边缘智能驱动的高能效无人机自主导航算法研究

doi:10.11959/j.issn.2096-3750.2021.00229

Abstract

Abstract:

Autonomous drone navigation has received growing attention in the recent community.Compared with traditional navigation approaches which rely on location-based services highly, deep learning based visual methods have showed superior performance in self-adaption and generalization, which are a promising solution for autonomous navigation.Running the resource-hungry deep learning execution in the resource-constrained unmanned aerial vehicle (UAV), however, significant challenges were presented in power efficiency.To tackle this challenge, following the idea of edge intelligence, a deep reinforcement learning approach was introduced to dynamically configure the computational scale of the deep learning model on UAV and hence realize the autonomous navigation with low latency and high energy efficiency.Evaluations based on both simulation and real prototype experiments show that the proposed approach has the less energy consumption, longer navigation trail and higher obstacle avoidance rate.

Key words: UAV, edge intelligence, deep learning, reinforcement learning, autonomous navigation

CLC Number:

TP399

Chunmin LIN, Liekang ZENG, Xu CHEN. Research on power efficient autonomous UAV navigation algorithm: an edge intelligence driven approach[J]. Chinese Journal on Internet of Things, 2021, 5(2): 87-96.

Figures/Tables 13

References 31

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算法	距离/m	成功率	功率/W
静态配置	73.6	89%	6.1
阈值配置	67.2	83%	4.2
强化学习配置	77.6	92%	3.9

CPU	2.00 GHz 四核ARM Cortex-A572.00 GHz 双核Denver 2
内存	1 600 MHz 8 GB LPDDR4
GPU	Pascal架构，256个CUDA核心
	理想状态5
功耗/W	满载状态15
	平均9.5

算法	静态配置	阈值配置	强化学习配置
续航时间/s	821	879	945

Research on power efficient autonomous UAV navigation algorithm: an edge intelligence driven approach

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