Algorithm and Semi-physical System Simulation for Command Intent Recognition of UAV in Low-resource Environment

doi:10.16182/j.issn1004731x.joss.24-0566

Abstract

Abstract:

When a communication network is partially disabled or disrupted, an UAV is plunged into a "low-resource environment" and must rely on local hardware resources. This situation imposes constraints on computing power, storage capacity, and energy availability. To address the need for command intent recognition in such environments, a semi-physical simulation system for UAV in emergency rescue operations has been designed and implemented. Based on the low resource airborne hardware in the loop, the system simulates UAV command intention recognition and mission planning through GIS+BIM 3D environment modeling task scenarios. A new lightweight algorithm for intent recognition has been proposed, based on the joint attention fusion mechanism of global sentence structure information extraction using GraphSAGE and local semantic features of FastText, which optimizes and improves the accuracy and response speed of intent understanding prediction. On the constructed professional UAV command intent dataset, semi-physical simulation verifies that the accuracy of command intention recognition is 0.890 7 and the time is 58.808 ms, which meets the real-time requirement.

Key words: UAV, low-resource environments, command intent recognition, text classification, lightweight algorithm, semi-physical simulation

CLC Number:

TP391

Liu Hongfu, Fu Yajing, Zhang Wanpeng, Zhang Hu. Algorithm and Semi-physical System Simulation for Command Intent Recognition of UAV in Low-resource Environment[J]. Journal of System Simulation, 2024, 36(12): 2894-2905.

Figures/Tables 12

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算法	A	F₁	R
TextCNN	0.875 2	0.873 9	0.875 0
TextRNN	0.863 5	0.855 6	0.862 1
TextRCNN	0.875 4	0.875 3	0.874 8
DPCNN	0.872 5	0.872 0	0.870 0
BERT	0.897 2	0.888 2	0.887 0
ERNIE	0.881 0	0.877 7	0.874 3
本文	0.890 7	0.889 0	0.889 0

算法	A	F₁	R
本文	0.890 7	0.889 0	0.889 0
Ⅰ	0.673 8	0.680 3	0.680 0
Ⅱ	0.867 2	0.863 5	0.863 5
Ⅲ	0.881 5	0.878 6	0.880 0
Ⅳ	0.793 4	0.800 7	0.793 0

算法	训练时间/min	预测时间/ms
TextCNN	21.317	507.239
TextRNN	43.617	1 042.942
TextRCNN	26.100	684.258
DPCNN	33.217	671.357
BERT	158.233	13 608.010
ERNIE	187.875	13 151.264
本文模型	29.633	58.808

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