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Tree climbing robot for stealthy surveillance

Obtaining accurate biodiversity data is important for rainforest conservation. This requires long-term visual and acoustic data sets in parts of the environment that are often hard to reach.This project involves developing robotic systems to conduct stealthy surveillance (visual and acoustic) in forest tree canopies for assessing biodiversity.A key requirement for these systems is that they're small and nimble enough to climb into the cluttered tree canopies. These systems should be able to wait for hours or days whilst recording data …

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

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

Surgery robot

Significant advances have been made in surgical assistance robots, but significant challenges remain.This project will develop new systems for surgical assistance robots, including the potential for advanced surgical localization and positioning systems.This project is part of a major Australian Research Council Industrial Transformation Training Centre for Joint Biomechanics.

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)

Visual place recognition for robots and autonomous vehicles

Knowing where you are is a fundamental capability for robots and autonomous vehicles moving through an environment.Increasingly, technologies in this area are based around camera sensors, performing a process known as Visual Place Recognition (VPR).VPR becomes challenging in the real-world when conditions or viewpoints change: for example in conditions of inclement weather, changing seasons or day-night cycles.Robots and autonomous vehicles must also be able to localise across viewpoint changes: for example driving down the same road but in the opposite …

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)

Neuroscience-based algorithms for robotics and autonomous vehicles

Increasingly, researchers are looking to neuroscience and the brain for inspiration in how they design their deep learning network architectures for robotics and artificial intelligence tasks.This project will design innovative new neural network-based learning approaches to make breakthroughs in robotic and autonomous vehicle capabilities in:sensingscene understandingvisual place recognitionnavigationSimultaneous Localisation And Mapping (SLAM)other key capabilities.In doing so you will gain invaluable experience and skills in one of the most exciting fields in the world today, along with opportunities for international travel …

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)

Performance monitoring of deep learning models for robotic perception

This project breaks the current 'weak' assumption in the literature that the performance of deep learning models reported on a holdout dataset is an indicator of the performance on all future and yet to be encountered conditions during deployment. In reality, performance fluctuates and can drop below critical thresholds when the robot travels through particular places, times and conditions.

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

Lifelong semantic mapping of large scale environments

When building a map of objects inside a real environment, this map can become quickly outdated as objects are moved around over time. This is especially true when the map is on the scale of a house, warehouse or a city.This PhD project investigates new methods for keeping a high-resolution semantic map up-to-date using partial, and possibly low-resolution, snapshots. These environment snapshots can be captured by sensors mounted on moving agents, such as vehicles and mobile robots. …

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

Visual memory summarisation for life-long mobile service robots operation in everyday environments

This research project aims to answer the question: How can video summarisation methods be used for efficient review and data storage of visual sensory data captured during a life-long operation of mobile service robot?Video summarisation refers to the process of generating a summary that best conveys the most informative content of a longer video.

Study level
PhD, 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)

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)

Soft and self-powered robotic skin

The sense of touch is one of the most important ways to:discover the environmentdifferentiate soft and hard surfacesoperate delicate objects.Our research group has successfully fabricated a new class of soft tactile sensors that combines the electroluminescence and photo-sensing modes of multifunctional diodes with a flexible elastomer capable of creating intelligent skin. Using this approach, we now aim to integrate this technology with modern medical, social and industrial robots.In this project you'll investigate the development of a flexible and stretchable smart …

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

Greenhouse robotic platform

This research project focuses on robotic grasping and agricultural robotics. The project aim is to design and develop of a robotic base platform for a greenhouse environment that can autonomously navigate down crop rows and capture important information about crop health.

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

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