Advances in intelligent and autonomous navigation systems for small UAS

Abstract

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 technological and regulatory challenges in order to address the complex safety, efficiency and sustainability requirements 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 Systems (NGS), both in terms of accuracy, integrity, continuity and availability. In most current NGS implementations, 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 opportunity 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. Additionally, 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.