Adaptive Multi-Robot Coordination in Multi-Human Settings

Adaptive multi-robot systems have the potential to transform how robots operate in complex human environments such as care homes, hospitals, and public spaces. This PhD will explore how multiple heterogeneous robots (for example, service, social, and aerial robots) can coordinate their activities in dynamic settings where many humans are moving, interacting, and changing their needs over time. The focus will be on developing adaptive coordination strategies that can balance multiple objectives – safety, efficiency, social acceptability, and user comfort – while responding robustly to uncertainty, incomplete information, and changing human behaviour. 

The project will combine methods from multi-robot planning, machine learning (including reinforcement or multi-objective learning), and human-robot interaction. Depending on your interests, this may include designing new algorithms for task allocation and navigation in crowded spaces, learning context-aware behaviours from data, and building models that predict or respond to human activities and preferences. There will be opportunities to validate your work both in simulation and on real robot platforms, and to collaborate with domain experts (e.g. in healthcare or social care) to design realistic scenarios and evaluation studies. The overall goal is to create coordination mechanisms that allow teams of robots to work smoothly alongside multiple people, in ways that are safe, interpretable, and genuinely useful.