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Dialogue Possibilities between a Human Supervisor and UAM Air Traffic Management: Route Alteration

Jeongseok Kim, Kangjin Kim

TL;DR

The paper tackles detour management for Urban Air Traffic Management (UATM) in a heterogeneous, data-sharing constrained environment. It adopts Answer Set Programming (ASP) with non-monotonic reasoning to formalize detour decisions and uses a two-phase dialogue between a human supervisor and the UATM system. Its contributions include a detour scenario, a theoretical enhancement to the method, and an additional inquiry for another scenario, all demonstrated on scenarios involving corridor rerouting and clearing. The results show satisfiable detour plans and provide an explainable reasoning trail via explicit ASP rules, indicating a robust, scalable approach to coordinating multiple UATM agents in dynamic urban airspace.

Abstract

This paper introduces a novel approach to detour management in Urban Air Traffic Management (UATM) using knowledge representation and reasoning. It aims to understand the complexities and requirements of UAM detours, enabling a method that quickly identifies safe and efficient routes in a carefully sampled environment. This method implemented in Answer Set Programming uses non-monotonic reasoning and a two-phase conversation between a human manager and the UATM system, considering factors like safety and potential impacts. The robustness and efficacy of the proposed method were validated through several queries from two simulation scenarios, contributing to the symbiosis of human knowledge and advanced AI techniques. The paper provides an introduction, citing relevant studies, problem formulation, solution, discussions, and concluding comments.

Dialogue Possibilities between a Human Supervisor and UAM Air Traffic Management: Route Alteration

TL;DR

The paper tackles detour management for Urban Air Traffic Management (UATM) in a heterogeneous, data-sharing constrained environment. It adopts Answer Set Programming (ASP) with non-monotonic reasoning to formalize detour decisions and uses a two-phase dialogue between a human supervisor and the UATM system. Its contributions include a detour scenario, a theoretical enhancement to the method, and an additional inquiry for another scenario, all demonstrated on scenarios involving corridor rerouting and clearing. The results show satisfiable detour plans and provide an explainable reasoning trail via explicit ASP rules, indicating a robust, scalable approach to coordinating multiple UATM agents in dynamic urban airspace.

Abstract

This paper introduces a novel approach to detour management in Urban Air Traffic Management (UATM) using knowledge representation and reasoning. It aims to understand the complexities and requirements of UAM detours, enabling a method that quickly identifies safe and efficient routes in a carefully sampled environment. This method implemented in Answer Set Programming uses non-monotonic reasoning and a two-phase conversation between a human manager and the UATM system, considering factors like safety and potential impacts. The robustness and efficacy of the proposed method were validated through several queries from two simulation scenarios, contributing to the symbiosis of human knowledge and advanced AI techniques. The paper provides an introduction, citing relevant studies, problem formulation, solution, discussions, and concluding comments.
Paper Structure (24 sections, 1 theorem, 2 figures)

This paper contains 24 sections, 1 theorem, 2 figures.

Table of Contents

  1. Introduction
  2. Related Works
  3. Problem Formulation
  4. Scenario 1: Reroute the Corridor
  5. Scenario 2: Clearing the Corridor
  6. Solution
  7. A Common Setting
  8. Basic Queries
  9. Find all covered agents by $UATM_1$
  10. Change the route for covered, $vp_3$ heading agents
  11. Advanced Queries
  12. Find all the unreachable with $UATM_1$, but $vp_3$ heading agents
  13. Change the route for all $vp_3$ heading agents
  14. Append a round detour for agents ahead of agent 7
  15. Discussion
  16. ...and 9 more sections

Key Result

Proposition 1

Every answer returned from ASP is valid.

Figures (2)

  • Figure 1: UATM Network, which consists of four vertiports, $vp_1$, $vp_2$, $vp_3$, and $vp_7$, bidirectionally connecting corridors between adjacent vertiports, three UATMs of $UATM_1$, $UATM_2$, and $UATM_3$, and their coverage represented with outer circles.
  • Figure 2: Re-route corridor scenario among human manager, UATMs, and agents

Theorems & Definitions (2)

  • Proposition 1
  • proof