Data-driven offline reinforcement learning approach for quadrotor's motion and path planning  

作　　者：Haoran ZHAO Hang FU Fan YANG Che QU Yaoming ZHOU 

机构地区：[1]School of Aeronautic Science and Engineering,Beihang University,Beijing 100191,China [2]Beijing Advanced Discipline Center for Unmanned Aircraft System,Beihang University,Beijing 100191,China [3]Tianmushan Laboratory,Hangzhou 311115,China

出　　处：《Chinese Journal of Aeronautics》2024年第11期386-397,共12页中国航空学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.52272382);the Aeronautical Science Foundation of China(No.20200017051001);the Fundamental Research Funds for the Central Universities,China。

摘　　要：Non-learning based motion and path planning of an Unmanned Aerial Vehicle(UAV)is faced with low computation efficiency,mapping memory occupation and local optimization problems.This article investigates the challenge of quadrotor control using offline reinforcement learning.By establishing a data-driven learning paradigm that operates without real-environment interaction,the proposed workflow offers a safer approach than traditional reinforcement learning,making it particularly suited for UAV control in industrial scenarios.The introduced algorithm evaluates dataset uncertainty and employs a pessimistic estimation to foster offline deep reinforcement learning.Experiments highlight the algorithm's superiority over traditional online reinforcement learning methods,especially when learning from offline datasets.Furthermore,the article emphasizes the importance of a more general behavior policy.In evaluations,the trained policy demonstrated versatility by adeptly navigating diverse obstacles,underscoring its real-world applicability.

关 键 词：Motion planning Unmanned aerial vehicle Reinforcement learning Data-driven learning Markov decision process 

分 类 号：V279[航空宇航科学与技术—飞行器设计]