Publikation
Mission Design for Unmanned Aerial Vehicles using Hybrid Probabilistic Logic Program
Simon Kohaut; Benedict Flade; Devendra Singh Dhami; Julian Eggert; Kristian Kersting
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2406.03454, Pages 1-8, arXiv, 2024.
Zusammenfassung
Advanced Air Mobility (AAM) is a growing field
that demands a deep understanding of legal, spatial and
temporal concepts in navigation. Hence, any implementation of
AAM is forced to deal with the inherent uncertainties of human-
inhabited spaces. Enabling growth and innovation requires the
creation of a system for safe and robust mission design, i.e.,
the way we formalize intentions and decide their execution as
trajectories for the Unmanned Aerial Vehicle (UAV). Although
legal frameworks have emerged to govern urban air spaces,
their full integration into the decision process of autonomous
agents and operators remains an open task. In this work we
present ProMis, a system architecture for probabilistic mission
design. It links the data available from various static and
dynamic data sources with legal text and operator requirements
by following principles of formal verification and probabilistic
modeling. Hereby, ProMis enables the combination of low-level
perception and high-level rules in AAM to infer validity over the
UAV’s state-space. To this end, we employ Hybrid Probabilistic
Logic Programs (HPLP) as a unifying, intermediate repre-
sentation between perception and action-taking. Furthermore,
we present methods to connect ProMis with crowd-sourced
map data by generating HPLP atoms that represent spatial
relations in a probabilistic fashion. Our claims of the utility
and generality of ProMis are supported by experiments on a
diverse set of scenarios and a discussion of the computational
demands associated with probabilistic missions.