Modeling method for simulation scene oriented to intelligent capability test of unmanned aerial vehicles

Shuoye Han; Zhiqiang Fan; Xiaokai Xia; Hao Shen; Jinke Li

doi:10.1117/12.3049915

11 December 2024 Modeling method for simulation scene oriented to intelligent capability test of unmanned aerial vehicles

Shuoye Han, Zhiqiang Fan, Xiaokai Xia, Hao Shen, Jinke Li

Author Affiliations +

Proceedings Volume 13441, International Conference on Cloud Computing and Communication Engineering (CCCE 2024); 1344103 (2024) https://doi.org/10.1117/12.3049915
Event: International Conference on Cloud Computing and Communication Engineering (CCCE 2024), 2024, Nanjing, China

Abstract

With the rapid advancement of drone technology, unmanned aerial vehicles (UAVs) have found widespread application in military domains. Ensuring the optimal performance of UAVs in military applications necessitates the assessment of their intelligent capabilities. Traditional live-flight test methods for UAVs pose inherent challenges such as high risk, substantial costs, and low efficiency. Consequently, research into intelligent level test methodologies based on simulated scene holds significant importance. To address the modeling requirements for UAV intelligent level test scene, this paper proposes a modeling approach founded on the Meta Object Facility (MOF) framework for constructing scene model tailored to evaluating UAV intelligence. Concurrently, this paper establishes a complexity model for test scene based on the Analytical Hierarchy Process (AHP), and employs this model to perform a graded assessment of the UAV's intelligence level according to the complexity of the simulated scene.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuoye Han, Zhiqiang Fan, Xiaokai Xia, Hao Shen, and Jinke Li "Modeling method for simulation scene oriented to intelligent capability test of unmanned aerial vehicles", Proc. SPIE 13441, International Conference on Cloud Computing and Communication Engineering (CCCE 2024), 1344103 (11 December 2024); https://doi.org/10.1117/12.3049915

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