• Follow a user-centred approach to co-design collaborative robot simulation scenarios within the healthcare context
• Design and prototype social navigation techniques to optimise human-robot collaboration in healthcare settings
• Develop and deploy social navigation algorithms integrating contextual information based on quantitative (e.g., sensor data) and qualitative data (e.g., user’s intentions and experiences) in healthcare scenarios
• Evaluate social navigation for human-robot interaction in healthcare context
The role of emerging technologies driving healthcare innovation to enhance healthcare efficiency is estimated to create £350 billion in technology-driven value by 2025 [Singhal, McKinsey 2020]. The smart healthcare of the future will combine data, software and algorithms to personalise healthcare delivery. Robots supporting healthcare staff would also play a role as part of a growing digital ecosystem of connected devices, which aims to improve patients' care experiences [Bo Chen, McKinsey 2020]. While robots were mostly found in manufacturing settings, we now foresee a future where humans will increasingly interact with robots in different areas of society. Such scenarios raise new questions about everyday interactions with robots under complex and uncertain environments, expectations and deployment challenges for safe and comfortable human-robot interactions.
Robots have the potential to support healthcare workers in medical environments, thus relieving clinical staff on routinely duties and hospital services, while also allowing healthcare workers to better distribute their time to patients. Some examples of such support capabilities include robots that assist surgeons through instrument handling, or robots that can conduct intra-hospital transport, as well as other repetitive tasks, which could increase the efficiency in many healthcare scenarios.
By taking a human-centred design approach, this project aims to identify human robot interaction techniques to provide contextualised social navigation strategies to improve mobile robots’ algorithms in healthcare service environments.
The overall objective is to contribute to the development of everyday robotics in healthcare, exploring what kind of task can be supported by robots and learning from a dynamic healthcare environment to enhance robots to provide appropriate social navigation.
This research will address the following research questions:
1. Which are the key aspects (social navigation strategies, cues, preferences) for human-robot interaction that shape the dynamics of collaborative tasks in healthcare environments
2. How to improve social navigation algorithms informed by qualitative measurements?
3. What kind of metrics for user experience and social acceptance emerge from Social
Individual Index(SII) and Social Group Index (SGI) in HRI?
This work will involve interdisciplinary work, conducting lab-based experiments, simulations and ethnographic studies in healthcare, human-robot interaction, robotics and social navigation.
Academic criteria: A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Applicants with appropriate professional experience are also considered. Degree-level mathematics (or equivalent) is required for research in some project areas.
Applicants for whom English is not their first language must demonstrate proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component.
How to apply:
Please contact the supervisors of the project prior to submitting your application to discuss and develop an individual research proposal that builds on the information provided in this advert. Once you have developed the proposal with support from the supervisors, please submit your application following the instructions provided below
This project is accepting applications all year round, for self-funded candidates via https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/computer-science-and-informatics
In order to be considered candidates must submit the following information:
- Supporting statement
- In the ‘Research Proposal’ section of the application enter the name of the project you are applying to and upload your Individual research proposal, as mentioned above in BOLD
- Qualification certificates and Transcripts
- Proof of Funding. For example, a letter of intent from your sponsor or confirmation of self-funded status (In the funding field of your application, insert Self-Funded)
- References x 2
- Proof of English language (if applicable)
For more information about this project, please contact Dr Carolina Fuentes Toro, [Email Address Removed]
If you have any questions or need more information, please contact [Email Address Removed]