The Duel

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Eurig Scandrett (ed) (2020) Public Sociology as Educational Practice: Challenges, Dialogues and Counter-Publics

Public Sociology as Educational Practice frames, critically engages with and seeksto further the theory and practice of public sociology as popularised by publicsociologist, Michael Burawoy. Public sociology is about taking sociology outsidethe university, and using it to support ‘publics’ or communities to generateknowledge about society and how it can be changed. To readers of a journal aboutcommunity education this aim will likely sound familiar; the explicit and implicitlinks between arguments for public sociology and the dialogical pedagogy of PauloFreire are clear throughout the book

Autonomous terminal area operations for unmanned aerial systems

After many years of successful operation in military domains, Unmanned Aerial Systems (UASs) are generating significant interest amongst civilian operators in sectors such as law enforcement, search and rescue, aerial photography and mapping. To maximise the benefits brought by UASs to sectors such as these, a high level of autonomy is desirable to reduce the need for highly skilled operators. Highly autonomous UASs require a high level of situation awareness in order to make appropriate decisions. This is of particular importance to civilian UASs where transparency and equivalence of operation to current manned aircraft is a requirement, particularly in the terminal area immediately surrounding an airfield. This thesis presents an artificial situation awareness system for an autonomous UAS capable of comprehending both the current continuous and discrete states of traffic vehicles. This estimate forms the basis of the projection element of situation awareness, predicting the future states of traffic. Projection is subject to a large degree of uncertainty in both continuous state variables and in the execution of intent information by the pilot. Both of these sources of uncertainty are captured to fully quantify the future positions of traffic. Based upon the projection of future traffic positions a self separation system is designed which allows an UAS to quantify its separation to traffic vehicles up to some future time and manoeuvre appropriately to minimise the potential for conflict. A high fidelity simulation environment has been developed to test the performance of the artificial situation awareness and self separation system. The system has demonstrated good performance under all situations, with an equivalent level of safety to that of a human pilot

Statistical Process Control in Medical Surveillance. An Application Using Spirometry

This research effort concentrated on applying statistical process control techniques to the results of seven years worth of spirometry exams of workers at Wright-Patterson AFB in helping the occupational health squadron identify potential work hazard areas. Each spirometry exam was classified as abnormal or normal based on a comparison with established normals or a significant loss of function from the previous year\u27s exam for the individual. Each test was classified into the work area of the individual and the number of abnormalities per population of the work area was examined

Lateral control of vehicle platoons with on-board sensing and Inter-Vehicle Communication

This paper presents a lateral control strategy for a platoon of vehicles which utilises only data which can realistically be measured by each vehicle, augmented with Inter-Vehicle Communication (IVC). The control problem resembles those which exist for longitudinal control and this introduces the challenge of estimating a vehicles lateral position and velocity when direct measurement is not possible (due to lane markings being obscured by a preceding vehicle). It is shown that the associated robust controller, which we propose, exhibits string stability in the presence of sensor and actuation delays and a high fidelity simulation is conducted to verify this

Symbiotic relationship between robots - a ROS ARDrone/YouBot library

A Symbiotic relationship between robots is theoretically developed. It is characterised by sharing sensory information and tightly coordinating operational logic by taking care of each other’s needs during missions. The system is characterised by an intertwined reasoning system while having separate conditioning and execution of plans to achieve subgoals to support each other. The results are illustrated on strong operational inter-dependence of a rover and a drone through shared logical inference. The drone uses the rover as a landing pad and the rover uses the drone to complements its sensor system by information gathering. There is a GitHub library provided in association with the demonstration for generic use of adding cameras and cooperation logic to a AR.Drone 2.0 and a KUKA youBot system. The benefits of symbiotic relationship are quantitatively evaluated on the demonstration example

A model based design framework for safety verification of a semi-autonomous inspection drone

In this paper, we present a model based design approach to the development of a semi-autonomous control system for an inspection drone. The system is tasked with maintaining a set distance from the target being inspected and a constant relative pose, allowing the operator to manoeuvre the drone around the target with ease. It is essential that the robustness of the autonomous behaviour be thoroughly verified prior to actual implementation, as this will involve the flight of a large multi-rotor drone in close proximity to a solid structure. By utilising the Robotic Operating System to communicate between the autonomous controller and the drone, the same Simulink model can be used for numerical coverage testing, high fidelity simulation, offboard execution and final executable deploymen

Towards artificial situation awareness by autonomous vehicles

This paper presents a novel approach to artificial situation awareness for an autonomous vehicle operating in complex dynamic environments populated by other agents. A key aspect of situation awareness is the use of mental models to predict future states of the environment, allowing safe and rational routing decisions to be made. We present a technique for predicting future discrete state transitions (such as the commencement of a turn) by other agents, based upon an uncertain mental model. Predictions take the form of univariate Gaussian Probability Density Functions which capture the inherent uncertainty in transition time whilst still providing great benefit to a decision making system. The prediction distributions are compared with Monte Carlo simulations and show an excellent correlation over long prediction horizons