Leticia Zamora-Cadenas

In order to reduce the accident risk in road construction and maintenance, this paper proposes a novel solution for road-worker safety based on an untethered real-time locating system (RTLS). This system tracks the location of workers in real time using ultra-wideband (UWB) technology and indicates if they are in a predefined danger zone or not, where the predefined safe zone is delimited by safety cones. Unlike previous works that focus on road-worker safety by detecting vehicles that enter into the working zone, our proposal solves the problem of distracted workers leaving the safe zone. This paper presents a simple-to-deploy safety system. Our UWB anchors do not need any cables for powering, synchronisation, or data transfer. The anchors are placed inside safety cones, which are already available in construction sites. Finally, there is no need to manually measure the positions of anchors and introduce them to the system thanks to a novel self-positioning approach. Our proposal, apart from automatically estimating the anchors’ positions, also defines the limits of safe and danger zones. These features notably reduce the deployment time of the proposed safety system. Moreover, measurements show that all the proposed simplifications are obtained with an accuracy of 97%.

Real time location systems (RTLSs) are becoming more relevant in a more data driven economy and society due to their wide range of application cases. When the location of an object needs to be tracked with high accuracy, ultra wideband (UWB) technology is usually the best option. Nevertheless, UWB ranging estimates are not completely immune to some sources of error such as non line of sight (NLOS) or multipath conditions. Thus, this paper proposes a real-time classification model based on machine learning (ML) to predict if received ranging estimates are in line of sight (LOS) or NLOS conditions and discard those in NLOS. However, it is also shown that classifying measurements as LOS or NLOS does not guarantee detecting inaccurate ranging estimates, since LOS measurements can also yield large errors. As an example, the ranging root mean square error (RMSE) of the data labelled as LOS in a UWB based localization system database in the literature is of 0.714 m, significantly higher than the theoretical accuracy of a UWB system. Thus, a novel ML-based classification model is proposed to predict the magnitude of the ranging error. After applying the proposed classification model in the same data, the ranging RMSE of those ranging samples classified as most accurate is of only 0.183 m, significantly lower than the best RMSE we can obtain on the classical LOS/NLOS classification approach.

This work presents a novel landing assistance system (LAS) capable of locating a drone for a safe landing after its inspection mission. The location of the drone is achieved by a fusion of ultra-wideband (UWB), inertial measurement unit (IMU) and magnetometer data. Unlike other typical landing assistance systems, the UWB fixed sensors are placed around a 2 × 2 m landing platform and two tags are attached to the drone. Since this type of set-up is suboptimal for UWB location systems, a new positioning algorithm is proposed for a correct performance. First, an extended Kalman filter (EKF) algorithm is used to calculate the position of each tag, and then both positions are combined for a more accurate and robust localisation. As a result, the obtained positioning errors can be reduced by 50% compared to a typical UWB-based landing assistance system. Moreover, due to the small demand of space, the proposed landing assistance system can be used almost anywhere and is deployed easily.

Automated Guided Vehicles (AGV) are unmanned transport vehicles widely used in the industrial sector to substitute manned industrial trucks and conveyors. In order to guarantee safe operation, AGVs must be equipped with a safety system to stop their movement in presence of obstacles or humans in their path. This work presents a novel safety system for AGVs that is based on Ultra Wideband (UWB) technology. Unlike previous works based on UWB Real-Time Location Systems (RTLS), the proposed safety system does not require installing hardware on the plant's infrastructure. Instead, the AGV is equipped with sensors capable of locating the tag of a person or a mobile asset. This simplifies deployment of the solution and enables its use in dynamic environments. The proposed safety system was mounted in an AGV designed by the company ASTI Mobile Robotics. Dynamic measurements showed that the proposed safety system accurately mirrors the relative movement between the AGV and tag. Furthermore, the proposed safety system employs a novel method for post-processing ranging data. Measurements showed that this method improves the accuracy of the system, resulting in a more homogeneously distributed positioning error around a room.

In this paper, we analyze the performance of a positioning system based on the fusion of Ultra-Wideband (UWB) ranging estimates together with odometry and inertial data from the vehicle. For carrying out this data fusion, an Extended Kalman Filter (EKF) has been used. Furthermore, a post-processing algorithm has been designed to remove the Non Line-Of-Sight (NLOS) UWB ranging estimates to further improve the accuracy of the proposed solution. This solution has been tested using both a simulated environment and a real environment. This research work is in the scope of the PRoPART European Project. The different real tests have been performed on the AstaZero proving ground using a Radio Control car (RC car) developed by RISE (Research Institutes of Sweden) as testing platform. Thus, a real time positioning solution has been achieved complying with the accuracy requirements for the PRoPART use case.

The European Union (EU) is bolstering the railway sector with the aim of making it a direct competitor of the aviation sector. For that to occur, railway efficiency has to be improved by means of increasing capacity and reducing operational expenditure. Tracks are currently used below their maximum capacity. Given this fact and the EU's goals for the railway sector, research on solutions for on-board positioning system based on global navigation satellite systems (GNSS) have arisen in recent years. By taking advantage of GNSS, safety critical positioning systems will be able to use the infrastructure more efficiently. However, GNSS based positioning systems still cannot fulfill current normative validation processes, mainly, due to the fact that GNSS based positioning performance evaluation is not compatible with the key performance indicators (KPIs) used to assess railway systems performance: reliability, availability, maintainability, and safety. This paper proposes a methodology and unified key performance indicators (KPIs). Additionally, it shows real examples to address this issue. It aims to fill the gap between the current railway standardization process and any on-board positioning system.

The present article summarises the doctoral dissertation of Leticia Zamora-Cadenas.

This work presents an Impulse Radio Ultra-Wideband ranging system that is compliant with the IEEE802.15.4a standard. A novel Time-of-Arrival estimator is proposed for ranging purposes. This estimator is robust against multipath fading and presents a small ranging error. Measurement results show the good accuracy of the proposed ranging system.