文献类型：期刊文献

英文题名：Online Hand Gesture Detection and Recognition for UAV Motion Planning

作者：Lu, Cong[1];Zhang, Haoyang[2,3];Pei, Yu[2,3];Xie, Liang[2,3];Yan, Ye[2,3];Yin, Erwei[2,3];Jin, Jing[4,5]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Acad Mil Sci, Natl Innovat Inst Def Technol, Beijing 100071, Peoples R China;[3]Tianjin Artificial Intelligence Innovat Ctr, Tianjin 300450, Peoples R China;[4]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[5]East China Univ Sci & Technol, Shenzhen Res Inst, Shenzhen 518063, Peoples R China

年份：2023

卷号：11

期号：2

外文期刊名：MACHINES

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000940928600001)】；

语种：英文

外文关键词：IMU data glove; hand gesture detection; hand gesture recognition; UAV motion planning; interaction efficiency

摘要：Recent advances in hand gesture recognition have produced more natural and intuitive methods of controlling unmanned aerial vehicles (UAVs). However, in unknown and cluttered environments, UAV motion planning requires the assistance of hand gesture interaction in complex flight tasks, which remains a significant challenge. In this paper, a novel framework based on hand gesture interaction is proposed, to support efficient and robust UAV flight. A cascading structure, which includes Gaussian Native Bayes (GNB) and Random Forest (RF), was designed, to classify hand gestures based on the Six Degrees of Freedom (6DoF) inertial measurement units (IMUs) of the data glove. The hand gestures were mapped onto UAV's flight commands, which corresponded to the direction of the UAV flight.The experimental results, which tested the 10 evaluated hand gestures, revealed the high accuracy of online hand gesture recognition under asynchronous detection (92%), and relatively low latency for interaction (average recognition time of 7.5 ms; average total time of 3 s).The average time of the UAV's complex flight task was about 8 s shorter than that of the synchronous hand gesture detection and recognition. The proposed framework was validated as efficient and robust, with extensive benchmark comparisons in various complex real-world environments.