Student topics

The QUT Centre for Robotics is working with the Australian Space Agency on the newly established Australian space program, in which robots will play a key role. There are multiple PhD projects available to work on different aspect of developing a new Lunar Rover (and later Mars Rover) and in particular its intelligence and autonomy. Future rovers will not only need to conduct exploration and science missions as famous rovers such as NASA's Curiosity or Perseverance are doing right now …

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

Stable correspondence-free image-based visual servoing is a challenging and important problem.In classical image-based visual controllers, explicit feature correspondence (matching) to some desired arrangement (configuration) is required before a control input is obtained. Instead, this project will investigate variable feature correspondence and robust feature selection to simultaneously solve visual servoing problem, removing any feature tracking requirement or additional image processing.Also involving Prof Jason Ford.Example of recent past work

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

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

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

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

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

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

Making a robot understand what it sees is one of the most fascinating goals in our current research. To this end, we develop novel methods for Semantic Mapping and Semantic SLAM by combining object detection with simultaneous localisation and mapping (SLAM) techniques.We work on novel approaches to SLAM that create semantically meaningful maps by combining geometric and semantic information. Such semantically enriched maps will help robots understand our complex world and will ultimately increase the range and sophistication of interactions …

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

How can robots best learn to navigate in challenging environments and execute complex tasks, such as tidying up an apartment or assist humans in their everyday domestic chores?Often, hand-written architectures are based on complicated state machines that become intractable to design and maintain with growing task complexity. I am interested in developing learning-based approaches that are effective and efficient and scale better to complicated tasks.Especially learning based on semantic information (such as extracted by the research in semantic SLAM above), …

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

In recent years the field of robotic navigation has increasingly harnessed semantic information in order to facilitate the planning and execution of robotic tasks. The use of semantic information focuses on employing representations more understandable by humans to accomplish tasks with robustness against environmental change, limiting memory requirements and improving scalability. Contemporary computer vision algorithms extracting semantic information have continuously improved their performance on benchmark datasets, however, most computations are expensive, limiting their use for robotic platforms constrained by size, …

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics

One of the remaining challenges to achieve off-road autonomous navigation for mobile robots is the accurate evaluation of vegetated environments, to determine where a robot can safely drive through. To achieve this, robots may use extra sensory modalities compared to humans, such as RADARs that can penetrate through vegetation and see behind it what is not visible to the naked eye. Another option is to physically interact with the environment to 'clear the way'.

Study level

PhD

Faculty

Faculty of Engineering

School

School of Electrical Engineering and Robotics