Application of MBSE to model Hierarchical AI Planning problems in HDDL

The recent improvements of hierarchical AI planning open the path to new and exciting applications in different areas of expertise. One domain with daring and complex planning and scheduling problems is the definition of operations for space exploration systems. For this specific application, the Hierarchical Definition Domain Language (HDDL) may be the most suitable AI planning language to be adopted, seeing its similarities to aerospace engineering functional analysis. The work proposed in this paper contributes to filling the gap between space operations engineers and the AI planning potentialities to solve planning and scheduling problems applied to space exploration systems. The problem and domain files typical of HDDL and PDDL can be built up from the formalism of SysML, a general-purpose architecture modelling language for System Engineering, and MBSE. The designers would be guided through a workflow that will aid them to simplify the translation from MBSE, or SySML, to HDDL. The workflow presented in this paper was applied and tested during an analogue space robotic mission, where a collaborative drone and a rover explore an unknown environment. The final aim of the method is to transfer the "human knowledge" in the planning problem and showing the capabilities of MBSE applied to Knowledge Engineering (KE) of AI planning problems.