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Four-Dimensional Trajectory Generation for UAVs Based on Multi-Agent Q Learning

Published online by Cambridge University Press:  12 February 2020

Wenjie Zhao ,
Zhou Fang  and
Zuqiang Yang
Wenjie Zhao
Affiliation:
(School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, Zhejiang Province, China)
Zhou Fang
Affiliation:
(School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, Zhejiang Province, China)
Zuqiang Yang*
Affiliation:
(Information Science Academy of China Electronics Technology Group Corporation, Beijing, China)
*
(E-mail: gaayzq@126.com)
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Abstract

A distributed four-dimensional (4D) trajectory generation method based on multi-agent Q learning is presented for multiple unmanned aerial vehicles (UAVs). Based on this method, each vehicle can intelligently generate collision-free 4D trajectories for time-constrained cooperative flight tasks. For a single UAV, the 4D trajectory is generated by the bionic improved tau gravity guidance strategy, which can synchronously guide the position and velocity to the desired values at the arrival time. Furthermore, to optimise trajectory parameters, the continuous state and action wire fitting neural network Q (WFNNQ) learning method is applied. For multi-UAV applications, the learning is organised by the win or learn fast-policy hill climbing (WoLF-PHC) algorithm. Dynamic simulation results show that the proposed method can efficiently provide 4D trajectories for the multi-UAV system in challenging simultaneous arrival tasks, and the fully trained method can be used in similar trajectory generation scenarios.

Keywords

AlgorithmFlightRoute PlanningUnmanned Aerial System (UAS)
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 4 , July 2020 , pp. 874 - 891
Copyright
Copyright © The Royal Institute of Navigation 2020

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