Explore a range of mechatronic engineering research internships to complete as part of your degree during the semester break.
The following internships listed are due to take place across the Summer break.
Application will open on 10th September and close on 30th September 2025.
Supervisor: Dr Don Dansereau
Eligibility:
Project Description:
Digital twins are becoming a critical tool in managing our infrastructure. From solar farms to urban infrastructure, we are working to automate the capture and modelling of how our assets evolve over time. A key challenge in this process is constructing and maintaining these models.
Building on existing work with the ACFR’s small, unmanned ground vehicle Wombat, you will advance techniques for repeated, automated inspection of outdoor assets allowing construction of high-fidelity 3D digital twins that evolve over time.
Depending on aptitude and interest, this could include development of sensor payloads, 3D reconstruction algorithms, interactive visualisation tools, and/or approaches to change detection and modelling. Deployment using the Wombat platform is a key part of this program.
Requirement to be on campus: Yes *dependent on government’s health advice.
Supervisor: Dr Don Dansereau
Eligibility:
Project Description:
3D modelling of reflective objects like glass buildings is almost impossible. From Google maps to Hollywood visual effects, automated solutions to this problem remain elusive.
We are developing physics-based solutions to this problem, enabling 3D modelling of reflective objects using drone-based imagery. To validate our approaches we will leverage visual effects technologies to build an outdoor drone motion tracking system to provide a source of ground truth for the drone’s pose.
Your task is to design and deploy the outdoor drone motion capture system. Depending on aptitude and interest, this can involve previsualisation for camera and lens selection, infrastructure and logistics for deployment and data capture, post-capture processing pipeline development and deployment, and/or design of an active drone-mounted marker system.
Requirement to be on campus: Yes *dependent on government’s health advice.
Supervisor: Dr Don Dansereau
Eligibility:
Project Description:
What does your robotic vacuum cleaner see, and who else has access to those images? In homes, hospitals, and secure industrial sites, the uptake of autonomous robots is limited by privacy concerns.
Working with researchers at the Australian Centre for Robotics, this project will develop novel sensing technologies to enable robots to visually understand their environments without capturing privacy-revealing images.
Building on a unique prototype technology, you will advance the design of an opto-mechatronic privacy-preserving robotic vision system.
Depending on interest and ability there is scope to focus on optics, analogue electronics, or algorithms to allow an outdoor ground vehicle to drive autonomously using privacy-preserving vision. There is also a scope to quantify privacy by constructing sophisticated attacks that exploit the points of weakness of alternative approaches.
Requirement to be on campus: Yes *dependent on government’s health advice.
Supervisor: Dr Don Dansereau
Eligibility:
Project Description:
Long-term autonomy requires robots to improvise solutions to novel challenging conditions. Resilience to such conditions represents a final frontier in the deployment of truly trustworthy systems.
In this project you will advance a processing architecture inspired by the global workspace model of human cognition. We hypothesise this will enable the sort of metareasoning and problem solving humans use when tackling unprecedented challenges.
Students will have an opportunity to engage with both theoretical and applied aspects of the work, from concept to deployment, including work with a small mobile unmanned ground vehicle.
Requirement to be on campus: Yes *dependent on government’s health advice.
Supervisors: Dr Viorela Ila and A/Prof. Craig Jin
Eligibility: Programming C++, Python
Project Description:
This project explores the conversion of visual information into audio streams to enhance situational awareness for robotic systems. The student will design algorithms that map image features, such as object location and motion, into structured sound cues. The goal is to assist operators or AI agents in perceiving complex environments through multi-modal feedback. Deliverables include a functional prototype translating real-time camera feeds into sound and a preliminary user study evaluating recognition accuracy.
Requirement to be on campus: Yes *dependent on government’s health advice.
Supervisors: Dr Viorela Ila and Dr Yiduo Wang
Eligibility: Programming C++, Python.
Project Description:
This project investigates algorithms for understanding dynamic scenes in environments with moving obstacles. The student will work on real-time object detection and trajectory prediction to improve path-planning performance in cluttered or unpredictable settings. Using simulated datasets and physical robot platforms, the project will assess various motion prediction models and integrate them into a navigation pipeline. Results will provide insights into improving safety and efficiency in industrial and autonomous systems.
Requirement to be on campus: Yes *dependent on government’s health advice.
Supervisors: Dr Viorela Ila and Prof. Ian Manchester
Eligibility: Robotics (ROS), Programming C++, Python.
Project Description:
This project focuses on developing an adaptive control framework for robotic grippers used in flexible manufacturing. The student will investigate lightweight perception-driven control algorithms for handling deformable or delicate objects with high accuracy. Using simulation and experimental setups with collaborative manipulators, the project will evaluate real-time feedback systems to adapt grasp strategies dynamically. Outcomes include a performance benchmark of different algorithms and a proof-of-concept demo showing improved reliability and precision in manufacturing tasks.
Requirement to be on campus: Yes *dependent on government’s health advice.
Supervisors: Dr Viorela Ila and Dr Xiaofeng Wu
Eligibility: Programming C++, Python.
Project Description:
This project focuses on developing algorithms for satellite tracking to support autonomous robotic interventions. The student will work with both simulated orbital datasets and real tracking data collected in the lab to analyse satellite motion, predict trajectories, and design intervention strategies. The project will involve implementing estimation and filtering techniques (e.g., EKF, particle filters) for robust state prediction under noisy conditions. Outcomes include a tested pipeline for real-time tracking and a demonstration using hardware-in-the-loop experiments.
Requirement to be on campus: Yes *dependent on government’s health advice.
Supervisor: Dr Viorela Ila
Eligibility: Programming C++, Python.9
Project Description:
This project aims to process and analyze 3D datasets for underwater robotic grasping. The student will explore techniques for noise filtering, localisation, surface reconstruction, and object segmentation from stereo and acoustic sensor data. The focus will be on building reliable object models for grasp planning in turbid and low-visibility conditions. Deliverables include a processed dataset, a pipeline for feature extraction, and a preliminary grasp-planning algorithm benchmarked on simulation or robotic platforms.
Requirement to be on campus: Yes *dependent on government’s health advice.
Supervisor: Dr Mitch Bryson
Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.
Project Description:
Three-dimensional sensor data (e.g. LiDAR, photogrammetry point clouds) are used extensively to measure complex environments in applications such as infrastructure inspection and environmental management. State-of-the-art approaches to interpreting this data are based on machine learning techniques in which deep neural networks which are trained for performing complex tasks. These techniques perform well in environments for which they are trained but often fail to interpret data in new environments or novel scenarios.
This research project will focus on developing approaches to 3D data analysis in novel/unseen environments based on human-machine teaming. This involves both a human expert and a machine-learnt/AI model working collaboratively, for example through a virtual-reality interface, to analyse complex 3D datasets in new situations. Approaches will be developed including interactive segmentation and active learning which combine the complimentary abilities of a human expert’s adaptability to new scenarios with the precision and throughput offered by machine learning and AI.
Requirement to be on campus: No
Supervisor: Prof Stefan Williams
Eligibility: 2nd - 4th year undergraduate students in Engineering (Mechatronics, Electrical, Software, or related disciplines)
Skills required:
Project Description:
This internship offers a hands-on opportunity to work with advanced robotic platforms and spatial computing tools. The selected student will:
This project is ideal for engineering students with a strong interest in robotics, spatial data, and real-world experimentation.
Requirement to be on campus: Students are expected to spend some time on campus, although there might be some flexibility for some of the simulator development work to be done remotely.
Last updated 26th September 2025.
