Mission Design for Unmanned Aerial Vehicles using Hybrid Probabilistic Logic Programs

TL;DR

This paper introduces ProMis, a probabilistic mission design system for UAVs in Advanced Air Mobility, integrating legal, spatial, and perception data using Hybrid Probabilistic Logic Programs to ensure safe, robust navigation.

Contribution

ProMis is the first system to formalize UAV mission planning by combining probabilistic logic with legal and spatial data sources for autonomous decision-making.

Findings

ProMis effectively integrates diverse data sources for UAV mission validation.

Experiments demonstrate ProMis's applicability across various scenarios.

The system manages computational complexity in probabilistic mission planning.

Abstract

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.