Uas conflict resolution integrating a risk-based operational safety bound as airspace reservation with reinforcement learning

Jueming Hu, Yongming Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

UAS trajectory prediction is stochastic in nature and randomness exists in almost every aspect of UAS Traffic Management. In order to address this challenge, it is critical to ensure a reasonable separation between UAS and obstacles when doing path planning and conflict resolution. In this paper, a novel method to deconflict for rotary-wing UAS traffic management is proposed. The main idea is to integrate a probabilistic dynamic anisotropic operational safety bound as airspace reservation with reinforcement learning method. The operational safety bound is based on UAS performance, weather condition and uncertainties in UAS operations, such as positioning error. Based on the operational safety bound concept, a new reward function in reinforcement learning is developed. The proposed methodology results in a trajectory prediction model under risk-based dynamic separation criterion. The algorithm of Q learning is adopted to find the optimal path planning. Simulations of avoiding static obstacles and multi-UAS conflict resolution are conducted to show the deconflict capability. Comparisons between results with operational safety bound and without operational safety bound are shown and analyzed.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2020 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Pages1-10
Number of pages10
ISBN (Print)9781624105951
DOIs
StatePublished - 2020
EventAIAA Scitech Forum, 2020 - Orlando, United States
Duration: Jan 6 2020Jan 10 2020

Publication series

NameAIAA Scitech 2020 Forum
Volume1 PartF

Conference

ConferenceAIAA Scitech Forum, 2020
CountryUnited States
CityOrlando
Period1/6/201/10/20

ASJC Scopus subject areas

  • Aerospace Engineering

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