A Smart Flight Controller based on Reinforcement Learning for Unmanned Aerial Vehicle (UAV)

Khan, Fawad Salam; Mohd, Mohd Norzali Haji; Larik, Raja Masood; Khan, Muhammad Danial; Abbasi, Muhammad Inam; Bagchi, Susama

doi:10.1109/icsipa52582.2021.9576806

Cited by 1 publication

(1 citation statement)

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“…Proportional Integral Derivates (PID) and fuzzy controllers help the aviation industry to design these technologies but with certain limitation such as professional knowledge for control, electronic noise from the remote controllers, sensor-based collision avoidance, etc. The hardware design mentioned in [2] utilizes the Inertial Measurement Unit (IMU) with the sensors for yaw, pitch and roll to provide the values to formulate the reward functions for the decision to mobilize the UAV. The best reward is estimated where after each episode a reset function is defined to learn the best path during training.…”

Section: Introductionmentioning

confidence: 99%

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

Khan¹,

Mohd²,

Zulkifli³

et al. 2022

Computers, Materials &Amp; Continua

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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%.

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Section: Introductionmentioning

confidence: 99%