QUT offers a diverse range of student topics for Honours, Masters and PhD study. Search to find a topic that interests you or propose your own research topic to a prospective QUT supervisor. You may also ask a prospective supervisor to help you identify or refine a research topic.
Found 660 matching student topics
Displaying 133–144 of 660 results
Robot learning for navigation, interaction, and complex tasks
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
Off-road mobile manipulation
This project will investigate how mobile manipulators can operate and interact in natural environments like rainforests, grassland, shrubland, farmland, or desert ecosystems. This research project would explore how to control a continuous track or quadruped mobile manipulator in outdoor natural environments with many obstacles and constraints.Is holistic mobile manipulation possible with uneven terrain? As a mobile robot traverses rough ground, the terrain difference will cause feedback in the end-effector position.The mobile manipulator must overcome obstacle challenges, i.e. traverse around a …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Semantic SLAM for robotic scene understanding, geometric-semantic representations for infrastructure monitoring and maintenance
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
Continual learning system
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
Drone ship landing under adverse sea condition
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
Estimation and control of networked cyberphysical systems
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
Towards resilient cyberphysical systems
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
Model predictive control of connected vehicle platoons
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
Coordinated control of multi-robot systems for dynamic task execution
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
Robust feature selection and correspondence for visual control of robots
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
Cobot contact tasks through multi-sensory deep learning
Contact tasks like grinding, polishing and assembly require a robot to physically interact with both rigid and flexible objects. Current methods relying on force control have difficulty achieving consistent finishing results and lack robustness in dealing with non-linear dynamics inherent in how the material is handled. This project will take a new approach that detects and diagnoses the dynamical process through deep learning fusion of multi-sensory data, including force/tactile, visual, thermal, sound, and acoustic emission; and generate corrective process parameters …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Manipulation in nature
Grasping/manipulating in rainforest, farmland or shrubland environments pose a new challenge for existing techniques. This project will investigate how they manipulate where there may be tree branches, foliage, or other natural obstacles in the way.This project would explore existing and emerging techniques such as optimisation, neural radiance fields (NeRFs), deep learning and reinforcement learning and equipment, including depth cameras, LiDAR, event cameras, and light field cameras.Does the manipulator have to re-arrange the environment to manipulate the point of interest?Or does …
- Study level
- PhD
- Faculty
- Faculty of Engineering
- School
- School of Electrical Engineering and Robotics
Contact us
If you have questions about the best options for you, the application process, your research topic, finding a supervisor or anything else, get in touch with us today.