In most keyhole operations, the surgeon requires human assistance in tasks ranging from holding additional tools (e.g. suction instruments), to help in retracting tissues and assisting in suturing. Workforce shortages can translate into prolonged or more difficult operations, postponement of elective surgeries, and excessive working hours. Recent advancements in artificial intelligence (AI) make robots reliable assistants in many human-robot collaborative scenarios outside of medicine, but their potential is yet to be fully explored in robot-assisted surgery.

This project aims to develop a smart work allocation framework for human-robot collaboration in robot-assisted surgery.

In high-rise construction, the current practice in on-site installation of prefabricated exterior wall modules is unsafe, inefficient, and requires considerable manual handling. Control of the modules with a crane alone is difficult as this non-linear system is highly underactuated.

This research asks how to improve the control precision and situational awareness of the crane operator, to improve the speed and safety in this process. Current progress indicates that this can be achieved by development of a system that augments crane control and information feedback to the crane operator.

Today, recognising the abilities and benefits of robots, they are increasingly considered for applications requiring a higher level of intelligence such as replacing humans for providing service to customers in retail stores and hotels. Service robots are required to work in unconstrained, human-centred environments where the user commonly expects some level of social interaction with the service provider.

This project aims to study the role of different factors in customer’s preference to interact with a robot in the service sector. Results will cast light on the suitable design and interaction strategies to increase acceptance of robots as service providers.

Australian football is among the most popular sports in this country. According to the AFL Annual Report (2017), there has been more than 1.5 million participation in 2017, up 10.7% on the 2016 total. This covers all age groups starting at five years of age through to Masters competition. The early detection of concussions in younger age groups is of high importance to reduce the risk of abnormalities in a still growing brain. Identification of players at risk of concussion in non-helmeted sports such as Australian football, is based on subjective observation by support staff and/or video footage review post-game. Objective head impact data enables fast and accurate detection of such risks, for early diagnosis and management of concussion and timely interventions for both professional and amature players.

This project develops a compact, self-adhesive wearable device for use by non-helmeted players, which records accelerometry data and uses AI to identify risky scenarios.