The absence of sunlight during the winter in the High Arctic results in a strong surface-based atmospheric temperature inversion, especially during clear skies and light surface wind conditions. The achieved training accuracy of polar mask segmentation for collision avoidance is 86.36%. The best observation has been captured on 2500 episodes where the rewards are calculated for maximum value. There are six reward functions estimated for 2500, 5000, 7500, and 10000 episodes of training, which have been normalized between 0 to-4000. 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.
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 UAV consist of a Jetson Nano embedded testbed, Global Positioning System (GPS) sensor module, and Intel depth camera. 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. 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. Reinforcement learning delivers appropriate outcomes when considering a continuous environment where the controlling Unmanned Aerial Vehicle (UAV) required maximum accuracy. The evident change in the design of the autopilot system produced massive help for the aviation industry and it required frequent upgrades. Finally, this review identifies the research gap and presents future research directions regarding UAVs. In particular, UAV applications, challenges, and security issues are explored in the light of recent research studies and development. In this review, a comprehensive study on UAVs, swarms, types, classification, charging, and standardization is presented. This study highlights the importance of drones, goals and functionality problems. Intuitively it is not suggested to load them with heavy batteries. Other critical concerns are battery endurance and the weight of drones, which must be kept low. Despite these potential features, including extensive variety of usage, high maneuverability, and cost-efficiency, drones are still limited in terms of battery endurance, flight autonomy and constrained flight time to perform persistent missions. UAVs offer implicit peculiarities such as increased airborne time and payload capabilities, swift mobility, and access to remote and disaster areas. The drone industry has been getting significant attention as a model of manufacturing, service and delivery convergence, introducing synergy with the coexistence of different emerging domains. Recently, unmanned aerial vehicles (UAVs), also known as drones, have come in a great diversity of several applications such as military, construction, image and video mapping, medical, search and rescue, parcel delivery, hidden area exploration, oil rigs and power line monitoring, precision farming, wireless communication and aerial surveillance.
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