LPS Auto-Calibration Algorithm with Predetermination of Optimal Zones

Accurate coordinates for active beacons placed in the environment are required in Local Positioning Systems (LPS). These coordinates and the distances (or differences of distances) measured between the beacons and the mobile node to be localized are inputs to most trilateration algorithms. As a first approximation, such coordinates are obtained by means of manual measurements (a time-consuming and non-flexible method), or by using a calibration algorithm (i.e., automatic determination of beacon coordinates from ad hoc measurements). This paper presents a method to calibrate the beacons’ positions in a LPS using a mobile receiver. The method has been developed for both, spherical and hyperbolic trilateration. The location of only three test points must be known a priori, while the position of the other test points can be unknown. Furthermore, the paper describes a procedure to estimate the optimal positions, or approximate areas in the coverage zone, where the test-points necessary to calibrate the ultrasonic LPS should be placed. Simulation and experimental results show the improvement achieved when these optimal test-points are used instead of randomly selected ones

Autocalibrating vision guided navigation of unmanned air vehicles via tactical monocular cameras in GPS denied environments

This thesis presents a novel robotic navigation strategy by using a conventional tactical monocular camera, proving the feasibility of using a monocular camera as the sole proximity sensing, object avoidance, mapping, and path-planning mechanism to fly and navigate small to medium scale unmanned rotary-wing aircraft in an autonomous manner. The range measurement strategy is scalable, self-calibrating, indoor-outdoor capable, and has been biologically inspired by the key adaptive mechanisms for depth perception and pattern recognition found in humans and intelligent animals (particularly bats), designed to assume operations in previously unknown, GPS-denied environments. It proposes novel electronics, aircraft, aircraft systems, systems, and procedures and algorithms that come together to form airborne systems which measure absolute ranges from a monocular camera via passive photometry, mimicking that of a human-pilot like judgement. The research is intended to bridge the gap between practical GPS coverage and precision localization and mapping problem in a small aircraft. In the context of this study, several robotic platforms, airborne and ground alike, have been developed, some of which have been integrated in real-life field trials, for experimental validation. Albeit the emphasis on miniature robotic aircraft this research has been tested and found compatible with tactical vests and helmets, and it can be used to augment the reliability of many other types of proximity sensors

Auto-localization algorithm for local positioning systems

This paper studies the problem of determining the position of beacon nodes in Local Positioning Systems (LPSs), for which there are no inter-beacon distance measurements available and neither the mobile node nor any of the stationary nodes have positioning or odometry information. The common solution is implemented using a mobile node capable of measuring its distance to the stationary beacon nodes within a sensing radius. Many authors have implemented heuristic methods based on optimization algorithms to solve the problem. However, such methods require a good initial estimation of the node positions in order to find the correct solution. In this paper we present a new method to calculate the inter-beacon distances, and hence the beacons positions, based in the linearization of the trilateration equations into a closed-form solution which does not require any approximate initial estimation. The simulations and field evaluations show a good estimation of the beacon node positions

Error Estimation for the Linearized Auto-Localization Algorithm

The Linearized Auto-Localization (LAL) algorithm estimates the position of beacon nodes in Local Positioning Systems (LPSs), using only the distance measurements to a mobile node whose position is also unknown. The LAL algorithm calculates the inter-beacon distances, used for the estimation of the beacons’ positions, from the linearized trilateration equations. In this paper we propose a method to estimate the propagation of the errors of the inter-beacon distances obtained with the LAL algorithm, based on a first order Taylor approximation of the equations. Since the method depends on such approximation, a confidence parameter τ is defined to measure the reliability of the estimated error. Field evaluations showed that by applying this information to an improved weighted-based auto-localization algorithm (WLAL), the standard deviation of the inter-beacon distances can be improved by more than 30% on average with respect to the original LAL method

Vision-Based Mobile Robot Self-localization and Mapping System for Indoor Environment

Localizing accurately and building map of an environment concurrently is a key factor of a mobile robot system. In this system, the robot makes localization and mapping with artificial landmarks and map-based system. It is a process by which a mobile robot can build a map of an environment while continuously determining the location of the robot within the map. The system estimates the robot position in indoor environments using sensors; a camera, three ultrasonic sensors and encoders. The main contribution of this paper is to reduce computational time and improve mapping with map-based system. The self-localization of mobile robot in an indoor environment is advanced through the construction of map based on sensors and recognition of artificial landmarks. Vision based localization system can benefit from using with ultrasonic sensors. From captured images, the system makes landmark detection by using Canny edge detection and Chain-code Approximation algorithms to represent the contour of landmarks by using edge points. The Kalman filter is aimed to accurately estimate position and orientation of the robot using relative distances to walls or artificial landmarks in environments. A robotic system is capable of mapping in an indoor environment and localizing with respect to the map, in real time, using artificial landmarks and sensors

Distance Estimation Between Transceivers Over Short Distances

Three methods for determining distances between a user and a fixed coordinate system are considered. One system is based on 802.11g packet communications, another on HF radio carrier frequency interference, and the final system on a signal transmitted over a radio channel. Emphasis is placed on the use of these systems in indoor, building-sized environments. Tests are performed to examine the effectivness and potential of these methods. None of the suggested methods are determined to be usable. Reasons for their failure are examined

Development of a real-time ultrasonic sensing system for automated and robotic welding

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The implementation of robotic technology into welding processes is made difficult by the inherent process variables of part location, fit up, orientation and repeatability. Considering these aspects, to ensure weld reproducibility consistency and quality, advanced adaptive control techniques are essential. These involve not only the development of adequate sensors for seam tracking and joint recognition but also developments of overall machines with a level of artificial intelligence sufficient for automated welding. The development of such a prototype system which utilizes a manipulator arm, ultrasonic sensors and a transistorised welding power source is outlined. This system incorporates three essential aspects. It locates and tracks the welding seam ensuring correct positioning of the welding head relatively to the joint preparation. Additionally, it monitors the joint profile of the molten weld pool and modifies the relevant heat input parameters ensuring consistent penetration, joint filling and acceptable weld bead shape. Finally, it makes use of both the above information to reconstruct three-dimensional images of the weld pool silhouettes providing in-process inspection capabilities of the welded joints. Welding process control strategies have been incorporated into the system based on quantitative relationships between input parameters and weld bead shape configuration allowing real-time decisions to be made during the process of welding, without the need for operation intervention.British Technology Group (BTG

Doctor of Philosophy

dissertationX-ray computed tomography (CT) is a widely popular medical imaging technique that allows for viewing of in vivo anatomy and physiology. In order to produce high-quality images and provide reliable treatment, CT imaging requires the precise knowledge of t

Cooperative Localization and SLAM Based on the Extended Information Filter

Virtual realit