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Advances in intelligent and autonomous navigation systems for small USA

机译：小美国智能和自主导航系统的进展

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A significant growth in Unmanned Aircraft System (UAS) operations has been observed over the past decade, largely driven by the emergence of new commercial opportunities and use-cases. This has posed new techno-logical and regulatory challenges in order to address the complex safety, efficiency and sustainability re-quirements associated with UAS operations in an increasingly congested airspace. The growing need for trusted autonomy in UAS operations imposes demanding performance requirements on Navigation and Guidance Sys-tems (NGS), both in terms of accuracy, integrity, continuity and availability. In most current NGS imple-mentations, system autonomy is tightly constrained within a specified set of operational and environmental conditions through a large number of explicit rules. Recent breakthroughs in Artificial Intelligence (AI)-based methods and the emergence of highly-parallelized processor boards with low form-factor has led to the oppor-tunity to employ Machine Learning (ML) techniques to enhance navigation system performance, particularly for small UAS (sUAS), which account for the majority of current and future unmanned aircraft use-cases. sUAS navigation systems typically employ diverse low Size, Weight, Power and Cost (SWaP-C) sensors such as Global Navigation Satellite System (GNSS) receivers, MEMS-IMUs, magnetometers, cameras and Lidars for localization, obstacle detection and avoidance. This paper presents a comprehensive review of conventional sUAS navigation systems, including aspects such as system architecture, sensing modalities and data-fusion algorithms. Addi-tionally, performance monitoring and augmentation strategies are critically reviewed and assessed against current and future UAS Traffic Management (UTM) requirements. The primary focus is on the identification of key gaps in the literature where the use of AI-based methods can potentially enhance navigation performance. A critical review of AI-based methods and their application to sUAS navigation is conducted, along with an assessment of the performance benefits they provide over conventional navigation systems. Reviewed methods include but are not restricted to Artificial Neural Networks (ANN) such as Convolutional and Recurrent Neural Networks (CNN and RNN), Support Vector Machines (SVM) and ensemble techniques. The key challenges associated with adapting these methods to address sUAS operational objectives are clearly identified. The review also covers the assurance of predictable, deterministic system behaviour which is a key requirement to support system certification. The review and analysis will inform the reader of the applicability of various AI/ML methods to sUAS navigation and autonomous system integrity monitoring, and its likely role in the ongoing UTM evolution.
机译：过去十年来观察到无人机系统（UAS）业务的显着增长，主要是由于新的商业机会和使用情况的出现而导致。这已经提出了新的技术逻辑和监管挑战，以解决与越来越拥挤的空域中的UAS操作相关的复杂安全，效率和可持续性重新询问。在UAS操作中越来越需要对可信的自主权对准确，完整性，连续性和可用性的导航和指导系统（NGS）提出了对导航和指导系统的要求。在最新的NGS IMPLE-决策中，系统自主权通过大量的明确规则在指定的操作和环境条件下严格限制。最近在人工智能（AI）的方法中的突破和具有低形式因子的高于缓和化处理器板的出现导致了造型机制，采用机器学习（ML）技术来增强导航系统性能，特别是对于小UA （苏斯），占大多数当前和未来无人驾驶飞机使用情况的案例。 SUAS导航系统通常采用多样化的低尺寸，重量，功率和成本（SWAP-C）传感器，例如全球导航卫星系统（GNSS）接收器，MEMS-IMU，磁力计，摄像机和LIDAR，用于定位，障碍物检测和避免。本文对传统的SUAS导航系统进行了全面的审查，包括系统架构，传感方式和数据融合算法等方面。可添加性，性能监测和增强策略受到严格审查和评估的，以防止当前和未来的UAS交通管理（UTM）要求。主要重点是在文献中识别关键间隙，其中使用基于AI的方法可能会提高导航性能。对基于AI的方法及其在SUAS导航的应用程序的关键审查，并评估了他们提供传统导航系统的性能效益。审查的方法包括但不限于人工神经网络（ANN），例如卷积和经常性神经网络（CNN和RNN），支持向量机（SVM）和集合技术。明确识别与调整这些方法以解决SUAS运营目标相关的关键挑战。审查还涵盖了可预测，确定性系统行为的保证，这是支持系统认证的关键要求。审查和分析将向读者通知各种AI / ML方法对苏斯导航和自主系统完整性监测的适用性，以及其在持续的UTM演变中的作用。

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