Range-only SLAM with a mobile robot and a Wireless Sensor Networks

This paper presents the localization of a mobile robot while simultaneously mapping the position of the nodes of a Wireless Sensor Network using only range measurements. The robot can estimate the distance to nearby nodes of the Wireless Sensor Network by measuring the Received Signal Strength Indicator (RSSI) of the received radio messages. The RSSI measure is very noisy, especially in an indoor environment due to interference and reflections of the radio signals. We adopted an Extended Kalman Filter SLAM algorithm to integrate RSSI measurements from the different nodes over time, while the robot moves in the environment. A simple pre-processing filter helps in reducing the RSSI variations due to interference and reflections. Successful experiments are reported in which an average localization error less than 1 m is obtained when the SLAM algorithm has no a priori knowledge on the wireless node positions, while a localization error less than 0.5 m can be achieved when the position of the node is initialized close to the their actual position. These results are obtained using a generic path loss model for the transmission channel. Moreover, no internode communication is necessary in the WSN. This can save energy and enables to apply the proposed system also to fully disconnected networks

Application of a mobile robot to spatial mapping of radioactive substances in indoor environment

Nuclear medicine requires the use of radioactive substances that can contaminate critical areas (dangerous or hazardous) where the presence of a human must be reduced or avoided. The present work uses a mobile robot in real environment and 3D simulation to develop a method to realize spatial mapping of radioactive substances. The robot should visit all the waypoints arranged in a grid of connectivity that represents the environment. The work presents the methodology to perform the path planning, control and estimation of the robot location. For path planning two methods are approached, one a heuristic method based on observation of problem and another one was carried out an adaptation in the operations of the genetic algorithm. The control of the actuators was based on two methodologies, being the first to follow points and the second to follow trajectories. To locate the real mobile robot, the extended Kalman filter was used to fuse an ultra-wide band sensor with odometry, thus estimating the position and orientation of the mobile agent. The validation of the obtained results occurred using a low cost system with a laser range finder.A medicina nuclear requer o uso de substâncias radioativas que pode vir a contaminar áreas críticas, onde a presença de um ser humano deve ser reduzida ou evitada. O presente trabalho utiliza um robô móvel em ambiente real e em simulação 3D para desenvolver um método para o mapeamento espacial de substâncias radioativas. O robô deve visitar todos os waypoinst dispostos em uma grelha de conectividade que representa o ambiente. O trabalho apresenta a metodologia para realizar o planejamento de rota, controle e estimação da localização do robô. Para o planejamento de rota são abordados dois métodos, um baseado na heurística ao observar o problema e ou outro foi realizado uma adaptação nas operações do algoritmo genético. O controle dos atuadores foi baseado em duas metodologias, sendo a primeira para seguir de pontos e a segunda seguir trajetórias. Para localizar o robô móvel real foi utilizado o filtro de Kalman extendido para a fusão entre um sensor ultra-wide band e odometria, estimando assim a posição e orientação do agente móvel. A validação dos resultados obtidos ocorreu utilizando um sistema de baixo custo com um laser range finder

Development and evaluation of a robot trilateration localization system using four beacons

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáKAWASHIMA, S. Y. Esta dissertação tem como objetivo investigar a localização de robôs móveis em um ambiente de software virtual (Simtwo) usando quatro beacons e um sensor LiDAR. A localização de robôs desempenha um papel crucial em várias indústrias e aplicações, como robótica, automação e navegação. Uma localização precisa é vital para que os robôs móveis possam navegar e desempenhar tarefas de forma eficaz. Para alcançar a localização, beacons artificiais são usados como pontos de referência. Para fazer isso, um método comumente usado é a trilateração, que requer pelo menos três beacons para serem reconhecidos simultaneamente, juntamente com a distância entre elas e o robô. Usando essa técnica e um algoritmo de identificação de beacons, este trabalho calculará a posição global estimada e erros associados de um robô em uma simulação virtual da Robot Factory no Simtwo.KAWASHIMA, S. Y. Esta dissertação tem como objetivo investigar a localização de robôs móveis em um ambiente de software virtual (Simtwo) usando quatro beacons e um sensor LiDAR. A localização de robôs desempenha um papel crucial em várias indústrias e aplicações, como robótica, automação e navegação. Uma localização precisa é vital para que os robôs móveis possam navegar e desempenhar tarefas de forma eficaz. Para alcançar a localização, beacons artificiais são usados como pontos de referência. Para fazer isso, um método comumente usado é a trilateração, que requer pelo menos três beacons para serem reconhecidos simultaneamente, juntamente com a distância entre elas e o robô. Usando essa técnica e um algoritmo de identificação de beacons, este trabalho calculará a posição global estimada e erros associados de um robô em uma simulação virtual da Robot Factory no Simtwo

Feasibility of LoRa for Smart Home Indoor Localization

With the advancement of low-power and low-cost wireless technologies in the past few years, the Internet of Things (IoT) has been growing rapidly in numerous areas of Industry 4.0 and smart homes. With the development of many applications for the IoT, indoor localization, i.e., the capability to determine the physical location of people or devices, has become an important component of smart homes. Various wireless technologies have been used for indoor localization includingWiFi, ultra-wideband (UWB), Bluetooth low energy (BLE), radio-frequency identification (RFID), and LoRa. The ability of low-cost long range (LoRa) radios for low-power and long-range communication has made this radio technology a suitable candidate for many indoor and outdoor IoT applications. Additionally, research studies have shown the feasibility of localization with LoRa radios. However, indoor localization with LoRa is not adequately explored at the home level, where the localization area is relatively smaller than offices and corporate buildings. In this study, we first explore the feasibility of ranging with LoRa. Then, we conduct experiments to demonstrate the capability of LoRa for accurate and precise indoor localization in a typical apartment setting. Our experimental results show that LoRa-based indoor localization has an accuracy better than 1.6 m in line-of-sight scenario and 3.2 m in extreme non-line-of-sight scenario with a precision better than 25 cm in all cases, without using any data filtering on the location estimates

A Low Cost Ultrasound-based Localisation System

This paper presents a low-cost localisation system based on ultrasonic sensing and time of flight measurements. A compact ultrasound emitter has been designed to generate omnidirectional train of ultrasound pulses which are then picked up by several fixed receivers measuring the time difference of arrival. A least squares approach is used to analytically obtain a first estimate of the emitter position, which is then refined through steepest descent optimisation. All processing is done via a standard Arduino platform, proving the low computational demands of the method. Localisation results are validated against a state-of-the-art Optitrack motion capture system. It is shown that the system can cover a 4.3x3.1m arena with a mean error localisation error of 1.57cm and an average standard deviation of 1.39cm throughout the arena

SwarMer: A Decentralized Localization Framework for Flying Light Specks

Swarm-Merging, SwarMer, is a decentralized framework to localize Flying Light Specks (FLSs) to render 2D and 3D shapes. An FLS is a miniature sized drone equipped with one or more light sources to generate different colors and textures with adjustable brightness. It is battery powered, network enabled with storage and processing capability to implement a decentralized algorithm such as SwarMer. An FLS is unable to render a shape by itself. SwarMer uses the inter-FLS relationship effect of its organizational framework to compensate for the simplicity of each individual FLS, enabling a swarm of cooperating FLSs to render complex shapes. SwarMer is resilient to both FLSs failing and FLSs leaving to charge their battery. It is fast, highly accurate, and scales to remain effective when a shape consists of a large number of FLSs.Comment: Source code available at https://github.com/flyinglightspeck/SwarMer. See https://youtu.be/BIiBxD_aUz8 for a MATLAB demonstration of SwarMer, https://youtu.be/Lh11tWWOP5Y for two relative localization techniques as SwarMer plugins. SwarMer is able to transition FLSs from illuminating one point cloud to the next point cloud, see https://youtu.be/4GhhlSq4Ur

INDOOR LOCALIZATION AND TRACKING: METHODS, TECHNOLOGIES AND RESEARCH CHALLENGES

The paper presents a comprehensive survey of contemporary methods, technologies and systems for localization and tracking of moving objects in indoor environment and gives their comparison according to various criteria, such as accuracy, privacy, scalability and type of location data. Some representative examples of indoor LBS applications available on the market are presented that are based on reviewed localization technologies. The prominent research directions in this domain are categorized and discussed

An indoor variance-based localization technique utilizing the UWB estimation of geometrical propagation parameters

A novel localization framework is presented based on ultra-wideband (UWB) channel sounding, employing a triangulation method using the geometrical properties of propagation paths, such as time delay of arrival, angle of departure, angle of arrival, and their estimated variances. In order to extract these parameters from the UWB sounding data, an extension to the high-resolution RiMAX algorithm was developed, facilitating the analysis of these frequency-dependent multipath parameters. This framework was then tested by performing indoor measurements with a vector network analyzer and virtual antenna arrays. The estimated means and variances of these geometrical parameters were utilized to generate multiple sample sets of input values for our localization framework. Next to that, we consider the existence of multiple possible target locations, which were subsequently clustered using a Kim-Parks algorithm, resulting in a more robust estimation of each target node. Measurements reveal that our newly proposed technique achieves an average accuracy of 0.26, 0.28, and 0.90 m in line-of-sight (LoS), obstructed-LoS, and non-LoS scenarios, respectively, and this with only one single beacon node. Moreover, utilizing the estimated variances of the multipath parameters proved to enhance the location estimation significantly compared to only utilizing their estimated mean values