Positioning system | EPFL Graph Search

A positioning system is a system for determining the position of an object in space. One of the most well-known and commonly used positioning systems is the Global Positioning System (GPS). Positioning system technologies exist ranging from worldwide coverage with meter accuracy to workspace coverage with sub-millimeter accuracy. Coverage Interplanetary systems Interplanetary-radio communication systems not only communicate with spacecraft, but they are also used to determine their position. Radar can track targets near the Earth, but spacecraft in deep space must have a working transponder on board to echo a radio signal back. Orientation information can be obtained using star trackers. Global systems Global navigation satellite system Global navigation satellite systems (GNSS) allow specialized radio receivers to determine their 3-D space position, as well as time, with an accuracy of 2–20 metres or tens of nanoseconds. Currently deployed system

Depth Camera and Electromagnetic Field Localization System For IoT Application - High level, lightweight data fusion

Alexis Boegli, Pooneh Mohaghegh, Yves Perriard, Rabia Saeed

This article demonstrates person localization using a hybrid system consisting of an electromagnetic positioning system and a depth camera to authorize access control. The ultimate aim of this system is to distinguish moving people in a defined area by tracking the RF device and the people. It focuses on the application and incorporation of the received data from these two systems. Both systems send data simultaneously which is stored in a Docker container for further analysis. The data is processed in real-time to track the movement of the targets. The centralized database monitoring grants secure access to the information. The motive for using this hybrid system lies in the ever-growing need for accurate position determination for indoor and complex environments. Track and tracing are especially important in access-control applications. The system has a great impact on real-life access-control applications in malls, shops, train stations, and generally everyplace where the access control requires monitoring. The non-blocking feature plus the accuracy can provide ease of use for the users. Moreover, employing a low-frequency tag system does not suffer from the multipath effect and non-line of sight problems that are inevitable for indoor applications. By extending the number of users for a larger area, this system can replace traditional security gates with a pleasant look and comfortable application.

Association for Computing Machinery2021

GNSS/INS Kalman Filter Integrity Monitoring with Uncertain Time Correlated Error Processes

Omar Garcia Crespillo

Safety-critical navigation applications require that estimation errors be reliably quantified and bounded. Over the last decade, significant effort has been put to guarantee a bounded position estimation by using Global Navigation Satellite Systems (GNSS) in satellite-based or ground-based augmentation systems (SBAS, GBAS) and Advanced Receiver Autonomous Integrity Monitoring (ARAIM) for aviation. This has been achieved by carefully designing models that overbound the different residual error components in range measurements (e.g., satellite clock and orbit, tropospheric and multipath among others). On the other hand, and as part of Avionics-based Augmentation Systems (ABAS), the use of Inertial Reference Systems (IRS) has been traditionally included as additional source of redundant navigation information. More recently, the use of Inertial Navigation Systems (INS) with a wider spectrum of possible inertial sensor qualities in tighter integration with GNSS has seen its way in a new Minimum Operational Performance Standard (MOPS). New GNSS/INS systems and standards could still benefit from the methodologies and aspects developed for future multifrequency-multiconstellation GNSS standards. However, safety-related GNSS systems like ARAIM are snapshot-based, that is, the position estimation is performed independently at every epoch, whereas GNSS/INS systems are typically based on Kalman filtering (KF). Therefore, the existing error overbounding models and methodologies are not enough to produce a robust KF position estimation since the impact of time-correlation must also be accounted for. Moreover, it has been observed that the time-correlation of different GNSS errors present also some level of uncertain behavior, which makes very challenging for linear dynamic systems to produce a guaranteed solution. As proposed by GNSS MOPS, there are sources of time-correlated errors that can be well modelled using Gauss-Markov processes (GMP). Using this GMP parametric model, it is possible to capture the uncertain time-correlated nature of errors processes by allowing the variance and time correlation constant of the GMP model to be in a bounded range. Under this situation, the first part of this thesis studies the propagation of the uncertain models through the Kalman filter estimation and provides new theoretical tools in time and frequency domain to bound the KF error estimation covariance. As a result, tight stationary bounding models on the GMP uncertain processes are derived in both continuous and discrete time domain. This is extended to non-stationary models that provide tighter error bounding during an initial transient phase when measurements are first introduced (which will be relevant in scenarios with changing number of visible satellites). The new models can very easily be used during the KF implementation which might be very attractive by regulators and designers.In the second part of the thesis, the new overbounding GMP models are applied for a tightly-coupled GNSS/INS integration. The design of the filter and error models are performed following compatibility with current aviation standards and ARAIM Working Group C results. The impact of the use of the new models is analysed in terms of conservativeness, integrity and availability based on realistic trajectory simulations. The benefit of an overbounded GNSS/INS solution is also compared with the current baseline ARAIM algorithm solution.

EPFL2022

Enabling Collective Operation of Indoor Flying Robots

James Roberts

Collective flying robots show great potential in many diverse indoor applications. Their added robustness, parallel operation and redundancy has clear advantages over a single flying robot. However, flying within cluttered and unprepared indoor environments, even for a single flying robot, is extremely challenging. The main reason why collective flying robots have not yet been successful within indoor environments, is due to a combination of several challenges related to the size constraints placed on an indoor hovering platform, which directly limits the available energy, embedded sensing and processing capabilities of a flying robot. The energetic cost of flying, places limits on the flight endurance and practicality of a swarm of flying robots. The current spatial-coordination approaches implement methods that are either too computationally expensive, or impractical for real-world operation, within unknown and unprepared indoor environments. The goal of this thesis was to develop a practical methodology for enabling energy efficient, autonomous indoor flying robots capable of inter-robot spatial-coordination for unprepared indoor environments, without using external aids. In order to enable collective operation of indoor flying robots, several practical methodologies have been proposed and demonstrated. A generalised design strategy has been proposed to dimension a hovering platform for a specific flight endurance, payload capability and robustness criteria. The developed method can be used as a practical design tool for anyone working with hovering platforms. The dimensioning strategy has created a design, which is highly suitable for carrying the necessary sensing and processing required to enable the collective operation of indoor flying robots. A simple sensing and control strategy is proposed for enabling anti-drift control and obstacle avoidance behaviours on an indoor highly dynamic, hovering platform. The approach has enabled one of the first indoor hovering platforms that could achieve such a capability without using any external aids. For the collective operation of indoor flying robots to work in reality, the on-board energy needs to be managed efficiently and conserved in a way that allows a swarm of robots to be useful, extending beyond the individual 10-20 min flight time. A generalised energy model has been developed, allowing for the accurate estimation of the flight endurance and perching time of hovering platforms. The energy model can be used to optimise the battery selection process of a hovering platform, to obtain the highest possible endurance. This is the only model known that is able to predict any combination of flying endurance and perching times. Additionally, a method of attaching to the ceiling has been presented that allows a flying robot to conserve energy and have a stable birds-eye-view while performing static sensing tasks. By applying energy management techniques, through use of energy modelling and behaviours that reduce the flight time, the energetic cost of flying can be mitigated and the mission endurance can be extended over several hours, which is especially useful for collective operation. A new infrared ranging technique has been developed that allows for a high sensing performance, including long range (12 m), high-speed (1 kHz / # robots) and high resolution (better than 1.1 cm up to 6 m). A practical on-board sensing method using this technique, has been developed that can provide spatial-coordination between multiple robots in three dimensions. The developed approach allows for easily adaptation, to suit other robots and applications, depending on a specific sensing speed and coverage requirement. The developed sensor is the worlds first embedded 3-D relative positioning sensor that has the ability to enable inter-robot spatial-coordination in three dimensions, which is necessary for achieving goal-directed flight on highly dynamic flying robots. A practical autonomous flight control methodology has been demonstrated that can provide hovering platform stabilisation, 3-D obstacle avoidance and 3-D waypoint navigation, all using the 3-D relative positioning sensor. Goal-directed flight and collective deployment have been achieved using only the information from the 3-D relative positioning sensing. The developed methodologies within this thesis, has enabled for the first time, the collective operation of highly dynamic indoor flying robots, without using external aids.