Collaborative UAVs Multi-task Video Processing Optimization Based on Enhanced Distributed Actor-Critic Networks
Ziqi Rong, Qiushi Zheng, Zhishu Shen, Xiaolong Li, Tiehua Zhang, Zheng, Lei, Jiong Jin

TL;DR
This paper presents CoUAV-Pro, a distributed UAV framework utilizing enhanced actor-critic networks for multi-task video processing, effectively optimizing task allocation in resource-limited, infrastructure-less environments.
Contribution
It introduces a novel distributed task allocation algorithm based on enhanced actor-critic networks for UAVs, improving efficiency without centralized control.
Findings
Achieves comparable task acquisition rates to centralized methods.
Reduces task latency and energy consumption.
Demonstrates effectiveness in resource-constrained environments.
Abstract
With the rapid advancement of the Internet of Things (IoT) and Artificial Intelligence (AI), intelligent information services are being increasingly integrated across various sectors, including healthcare, industry, and transportation. Traditional solutions rely on centralized cloud processing, which encounters considerable challenges in fulfilling the Quality of Service (QoS) requirements of Computer Vision (CV) tasks generated in the resource-constrained infrastructure-less environments. In this paper, we introduce a distributed framework called CoUAV-Pro for multi-task video processing powered by Unmanned Aerial Vehicles (UAVs). This framework empowers multiple UAVs to meet the service demands of various computer vision (CV) tasks in infrastructure-less environments, thereby eliminating the need for centralized processing. Specifically, we develop a novel task allocation algorithm…
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Taxonomy
TopicsDistributed Control Multi-Agent Systems · Advanced Vision and Imaging · Reinforcement Learning in Robotics
