Autonomous rerouting flight path planning using Gaussian-mixture-based artificial potential field method

EasyChair Preprint 1467

Abstract

As air transportation traffic increases, Free Flight has been considered a possible solution for future challenges in air traffic management (ATM). Autonomous navigation and guidance system can be used for a free flight of a UAV or assist a pilot in path planning to avoid flight into an unsafe area or collision with other aircrafts (or UAVs) in dense air traffic environments. To implement the collision avoidance and guidance function, we use a Gaussian-mixture-based artificial potential field method. In this paper, we introduce the gaussian-mixture based APF method which improves traditional APF problem and can be applied to air traffic routing scenarios. This APF method can be easily extended to air traffic modeling such as weather condition, traffic density, Special Use Airspace (SUA) as well as path planning for collision avoidance. This indicates that the APF approach can be applied effectively in the field of civil aviation air traffic management. The proposed collision avoidance algorithm generates a path for multi-UAVs, with each UAV considering of the other UAVs as obstacles. We also apply the developed algorithm to a possible scenario and demonstrate its performance through simulation using multicopter-type UAVs and obstacles.

Keyphrases: Air Traffic Management, Gaussian Mixture Model, artificial potential field, path planning

@booklet{EasyChair:1467,
  author    = {Jihyun Mok and Seungchan Shin and Jaeho Shin and Sangho Ko},
  title     = {Autonomous rerouting flight path planning using  Gaussian-mixture-based artificial potential field method},
  howpublished = {EasyChair Preprint 1467},
  year      = {EasyChair, 2019}}