基于数字孪生的无人机视觉导航系统

Jingsi Miao, Ping Zhang
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引用次数: 0

摘要

近年来,许多学者从各个方面对无人机数字孪生进行了研究。但是,目前的研究还处于初级阶段,还存在数据与模型融合不完整、算法策略迁移性差、虚拟空间与物理空间关联性差、应用场景扩展性不足等问题。为了探索数字孪生技术在无人机领域的应用潜力,将数字孪生技术引入无人机单目视觉导航中。为此,本文提出了一种结合深度神经网络的基于数字孪生的框架,该框架由物理空间、虚拟空间、孪生数据层和应用层组成。其次,构建应用层感知模型和控制模型的解耦多模态决策模型,探索全局最优解并控制无人机的行为;最后,分别在虚拟空间和物理空间对数字孪生系统和决策模型进行了验证。结果表明,基于数字孪生的无人机视觉导航系统降低了应用、算法开发和部署成本,提高了导航策略的迁移能力。与基线相比,本文提出的决策模型在虚拟空间和物理空间均具有最佳的导航性能。与未采用解耦方法的导航策略相比,该策略在虚拟空间的性能指标提高了约8.6%,在物理空间的性能指标提高了2.7倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UAV Visual Navigation System based on Digital Twin
In recent years, many scholars have carried out researchs on UAV digital twin from various aspects. However, the research is still in the preliminary stage, and there are still some problems, such as incomplete data and model fusion, poor migration of algorithm policy, poor relation between virtual and physical space, and lack of extensibility of application scenarios. In order to explore the application potential of digital twin technology in UAV fields, this paper introduces digital twin into UAV monocular visual navigation. Therefore, this paper proposes a digital twin(DT)-based framework integrating with deep neural network, which consists of physical space, virtual space, twin data layer and application layer. Next, the multi-modal decision model with decoupling methods in application layer consisting of perception model and control model is built to explore the global optimal solution and control the behaviors of UAV. Finally, the digital twin system and decision model are verified in virtual space and physical space respectively. The results shows that the UAV visual navigation system based on digital twin reduces the cost of application, algorithm development and deployment, and improves the migration ability of navigation policy. Compared with the baselines, the proposed decision model has the best navigation performance in both virtual space and physical space. Compared with the navigation policy without the decoupling method, the performance index is improved by about 8.6% in virtual space and 2.7 times in physical space.
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