Multi-UAV Systems

The project will investigate the development of machine learning algorithms that will support human machine teams. These teams involve humans working alongside software agents or robots through a broad range of collaborative and competitive relationships. At some points humans may be in charge of the team and at others, agents or robots may be in charge. As previously shown in our studies, such forms of flexible autonomy require machines to be proactive and exhibit learning abilities in order to adapt their responses to the varying requirements of the individual team members and particularly the human members of the team. The challenges involved in designing such algorithms include (i) dealing with sparse datasets (ii) infering human preferences from limited inputs and (iii) learning over time and in different situations.

The project will look at developing a survey of machine learning techniques used in human-machine interaction, with a focus on re-inforcement learning techniques and human-machine interface design for learning systems. Following this, we will develop methodologies to design human-machine teaming algorithms and learning algorithms.

The evaluation of the algorithms will be done on real data with two possible scenarios: (i) assisted living systems where humans rely on autonomous IoT platforms to monitor family members with dementia and (ii) multi-UAV systems that rely on a minimum set of pilots in very dynamic disaster response settings.

This project fits within the university's focus on AI and Autonomous Systems. In particular, the supervisor will be collaborating with colleagues across ECS and Engineering to develop testbeds and evaluate the algorithms designed.

The project also perfectly aligns with the newly created Centre for Machine Intelligence directed by the supervisor.