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Coordinated control of multi-robot systems for environmental management

Single robotic systems can't accurately measure dynamic processes and map large areas in challenging environments. However, managing multiple robotic systems simultaneously poses many challenges around coordination and control. This is particularly true in environments where there's a lack of communication with the system. This project will explore multi-robot swarming and formation control to monitor, manage and track large-scale environmental phenomena.In this project, you will explore multi-robot swarming and coordinated formation control for dynamic process monitoring, target tracking and coordinated mapping. …

Study level
PhD, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Robotics

Gesture-based control of underwater helper-bots

Underwater robotic systems have been in use for several decades. In recent years, various groups have been adding manipulators and other payloads to increase their utility. However, their role has primarily been monitoring and mapping the oceans without human interaction.The next frontier is to have human divers and robotic system collaborate safely and productively in the same space to jointly complete complex tasks. This will involve the robotic system directly understanding and interacting with the diver in a non-verbal manner. …

Study level
PhD, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Robotics

Deep learning for robotics in open-world conditions

To fully integrate deep learning into robotics, it's important that deep learning systems can reliably estimate the uncertainty in their predictions. This allows robots to treat a deep neural network like any other sensor and use the established Bayesian techniques to fuse the network’s predictions with prior knowledge or other sensor measurements or to accumulate information over time.Deep learning systems typically return scores from their softmax layers that are proportional to the system’s confidence. They are not calibrated probabilities and …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)

Augmented reality (AR) applications for robotic scene understanding

Augmented reality (AR), or mixed reality, has become a mature technology with many possible practical applications in manufacturing, retail, navigation and entertainment.We're interested in using AR to support human-robot interaction. In this project, you'll investigate how a human can use AR to better understand how a robot perceives the world and to understand the robot's intentions.

Study level
Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)

Text analysis in engineering education

Textual data contains a large amount of embedded information. This text information is easily extracted by humans, but is difficult for machines to interpret. Using various textual analysis methods some information can be drawn out from pieces of text. An example includes finding the confidence in student-written answers.

Study level
Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)

3D printing a soft robotic gripper

This research is focused on robotic grasping and agricultural robotics.

Study level
Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)

Degradation-conscious charging of lithium-ion batteries

The adoption of electric vehicles and grid storage systems by the present day consumers has been phenomenal. The most critical issue with the lithium-ion (Li-ion) battery is how to manage its degradation.Understanding battery degradation mechanisms and the development of charging strategies to optimally manage battery degradation is very important. There are a number of important issues that need to be addressed carefully, such as thermo-electrochemical modelling of large battery packs and fast charging protocols.

Study level
PhD, Master of Philosophy
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)

Dynamic wireless transfer systems

There's a growing interest by many governments and industry sectors on dynamic wireless power transfer systems for electric vehicle charging. This means there's increased research intensity in this domain. However, there are numerous important issues that need to be addressed carefully, such as:load estimationsensorless vehicle position estimationrobust controlimproved power converter topologies.

Study level
PhD, Master of Philosophy
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)

Machine learning for wildlife monitoring

This project will investigate methods to monitor wildlife using machine learning applied to aerial imagery.While it's highly desirable to use drones and aerial footage to monitor wildlife, there are substantial challenges created by the nature of the data and target wildlife.This, combined with the vast nature of any collected aerial data, makes manual analysis difficult. This challenge motivates the development of machine learning methods to automatically process data and perform tasks, such as:detecting target animalscounting herd animalsclassifying land useassessing environment …

Study level
Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Data Science

The insufficient informativeness of measurements in Bayesian detection problems

Shiryaev's Bayesian Quickest Change Detection (QCD) problem is to detect a change in the statistical problems of an observed process. This is an important signal processing problem with application in a diverse range of areas, including:automatic controlquality controlstatisticstarget detection.Recently a critical deficiency in Shiryaev's QCD problem has been identified to occur due to the insufficient informativeness of measurement in low signal-to-noise (SNR) to overcome geometric prior assumption on the change event.These deficiencies are due to the non-ergodic nature of the …

Study level
PhD
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)

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:energytransportationmanufacturinghealthcare.Due to the interplay between control systems, communications and computations, the design of CPS requires novel approaches, which bridge disciplinary boundaries.We're interested in developing engineering science and methods for the analysis and design of CPS operating in closed loop. Our research brings together elements of control systems engineering, as well as telecommunications and reinforcement learning.The current …

Study level
PhD, Master of Philosophy, Honours
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)

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