Performance Evaluation Model of Human-Machine Interactions for UAV Cluster Attack

Chen, Anqi; Bai, Wei; Xie, Feng

doi:10.1007/978-981-99-0479-2_76

Cited by 2 publications

(1 citation statement)

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“…To test the capability of the model, based on the design paradigm and research foundation [31,32], a typical UAV cluster ground attack scenario was established using the Qt graphical user interface development software. The design of the HCI interface and the definition of the IEEs are shown in Figure 3.…”

Section: Simulation Environmentmentioning

confidence: 99%

Intelligent Optimization Method of Human–Computer Interaction Interface for UAV Cluster Attack Mission

Chen,

Xie,

Wang

et al. 2023

Electronics

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In modern warfare, it is often necessary for the operator to control the UAV cluster from a ground control station to perform an attack task. However, the absence of an effective method for optimizing the human–computer interface in ground control stations for UAV clusters leads to usability difficulties and heightens the probability of human errors. Hence, we propose an optimization framework for human–computer interaction interfaces within UAV ground control stations, rooted in interface-essential elements. Specifically, the interface evaluation model was formulated by combining the Salient, Effort, Expectancy, and Value (SEEV) framework with the essential factor mutation cost of the quantified interface. We employed the SEEV–ant colony algorithm to address the challenge of optimizing the interface design within this context. For a typical UAV cluster attack mission, we optimized the human–computer interaction interfaces of the three mission stages based on the proposed SEEV-AC model. We conducted extensive simulation experiments in these optimized interfaces, and used eye-movement indicators to evaluate the effectiveness of the interface optimization model. Based on the experimental results, divergence is reduced by 11.59%, and the fitness of the optimized interface is increased from 1.34 to 3.42. The results show that the proposed intelligent interface optimization method can effectively improve the interface design and reduce the operator’s workload.

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Section: Simulation Environmentmentioning

confidence: 99%