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deep Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Control Using 3d hand gestures

Computers, Materials & Continua 2022年第9期72卷 5741-5759页

作者： Fawad Salam Khan Mohd Norzali Haji Mohd Saiful Azrin B.M.Zulkifli Ghulam E Mustafa Abro Suhail Kazi dur Muhammad Soomro Faculty of Electrical and Electronics(FKEE) Universiti Tun Hussein Onn MalaysiaParit Raja81756Malaysia department of Electrical&Electronic Engineering Universiti Teknologi PETRONASSeri Iskandar32610Malaysia Faculty of Engineering Science and Technology Isra UniversityHyderabad71000Pakistan department of Innovation CONVSYS(Pvt)Ltd.44000IslamabadPakistan

The evident change in the design of the autopilot system produced massive help for the aviation industry and it required frequent *** learning delivers appropriate outcomes when considering a continuous environment where the controlling Unmanned Aerial Vehicle(UAV)required maximum *** this paper,we designed a hybrid framework,which is based on Reinforcement Learning and deep Learning where the traditional electronic flight controller is replaced by using 3d hand *** algorithm is designed to take the input from 3d hand gestures and integrate with the deep deterministic Policy Gradient(ddPG)to receive the best reward and take actions according to 3d hand gestures *** UAV consist of a Jetson Nano embedded testbed,Global Positioning System(GPS)sensor module,and Intel depth *** collision avoidance system based on the polar mask segmentation technique detects the obstacles and decides the best path according to the designed reward *** analysis of the results has been observed providing best accuracy and computational time using novel design framework when compared with traditional Proportional Integral derivatives(PId)flight *** are six reward functions estimated for 2500,5000,7500,and 10000 episodes of training,which have been normalized between 0 to−*** best observation has been captured on 2500 episodes where the rewards are calculated for maximum *** achieved training accuracy of polar mask segmentation for collision avoidance is 86.36%.

关键词： deep reinforcement learning UAV 3d hand gestures obstacle detection polar mask

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