基于强化学习的无人机自主导航CO源定位

IF 2.2 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Pritika Marik;Harshit Kumar Sahu;Chiranjib Ghosh;Amit Ruidas;Soumajit Pramanik;Avishek Adhikary
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引用次数: 0

摘要

在线监测城市和工业地区的一氧化碳(CO)水平可以减少一氧化碳中毒造成的死亡人数。基于无人机的气体传感提供了一个动态的解决方案,通过适当的跟踪快速定位来源。然而,无人机(UAV)具有有限的飞行时间;因此,确保快速跟踪源的优化搜索至关重要。在这封信中,我们提出了一个基于粒子聚类深度q学习的框架,用于无人机气源的自主定位。无人机结构是定制的,以这样一种方式安装MQ-9气体传感器,螺旋桨湍流的影响被最小化。此外,还设计了一种改进的高斯梅模型,用于增强真实数据,提高训练精度。与之前的模型相比,该模型的成功率更高,步长更小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reinforcement Learning-Based Autonomous UAV Navigation for CO Source Localization
Online monitoring of carbon monoxide (CO) levels in urban and industrial areas may reduce the rising death toll from CO poisoning. A UAV-based gas sensing provides a dynamic solution to this problem by quickly locating the source through proper tracking. However, unmanned aerial vehicle (UAV) has a limited flight time; thus, an optimized search ensuring fast tracking of the source is crucial. In this letter, we propose a particle clustering deep Q-learning-based framework for autonomous localization of gas source using a UAV. The UAV structure is customized to mount the gas sensor MQ-9 in such a way that the effect of propeller turbulence is minimized. Besides, a modified Gaussian plum model is designed for augmenting real data for more accurate training. A comparison with the previous model highlights the higher success rate and lower step size achieved by this work.
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来源期刊
IEEE Sensors Letters
IEEE Sensors Letters Engineering-Electrical and Electronic Engineering
CiteScore
3.50
自引率
7.10%
发文量
194
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