Student topics

AI that is pre-programmed is limited in its tasks and human bias. Learning systems offer richer decision-making behaviors where collaborative projects have led to the following three systems that require integration:A symbolic learning system that can continually learn Boolean classification problems as they are presented to it. But this needs to be extended to real-valued, noisy and uncertain classification problems.A lateralized system that can consider an input at the constituent level and the holistic level simultaneously, which enables flexible and …

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

Estimating the motion of a landing deck, and controlling the descent of a drone under severe weather events is a challenging task. We have developed a simulation environment to test control and prediction algorithms that could allow a drone to safely land on a ship. This PhD program involves the investigation of innovative predictive control approaches closely linked with predictors that provide T secs ahead the future position of the landing deck.

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

Cyberphysical systems (CPS) integrate sensors, communication networks, controllers, dynamic processes and actuators. CPS play an increasingly important role in modern society, in areas such as energy, transportation, manufacturing, healthcare. Due to the interplay between control systems, communications and computations, the design of CPS requires novel approaches, which bridge disciplinary boundaries.This PhD project will develop engineering science and methods for the analysis and design of CPS operating in closed loop. Your research will bring together elements of control systems engineering, as …

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

Many critical infrastructure systems are operated using networked feedback control. These systems crucially use wireless networks to transmit sensor and actuation signals. Unfortunately, wireless technology (sensors, actuators and communications) is unreliable and increasingly vulnerable to cyberattacks. This causes performance degradation, loss of stability, system failure and, at worst, leads to deaths and disasters. Therefore, mitigating the effects of attack algorithms on Cyberphysical Systems (CPSs) is of utmost importance.A distinguishing aspect, when compared to attacks on classical information systems, is that …

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

Control of connected vehicle platoons can ensure the swift movement of traffic through a city by sharing vehicles' states and desired actuation. This networked control design can alleviate traffic jams, reduce vehicle emissions, and reduce fuel usage through improved aerodynamics. Model Predictive Control algorithms are a natural solution to address constraints arising from both communications and system dynamics. A key challenge is to design distributed control algorithms that are robust to disturbances in the environment and to stochastic information from …

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

Managing multiple robotic systems simultaneously poses many challenges around coordination and control. This is particularly true in environments where there's a lack of accurate localisation, sensing uncertainty and limited communications, yet there is an overarching mission objective or series of tasks that need to be completed.In this project, you will explore and develop approaches around multi-robot swarming and coordinated formation control for dynamic process monitoring, target tracking and coordinated mapping. There will be a particular focus on underwater and surface …

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

Unmanned Traffic Management (UTM) describes a set of systems, services and procedures that will be developed to manage drone (unmanned aircraft systems/unmanned aerial vehicle/remotely piloted aircraft) operations in and around our cities. From surveillance tasks and package delivery through to passenger transport, UTM will be essentially in ensuring safe and efficient use of our airspace. Essentially, UTM is a new air traffic control system for drones with high levels of automation and advanced decision making and control. This research aims …

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

This project concerns computer vision and statistical analysis of performance in professional level matches in the game of squash.The goal is to use computer vision and existing systems to capture and analyse patterns of play, allowing coaches and professional players to develop strategies to improve performance, to counter particular types of play and even to tailor game plans to attack individual opponents.

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Computer Science

Research centre(s)

Centre for Data Science

The aim of this research is to develop a framework for multiple Unmanned Aerial Vehicles (UAV), that balances information sharing, exploration, localization, mapping, and other planning objectives thus allowing a team of UAVs to navigate in complex environments in time critical situations. This project expects to generate new knowledge in UAV navigation using an innovative approach by combining Simultaneous Localization and Mapping (SLAM) algorithms with Partially Observable Markov Decision Processes (POMDP) and Deep Reinforcement learning. This should provide significant benefits, …

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

This PhD project aims to develop a framework for unmanned aerial vehicles (UAV), which optimally balances localisation, mapping and other objectives in order to solve sequential decision tasks under map and pose uncertainty. This project expects to generate new knowledge in UAV navigation using an innovative approach by combining simultaneous localisation and mapping algorithms with partially observable markov decision processes. The project’s expected outcomes will enable UAVs to solve multiple objectives under map and pose uncertainty in GPS-denied environments. This …

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

Future robotics systems are likely to benefit from having an ability to self-diagnose self-failure or the presence of anomalous situations (so that they can switch to fallback or fail-safe modes). Example situations include subtle sensor or actuator failure and cyber security or physical intruder detection.Such low signal-to-noise anomaly detection or self-diagnose problems can be understood using powerful mathematical and statistical tools which QCR has a rich history of advancing through collaboration with industry partners and publication in premium international venues.

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

Data is recognised a strategic asset for organisations. There is a growing need to manage the voluminous data an organisation is exposed to in order to use it for decision-making.Of particular significance is process data, which consists of information about the execution of processes. Such information is used to uncover behaviour of processes within an organisation. This brings forth the significance of data governance. Data governance is the exercise of control and authority over management of data. Despite its significance, …

Study level

PhD, Master of Philosophy, Honours

Faculty

Faculty of Science

School

School of Information Systems