A highly accurate and scalable approach for addressing location uncertainty in asset tracking applications

Tracking systems that use RFID are increasingly being used for monitoring the movement of goods in supply chains. While these systems are effective, they still have to overcome significant challenges, such as missing reads, to improve their performance further. In this paper, we describe an optimised tracking algorithm to predict the locations of objects in the presence of missed reads using particle filters. To achieve high location accuracy we develop a model that characterises the motion of objects in a supply chain. The model is also adaptable to the changing nature of a business such as flow of goods, path taken by goods through the supply chain, and sales volumes. A scalable tracking algorithm is achieved by an object compression technique, which also leads to a significant improvement in accuracy. The results of a detailed simulation study shows that our object compression technique yields high location accuracy (above 98% at 0.95 read rate) with significant reductions in execution time and memory usage.Rengamathi Sankarkumar, Damith C. Ranasinghe, Thuraiappah Sathya

An extended Kalman filter for localisation in occupancy grid maps

© 2015 IEEE. The main contribution of this paper is an extended Kalman filter (EKF) based framework for mobile robot localisation in occupancy grid maps (OGMs), when the initial location is approximately known. We propose that the observation equation be formulated using the unsigned distance transform based Chamfer Distance (CD) that corresponds to a laser scan placed within the OGM, as a constraint. This formulation provides an alternative to the ray-casting model, which generally limited localisation in OGMs to Particle Filter (PF) based frameworks that can efficiently deal with observation models that are not analytic. Usage of an EKF is attractive due to its computational efficiency, especially as it can be applied to modern day field robots with limited on-board computing power. Furthermore, well-developed tools for dealing with potential outliers in the observations or changes to the motion model, exists in the EKF framework. The effectiveness of the proposed algorithm is demonstrated using a number of simulation and real life examples, including one in a dynamic environment populated with people

Wearable Quarter-Wave Folded Microstrip Antenna for Passive UHF RFID Applications

A wearable low-profile inset-fed quarter-wave folded microstrip patch antenna for noninvasive activity monitoring of elderly is presented. The proposed antenna is embedded with a sensor-enabled passive radio-frequency identification (RFID) tag operating in the ultra-high frequency (UHF) industrial-scientific-medical (ISM) band around 900 MHz. The device exhibits a low and narrow profile based on a planar folded quarter-wave length patch structure and is integrated on a flexible substrate to maximise comfort to the wearer. An extended ground plane made from silver fabric successfully minimises the impact of the human body on the antenna performance. Measurements on a prototype demonstrate a reflection coefficient (S₁₁) of −30 dB at resonance and a −10 dB bandwidth from 920 MHz to 926 MHz. Simulation results predict a maximum gain of 2.8 dBi. This is confirmed by tag measurements where a 4-meter read range is achieved using a transmit power of 30 dBm, for the case where the passive wearable tag antenna is mounted on a body in a practical setting. This represents an almost 40% increase in read range over an existing dipole antenna placed over a 10 mm isolator layer on a human subject.Thomas Kaufmann, Damith C. Ranasinghe, Ming Zhou, and Christophe Fumeau

Identification of Haptic Based Guiding Using Hard Reins

This paper presents identifications of human-human interaction in which one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several identifications of the interaction between the guider and the follower such as computational models that map states of the follower to actions of the guider and the computational basis of the guider to modulate the force on the rein in response to the trust level of the follower. Based on experimental identification systems on human demonstrations show that the guider and the follower experience learning for an optimal stable state-dependent novel 3rd and 2nd order auto-regressive predictive and reactive control policies respectively. By modeling the follower's dynamics using a time varying virtual damped inertial system, we found that the coefficient of virtual damping is most appropriate to explain the trust level of the follower at any given time. Moreover, we present the stability of the extracted guiding policy when it was implemented on a planar 1-DoF robotic arm. Our findings provide a theoretical basis to design advanced human-robot interaction algorithms applicable to a variety of situations where a human requires the assistance of a robot to perceive the environment

A Methodology for Prognostics under the Conditions of Limited Failure Data Availability

When failure data are limited, data-driven prognostics solutions underperform since the number of failure data samples is insufficient for training prognostics models effectively. In order to address this problem, we present a novel methodology for generating failure data which allows training datasets to be augmented so that the number of failure data samples is increased. In contrast to existing data generation techniques which duplicate or randomly generate data, the proposed methodology is capable of generating new and realistic failure data samples. The methodology utilises the conditional generative adversarial network and auxiliary information pertaining to failure modes to control and direct the failure data generation process. The theoretical foundation of the methodology in a non-parametric setting is presented and we show that it holds in practice using empirical results. The methodology is evaluated in a real-world case study involving the prediction of air purge valve failures in heavy-trucks. Two prognostics models are developed using the gradient boosting machine and random forest classifiers. When these models are trained on the augmented training dataset, they outperformed the best solution previously proposed in the literature for the case study by a large margin. More specifically, costs due to breakdowns and false alarms are reduced by 44%The first author’s PhD research is funded by the EPSRC through a Doctoral Training Partnership grant (EP/M508007/1). This research was partly supported by the Next Generation Digital Infrastructure project (EP/R004935/1) funded by the EPSRC and B

Fast global scan matching for high-speed vehicle navigation

© 2015 IEEE. This paper presents a fast global scan matching technique for high-speed vehicle navigation. The proposed grid-based scan-to-map matching technique collectively handles unprocessed scan points at each grid cell as a grid feature. The grid features are transformed and located in the global frame and updated every time a new scan is acquired. Since registered and updated are only grid features, which are each the mean of scan points in a grid cell, the proposed grid feature matching technique is very fast. Representation for each grid cell by multiple grid features further maintains accuracy regardless of the grid size while fast processing is achieved. The technique is therefore suited for localization of high-speed vehicle navigation. Experimental results show the effectiveness of the proposed technique numerically and experimentally

Climate change-driven losses in ecosystem services of coastal wetlands: A case study in the West coast of Bangladesh

© 2018 The Authors Climate change is globally recognized as one of the key drivers of degradation of coastal wetland ecosystems, causing considerable alteration of services provided by these habitats. Quantifying the physical impacts of climate change on these services is therefore of utmost importance. Yet, practical work in this field is fragmented and scarce in current literature, especially in developing countries which are likely to suffer most from the adverse climate change impacts. Using a coherent scenario-based approach that combines assessment of physical impacts with economic valuation techniques, here we quantify potential climate change driven losses in the value of wetland ecosystems services due to relative sea-level rise (RSLR)-induced inundation in the vulnerable Western coastal area of Bangladesh in 2100. The results show a small inundation area in 2100 under the three IPCC climate scenarios of RCP2.6 (with 0.25 m of RSLR), RCP6.0 (with 1.18 m of RSLR), and RCP8.5 (with 1.77 m of RSLR) for the coastal wetland ecosystems including the Sundarbans mangrove forest, neritic system and aquaculture ponds. In all scenarios, RSLR will drive a loss in the total value of ecosystem services such as provision of raw materials, and food provision, ranging from US$0–1 million to US$ 16.5–20 million, respectively. The outcomes of this study reveal that RSLR-induced inundation on its own, is unlikely to be a major threat to the wetland ecosystems in Western coast of Bangladesh. This would suggest that other climate change impacts such as coastal erosion, increase in frequency of cyclone events, and sea temperature rise might be the likely primary drivers of change in the value of wetland ecosystems services in this area

Vector Distance Function Based Map Representation for Robot Localisation

This paper introduces the use of the vector distance function (VDF) for representing environments, particularly for the use in localisation algorithms. It is shown that VDF has a continuous derivative at the object boundary in contrast to unsigned distance transform, and does not require an environment populated with closed object as in the case of the signed distance transforms, the two most common strategies reported in the literature for representing environments based on distances to nearest occupied regions. As such VDF overcomes the main disadvantages of the existing distance transform based representations in the context of robot localisation. The key properties of VDF are demonstrated and the use of VDF in robot localisation using an optimization based algorithm is illustrated using three examples. It is shown that the proposed environment representation and the localisation algorithm is effective in providing accurate location estimates as well as the associated uncertaintie

Economic Value of Climate Variability Impacts on Coconut Production in Sri Lanka

This paper assesses the economic value of climate variability, employing a percentile analysis on an array of 31-years national annual coconut production data from 1971 to 2001. Of the production array, 10% and 90% percentiles have been considered respectively as lower and upper production extremes. The 60% of production departures of each year of extremes with respect to the mean production of 10% to 90% percentile were attributed to climate variability because studies show that the 60% of the variation of coconut production is explained by climate. These production deviations were then valued multiplying by free-on-board (FOB) prices of fresh coconuts. Results show that the foregone income from coconuts due to low rainfall varied between US$32 million to US$ 73 million while the incremental coconut income in crop glut extremes due to high rainfall varied between US$42 million and US$ 87 million. Results show that the climate variability causes income losses to the economy estimated at US$32 million to US$ 73 million in years of extreme crop shortage. And in years of extreme crop surplus, the economy realises income gains of US$42 million to US$ 87 million. These indicate the potential for significant economic benefits from investments in adaptations that would reduce variability in nut production which is caused by variations in climate. Further work is however needed to estimate the effectiveness and economic benefits that might be achieved from investments in adaptation

Distance function based 6DOF localization for unmanned aerial vehicles in GPS denied environments

© 2017 IEEE. This paper presents an algorithm for localizing an unmanned aerial vehicle (UAV) in GPS denied environments. Localization is performed with respect to a pre-built map of the environment represented using the distance function of a binary mosaic, avoiding the need for extraction and explicit matching of visual features. Edges extracted from images acquired by an on-board camera are projected to the map to compute an error metric that indicates the misalignment between the predicted and true pose of the UAV. A constrained extended Kalman filter (EKF) framework is used to generate an estimate of the full 6-DOF location of the UAV by enforcing the condition that the distance function values are zero when there is no misalignment. Use of an EKF also makes it possible to seamlessly incorporate information from any other system on the UAV, for example, from its auto-pilot, a height sensor or an optical flow sensor. Experiments using a hexarotor UAV both in a simulation environment and in the field are presented to demonstrate the effectiveness of the proposed algorithm