Intelligent navigation systems for autonomous vehicle operations in urban environments
Creators: name: SURESH BIJJAHALLI, Suraj
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 400103
subject: Aircraft performance and flight control systems
classificationCode: 400104
subject: Avionics
classificationCode: 400105
subject: Flight dynamics

description: The current airspace is in a state of transition with regards to the integration of Unmanned Aircraft System (UAS) operations. These operations have largely been segregated from manned aircraft up to this point. However, this is likely to change as more commercial applications and use-cases for UAS emerge. In response, the UAS Traffic Management (UTM) concept, a cooperative network which is complementary to Air Traffic Control (ATC) has emerged as a means to safely coordinate operations. The UTM evolution is structured into four distinct Technical Capability Levels (TCLs) characterized by successive progression from Visual Line Of Sight (VLOS) operations in scarcely populated areas to Beyond Visual Line Of Sight (BVLOS) operations in urban areas. The progression of the project is characterized by increasing scenario complexity and required autonomous capabilities. TCL4 is characterized by complex operations in densely populated urban areas. Apart from unmanned aircraft, the emergence of on-demand Urban Air Mobility (UAM) will also place increasing demands on the airspace by enabling highly automated, cooperative, passenger transport in urban areas. The realization of this concept is supported by the development of Distributed Electric Propulsion (DEP) and electric Vertical Take-Off and Landing (eVTOL) aircraft. Prototypes for multi-mode transport vehicles, i.e., vehicles that can operate in aerial and terrestrial modes, are also emerging. The safe integration of these agents in a shared, unsegregated airspace is contingent on the availability of reliable, high-performance Communication, Navigation and Surveillance (CNS) infrastructure to support safe separation from terrain and other aircraft. This thesis is specifically concerned with high-performance navigation to support operations in dense urban environments. High-performance navigation supports TCL4 functions such as Beyond Line of Sight (BLOS) command and control, large-scale contingency management, in-flight deconfliction and trajectory conformance monitoring. Within the automotive domain, there is a similar requirement for high-performance navigation with the advent of autonomous driving. Integrity, which is a measure of trust or reliability of the solution is one of the key performance measures of navigation systems owing to its Safety of Life (SoL) implications. In this thesis, an integrity-driven navigation system architecture is developed, which is generally applicable to aerial and ground vehicles. Ground vehicles are included within the scope of the work owing to the similarity of their operational domain with urban UAS, particularly with regard to navigation performance. For both aerial and terrestrial vehicles, positioning in urban environments is degraded owing to poor Global Navigation Satellite System (GNSS) performance. Therefore both use-cases require augmentation and/or alternative means of ensuring navigation performance. The developed system architecture comprises sensors that are well suited to low Size, Weight, Power and Cost (SWaP-C) platforms. This includes traditional sensing modalities such as Global Navigation Satellite System (GNSS) receivers and Inertial Navigation Systems (INS). Additionally, the growing maturity of computer vision algorithms presents the opportunity to include a Vision-Based Navigation (VBN) system to enhance system performance. Further, the state-estimation is aided by the use of a Vehicle Dynamics Model (VDM), which is essentially a knowledge-based module serving as a virtual 'sensor'. Both the VBN and VDM improve system performance and overall availability in GNSS-degraded conditions which are representative of urban environments. The system is divided into different modes or combinations of sensors. A methodology for propagating the overall system integrity requirements to sub-system and mode level is presented, along with conversion of the integrity requirements to mode-specific Protection Levels (PL). Additionally, a mode-selection framework based on Boolean Decision logic is presented, allowing the system to switch between modes based on pre-programmed logic to maintain system integrity. Sensor integration and data-fusion algorithms are presented for both an aerial and a ground vehicle. The integrity-driven architecture is demonstrated in two separate simulation case studies.

Owing to the widespread use of GNSS in most navigation system architectures, it is necessary to model its error characteristics to predict navigation performance over for the mission. Within urban environments, signal multipath is the dominant source of pseudorange error. Therefore, it is necessary to model its impact on positioning performance. Standardized multipath models for aviation are not intended for urban scenarios. Therefore, a multipath and Non-Line Of Sight (NLOS) model based on ray tracing is introduced to support accurate error budgeting in low-altitude urban airspace. The model accounts for signal reflections from the ground, building facades, and the airframe of the aerial vehicle. The model requires GNSS satellite ephemerii and urban terrain data as inputs. Additionally, receiver characteristics can also be accounted for in the model. The output is an upper bound on the positioning error, which accounts for the effects of signal reflections and satellite geometry over the intended operational area. The output can be stored as a Spatio-temporal grid map by a UAS Service Supplier (USS) to assess the expected navigation performance of user-requested trajectories. In order to balance safety and efficiency, aviation authorities are adopting a risk-based approach in assessing the integration of UAS operations in the existing airspace. Collision risk is one of the key components of the overall risk, and navigation performance in turn significantly contributes to collision risk. Owing to the highly dynamic nature of UTM operations, there is a need for a risk assessment methodology which is conservative, and quick to compute. The methodology must support the explicit evaluation of risk contribution from different systems involved in a collision scenario. A novel risk management framework is introduced, and specifically, a methodology to model UAS collision risk is presented. The model explicitly accounts for navigation performance and additionally, is flexible to accommodate surveillance and communication performance as well. Communication, Navigation and Surveillance (CNS) performance factors are converted to equivalent spatial bounds which form a virtual protection volume around the aircraft. Evaluation of collision risk has traditionally been challenging and computationally expensive owing to the requirement to evaluate multiple integrals numerically. A methodology to quickly and conservatively approximate it through a closed-form expression is developed. Since the navigation performance is explicitly included in the collision risk evaluation, its contribution to risk is readily assessed. Additionally, trade-offs against communication and surveillance performance or separation standards can be evaluated. Threshold values for navigation performance including Alert Limits can then be derived analytically derived from an overarching Target Level of Safety (TLS). The model applicability to strategic offline scenarios is demonstrated through two verification case studies involving aircraft on intersecting and parallel routes.