Towards Probabilistic Clearance, Explanation and Optimization

TL;DR

This paper advances UAS safety and legal compliance by integrating probabilistic mission planning with a clearance, explanation, and optimization cycle, demonstrated through navigation scenarios using crowd-sourced data.

Contribution

It introduces the ProMis framework for probabilistic navigation and the CEO cycle for safety and legal compliance in UAS missions, combining domain knowledge and constraints.

Findings

Navigation within ProMis is feasible using probabilistic landscapes.

The CEO cycle effectively guides mission design respecting safety constraints.

Application demonstrated with crowd-sourced map data and synthetic scenarios.

Abstract

Employing Unmanned Aircraft Systems (UAS) beyond visual line of sight (BVLOS) is an endearing and challenging task. While UAS have the potential to significantly enhance today's logistics and emergency response capabilities, unmanned flying objects above the heads of unprotected pedestrians induce similarly significant safety risks. In this work, we make strides towards improved safety and legal compliance in applying UAS in two ways. First, we demonstrate navigation within the Probabilistic Mission Design (ProMis) framework. To this end, our approach translates Probabilistic Mission Landscapes (PML) into a navigation graph and derives a cost from the probability of complying with all underlying constraints. Second, we introduce the clearance, explanation, and optimization (CEO) cycle on top of ProMis by leveraging the declaratively encoded domain knowledge, legal requirements, and safety assertions to guide the mission design process. Based on inaccurate, crowd-sourced map data and a synthetic scenario, we illustrate the application and utility of our methods in UAS navigation.