Mission Design for Unmanned Aerial Vehicles using Hybrid Probabilistic Logic Program
<|reference_start|>Mission Design for Unmanned Aerial Vehicles using Hybrid Probabilistic Logic Program: 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 representation 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.<|reference_end|>
@article{kohaut2024mission,
    title={Mission Design for Unmanned Aerial Vehicles using Hybrid Probabilistic
  Logic Programs},
    author={Simon Kohaut and Benedict Flade and Devendra Singh Dhami and Julian
  Eggert and Kristian Kersting},
    journal={arXiv preprint arXiv:2406.03454},
    year={2024},
    doi={10.1109/ITSC57777.2023.10422083},
    archivePrefix={arXiv},
    eprint={2406.03454},
    primaryClass={cs.RO}
}