Deep Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Control Using 3D Hand Gestures

The evident change in the design of the autopilot system produced massive help for the aviation industry and it required frequent upgrades.Reinforcement learning delivers appropriate outcomes when considering a continuous environment where the controlling Unmanned Aerial Vehicle(UAV)required maximum accuracy.In 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 gestures.The 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 input.The UAV consist of a Jetson Nano embedded testbed,Global Positioning System(GPS)sensor module,and Intel depth camera.The collision avoidance system based on the polar mask segmentation technique detects the obstacles and decides the best path according to the designed reward function.The 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 controller.There are six reward functions estimated for 2500,5000,7500,and 10000 episodes of training,which have been normalized between 0 to−4000.The best observation has been captured on 2500 episodes where the rewards are calculated for maximum value.The achieved training accuracy of polar mask segmentation for collision avoidance is 86.36%.

Hand gesture tracking algorithm based on visual attention

In the majority of the interaction process, the operator often focuses on the tracked 3D hand gesture model at the ＂interaction points＂ in the collision detectionscene, such as ＂grasp＂ and ＂release＂ and objects in the scene, without paying attention to the tracked 3D hand gesture model in the total procedure. Thus in this paper, a visual attention distribution model of operator in the ＂grasp＂, ＂translation＂, ＂release＂ and other basic operation procedures is first studied and a 3D hand gesture tracking algorithm based on this distribution model is proposed. Utilizing the algorithm, in the period with a low degree of visual attention, a pre-stored 3D hand gesture animation can be used to directly visualise a 3D hand gesture model in the interactive scene; in the time period with a high degree of visual attention, an existing ＂frame-by-frame tracking＂ approach can be adopted to obtain a 3D gesture model. The results demonstrate that the proposed method can achieve real-time tracking of 3D hand gestures with an effective improvement on the efficiency, fluency, and availability of 3D hand gesture interaction.