC2011-27516.Parra-Boronat, L.; Sendra, S.; Vincent Vela, MC.; García Gabaldón, M.; Lloret, J. (2017). Improving the Signal Propagation at 2.4 GHz Using Conductive Membranes. IEEE Systems Journal. 11(4):2315-2324. https://doi.org/10.1109/JSYST.2015.2496204S2315232411

Sensor Network Based Collision-Free Navigation and Map Building for Mobile Robots

Safe robot navigation is a fundamental research field for autonomous robots including ground mobile robots and flying robots. The primary objective of a safe robot navigation algorithm is to guide an autonomous robot from its initial position to a target or along a desired path with obstacle avoidance. With the development of information technology and sensor technology, the implementations combining robotics with sensor network are focused on in the recent researches. One of the relevant implementations is the sensor network based robot navigation. Moreover, another important navigation problem of robotics is safe area search and map building. In this report, a global collision-free path planning algorithm for ground mobile robots in dynamic environments is presented firstly. Considering the advantages of sensor network, the presented path planning algorithm is developed to a sensor network based navigation algorithm for ground mobile robots. The 2D range finder sensor network is used in the presented method to detect static and dynamic obstacles. The sensor network can guide each ground mobile robot in the detected safe area to the target. Furthermore, the presented navigation algorithm is extended into 3D environments. With the measurements of the sensor network, any flying robot in the workspace is navigated by the presented algorithm from the initial position to the target. Moreover, in this report, another navigation problem, safe area search and map building for ground mobile robot, is studied and two algorithms are presented. In the first presented method, we consider a ground mobile robot equipped with a 2D range finder sensor searching a bounded 2D area without any collision and building a complete 2D map of the area. Furthermore, the first presented map building algorithm is extended to another algorithm for 3D map building

Target Localization and Tracking in Wireless Sensor Networks

This thesis addresses the target localization problem in wireless sensor networks (WSNs) by employing statistical modeling and convex relaxation techniques. The first and the second part of the thesis focus on received signal strength (RSS)- and RSS-angle of arrival (AoA)-based target localization problem, respectively. Both non-cooperative and cooperative WSNs are investigated and various settings of the localization problem are of interest (e.g. known and unknown target transmit power, perfectly and imperfectly known path loss exponent). For all cases, maximum likelihood (ML) estimation problem is first formulated. The general idea is to tightly approximate the ML estimator by another one whose global solution is a close representation of the ML solution, but is easily obtained due to greater smoothness of the derived objective function. By applying certain relaxations, the solution to the derived estimator is readily obtained through general-purpose solvers. Both centralized (assumes existence of a central node that collects all measurements and carries out all necessary processing for network mapping) and distributed (each target determines its own location by iteratively solving a local representation of the derived estimator) algorithms are described. More specifically, in the case of centralized RSS-based localization, second-order cone programming (SOCP) and semidefinite programming (SDP) estimators are derived by applying SOCP and SDP relaxation techniques in non-cooperative and cooperative WSNs, respectively. It is also shown that the derived SOCP estimator can be extended for distributed implementation in cooperative WSNs. In the second part of the thesis, derivation procedure of a weighted least squares (WLS) estimator by converting the centralized non-cooperative RSS-AoA localization problem into a generalized trust region sub-problem (GTRS) framework, and an SDP estimator by applying SDP relaxations to the centralized cooperative RSS-AoA localization problem are described. Furthermore, a distributed SOCP estimator is developed, and an extension of the centralized WLS estimator for non-cooperative WSNs to distributed conduction in cooperative WSNs is also presented. The third part of the thesis is committed to RSS-AoA-based target tracking problem. Both cases of target tracking with fixed/static anchors and mobile sensors are investigated. First, the non-linear measurement model is linearized by applying Cartesian to polar coordinates conversion. Prior information extracted from target transition model is then added to the derived model, and by following maximum a posteriori (MAP) criterion, a MAP algorithm is developed. Similarly, by taking advantage of the derived model and the prior knowledge, Kalman filter (KF) algorithm is designed. Moreover, by allowing sensor mobility, a simple navigation routine for sensors’ movement management is described, which significantly enhances the estimation accuracy of the presented algorithms even for a reduced number of sensors. The described algorithms are assessed and validated through simulation results and real indoor measurements

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

ALO: Sistemas de localización y orientación por ángulos basados en recepción diferencial

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior. Fecha de lectura : 20-07-2016La presente tesis doctoral presenta un algoritmo diseñado para proporcionar la localización y orientación a un nodo, necesitando para ello la existencia de una serie de balizas cuyas posiciones deben ser conocidas. El algoritmo propuesto se ha implementado utilizando señales de ultrasonidos como medio de comunicación entre balizas y nodos, obteniendo un sistema en el que los nodos son capaces de localizarse y orientarse en el interior de viviendas o naves de forma precisa (el sistema alcanza precisiones de unos pocos centímetros en la localización y de unos pocos grados en la orientación) acarreando un reducido coste adicional para cada nodo. El algoritmo se basa en que cada uno de los nodos mide el retardo con el que se recibe la señal generada por cada baliza mediante el uso de varios receptores desplegados en el propio nodo. Posteriormente, el nodo utiliza esta información para determina el ángulo con el que se recibe la señal y, combinando los ángulos percibidos por el nodo respecto a varias balizas, determinar su posición y orientación en todo momento. El algoritmo demanda un reducido coste computacional, lo que permite implementarlo en los microcontroladores o FPGAs de que ya disponen los nodos para realizar las tareas de navegación y sensado, por lo que no se incurre en un coste adicional en este sentido. Además, como el algoritmo se ejecuta en cada uno de los nodos de forma autónoma, el sistema resultante es altamente escalable, permitiendo desplegar cualquier número de nodos en un determinado espacio. En esta tesis se presentan cuatro variantes del algoritmo, las cuales se analizan en detalle tanto analítica como experimentalmente. Cada una de estas variantes permite elegir entre diferentes prestaciones, permitiendo el desarrollo de sistemas en donde cada nodo sólo necesita de un receptor, pero demanda recibir la señal de al menos 4 balizas para poder conocer su posición, hasta dispositivos con cuatro receptores que sólo necesitan de 2 balizas para conocer su posición y orientación y, además, pueden funcionar incluso con la pérdida de uno de sus receptores (a costa de una menor precisión). La decisión entre una u otra variante del algoritmo depende de las necesidades del sistema, siendo posible combinarlas. Adicionalmente, en la presente tesis se recogen los resultados obtenidos al cambiar la tecnología de ultrasonidos por una basada en sonido en el rango audible por el ser humano. Se demuestra que dicha tecnología no es adecuada al obtener mucha menor precisión debido, principalmente, al uso de una menor frecuencia en la señal de referencia y a la interferencia generada por los rebotes en las paredes, techo y objetos situados en el entorno de aplicación.This doctoral thesis presents an algorithm designed to provide the localization and orientation information to a node, requesting the deployment of multiple beacons whose positions shall be known. The performance of the algorithm has been tested using ultrasounds as the reference signals between the beacons and the nodes, obtaining a system where the nodes can locate and orientate themselves precisely in an indoor environment (with an error of a few centimeters in their position and an error of a few degrees in their orientation), demanding a low cost increase to each node. This algorithm is based on measuring the propagation delay of the signal generated at the beacons in multiple receivers deployed in each node. With this information, the node is able to know the reception angle of the signal and, combining the angles received with respect to different beacons, determines its position and orientation. The computational cost of the algorithm is so low that it can be implemented in the microcontrollers or FPGAs already used in the node for the navigation and sensing tasks, so it does not incur in any additional cost for computational purposes. Additionally, as the algorithm is executed autonomously in each node, the system supports any number of nodes deployed in a defined region, resulting in a high scalable system. Four versions of the algorithm are presented, analyzed and experimentally tested. These alternatives allow choosing a different algorithm in function on the performance demanded by the system: from systems where the node only needs one receiver, but demands staying in an area covered by four beacons to know the position of the node, up to systems where the node demands four receivers, but only needs two beacon signals to know its position and orientation. These alternatives can be combined, increasing the performance of the system. Additionally, this document shows the results obtained when the algorithm is implemented with sound signals, concluding that this technology is not a good choice as it obtains lower performance than the same algorithm implemented with ultrasonic technology. This lower performance is caused due to the lower frequency of sound signals and due to the interferences of the rebounds on the ceiling, walls and objects deployed in the environment