The autonomy of embedded systems, in particular robots, comes from their capability to plan their next actions. Although, it becomes critical to ensure the safety of the behaviour as the systems are exposed to human interaction more and more (eg. Autonomous cars, toy UAVs, cobots in manufacturing and so on).
The goal of the thesis is to study task planning under constraints: select the best sequence of actions while optimising several criteria like efficiency, safety and other domain specifics. The thesis involves two main axes, the first is to study how to model the systems constraints in a manner that can be understood both by the human experts and the planning algorithm (eg. Using Operational Design Domain or Dynamic Assurance Case to evaluate system’s safety). Ontologies and knowledge graphs would probably be adequate to model the constraints. The model would benefit from their expressivity and the already-existing tooling. The second main axis is the improvement of the planning algorithm to leverage those models. Those models shall have a generic structure since it is necessary to represent many natures of constraints: safety, efficiency/cost, social “confort”, shared resources on the critical path, type and quantity of interactions between the agents, geometric feasibility, ...
As the thesis is aimed at robotic autonomous systems, it will be important to demonstrate and evaluate the system on real-world use cases.