A Dual-Mode Medium Access Control Mechanism for UAV-Enabled Intelligent Transportation System

Khan, Muhammad Asghar; Cheema, Tanweer Ahmad; Ullah, Insaf; Noor, Fazal; Rehman, Sajjad Ur; Rehman, Ateeq Ur; Aziz, Muhammad Adnan

doi:10.1155/2021/5578490

Cited by 5 publications

(1 citation statement)

References 22 publications

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“…Unmanned Aerial Vehicles (UAVs) have been widely used in many applications such as package delivery [1,2], disaster rescue [3][4][5], industrial facilities inspection [6], automated maintenance [7], and entertainment. Using UAVs for these applications could be beneficial and more useful if they demonstrate autonomous behavior; thus, reducing the need for humans to be involved in potentially hazardous situations.…”

Section: Introductionmentioning

confidence: 99%

UAV Dynamic Object Tracking with Lightweight Deep Vision Reinforcement Learning

Nguyen

Thudumu

et al. 2023

Algorithms

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Several approaches have applied Deep Reinforcement Learning (DRL) to Unmanned Aerial Vehicles (UAVs) to do autonomous object tracking. These methods, however, are resource intensive and require prior knowledge of the environment, making them difficult to use in real-world applications. In this paper, we propose a Lightweight Deep Vision Reinforcement Learning (LDVRL) framework for dynamic object tracking that uses the camera as the only input source. Our framework employs several techniques such as stacks of frames, segmentation maps from the simulation, and depth images to reduce the overall computational cost. We conducted the experiment with a non-sparse Deep Q-Network (DQN) (value-based) and a Deep Deterministic Policy Gradient (DDPG) (actor-critic) to test the adaptability of our framework with different methods and identify which DRL method is the most suitable for this task. In the end, a DQN is chosen for several reasons. Firstly, a DQN has fewer networks than a DDPG, hence reducing the computational resources on physical UAVs. Secondly, it is surprising that although a DQN is smaller in model size than a DDPG, it still performs better in this specific task. Finally, a DQN is very practical for this task due to the ability to operate in continuous state space. Using a high-fidelity simulation environment, our proposed approach is verified to be effective.

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