Dr. Roberto Sabatini

An Intelligent Cyber-Physical System for Multi-Domain Traffic Management

Commercial air and space transport operations are on the rise and the integration of conventional Air Traffic Management (ATM) with emerging Unmanned Aircraft System (UAS) Traffic Management (UTM) and Space Traffic Management (STM) operations is becoming an essential factor to support the anticipated growth of the sector. More specifically, the consolidation of these systems into a Multi-Domain Traffic Management (MDTM) network is required to simultaneously address the array of interrelated requirements (e.g., safety, efficiency and sustainability) underpinning the introduction of new long-range, regional and urban transport services. Recent trends in both atmospheric and sub-orbital point-to-point flight operations have extended the airspace usage beyond conventional ATM boundaries, eliciting the adoption of new legal and technical requirements, which are not properly addressed by the existing International Civil Aviation Organization (ICAO) and Committee on Peaceful Uses of Outer Space (COPUOS) regulations. The coexistence of manned and unmanned (remotely piloted/autonomous) vehicles requires an integrated air-and-space traffic management network with much higher levels of automation than present-day ATM, which can be delivered by evolving Cyber-Physical System (CPS) architectures and Artificial Intelligence (AI) technologies. However, the human understanding of AI based automated decision-making processes is vital in order to build trust, enhance human-autonomy teaming, and support the introduction of mission-essential and safety-critical functions in next-generation ATM and avionics systems. A novel methodology for MDTM and associated Intelligent Cyber-Physical Systems (iCPS) are therefore needed to support harmonized operations across the air and space domains. Such new approach should incorporate the current ICAO and COPUOS regulatory framework guidelines and addresses the need for unsegregated operations in uncontrolled airspace above 20 km and below the Karman line (100 Km). Despite the challenges existing at a national and global level, an integrated MDTM framework will give administrators the flexibility to manage traffic in a safe, efficient and sustainable manner, and to implement new policies and regulations/standards that accommodate the needs of both aerospace vehicle manufacturers and service providers (i.e., air-and-space transport operators).

Satellite Resilience and Autonomous Manoeuvring

Both commercial and government entities are increasingly reliant on space-based capabilities including communications; Position, Navigation and Timing (PNT); and Intelligence, Surveillance and Reconnaissance (ISR)/Earth Observation. In this context, resilience is seen as one of the key capabilities. Satellite resilience is affected by a broad range of factors, which collectively contribute to ensuring: a single sensor is working when required; a single satellite can maintain its mission requirements; the constellation as a whole is operationally ready and able; and the operator has the mission specific capabilities available exactly when needed. The focus of this project is on the physical resilience of Low Earth Orbit (LEO) satellites (both individual and within a constellation). For this task, physical resilience is loosely defined as ensuring the satellite/constellation is able to complete its mission in a congested (not contested) and naturally hostile operating environment; this means increasing survivability of the physical asset over the expected lifetime as well as enacting measures to ensure the constellation can maintain operations in the event that a satellite is not functioning properly. The next step is represented by the introduction of Distributed Space Systems (DSS) architectures, adopting intersatellite communication links and AI to dynamically reconfigure LEO constellations or alternative satellite formations (swarms, trains, clusters, etc.). In this context, the introduction of AI-based diagnosis and prognosis functionalities is redefining the role of vehicle health monitoring and mission management systems in both civil and military applications.

Artificial Intelligence for Distributed Satellite Systems Autonomous Operations

The goal of this research project is to establish Trusted Autonomous Space Operations (TASO) through dedicated Artificial Intelligence (AI) data processing in Distributed Satellite Systems (DSS) with a focus on the space and control segment co-evolution for Earth Observation (EO) and C4ISR (Communication, Command, Control, Computing, Intelligence, Surveillance and Reconnaissance) missions. Trusted autonomy enhances both DSS design methods and operations, while maximising safety, efficiency and sustainability of space missions. Recent advances in AI techniques for spaceflight systems have proven the ability to perform, adapt and respond to external environmental changes without human intervention. This is critically important for the development of next generation DSS, which require advanced collaboration and coordination approaches to enable new structural functions such as opportunistic coalitions, resource sharing and in-orbit data services. Thus, the system and software design methodologies developed in this project will provide a technical and regulatory pathway for the adoption of trusted autonomous DSS in a variety of civil and military applications. Dedicated EO and C4ISR mission scenarios (developed in collaboration with industry) constitute the basis of detailed simulation case studies to corroborate the validity and effectiveness of the proposed approach.

Human-Autonomy Teaming for Intelligent Distributed Satellite Operations

To fully exploit the advantages of Distributed Satellite System (DSS) architectures, an evolution is required from the inflexible pre-planned approaches of traditional space operations to systems that are suited for reactive and resilient mission approaches. At its core, this requires the development of novel intelligent Mission Planning Systems (iMPS) that facilitate autonomous Goal-Based Operations (GBO). From a technical standpoint, iMPS must facilitate the autonomous cooperation of DSS to optimally achieve global systems goals within an uncertain, dynamic mission environment. From the human perspective, GBO marks a paradigm shift from a command sequence role to one of a supervisory nature, where system autonomy must be monitored and managed in near-real time. This research explores the concept of supervisory control through the design and development of an human centric iMPS for autonomous GBO. This system will enable an operator to express their intentions in the form of system goals, predict and visualize the effects of these intentions and provide intelligent mechanisms that support trusted autonomous system behaviour. System design and development will follow a Model-Based Systems Engineering (MBSE) approach and verified through case studies that include bushfire detection and maritime surveillance while considering key dynamic mission aspects such as the availability of inter-satellite communication links.

Intelligent Health and Mission Management Systems for Aerospace and Defence Applications

Intelligent Health and Mission Management (IHMM) is the next major evolution in the line of system health management concepts in the aerospace and defence industries. IHMM exceeds the maintenance and logistics support benefits of traditional health management systems by introducing predictive integrity and dynamic mission management capabilities. This involves utilizing a combination of real-time measurements from distributed sensor networks as well as high-fidelity models of subsystems and faults to predict the state of health of systems and enable subsequent reconfiguration of systems and replanning of mission activities. Furthermore, dynamic mission management and real-time decision support are novel aspects of health management systems that are enabled by the enhanced health monitoring and health prediction capabilities brought forward by IHMM. These capabilities allow IHMM systems to assume a safety-critical and mission-essential role in the next generation of trusted autonomous aerospace and defence systems. This research project addresses the development of a number of diagnostic and prognostic tools to be utilized in such health management system frameworks for three distinct case studies. The case studies are selected to analyse the different IHMM development requirements across conventional, semi-autonomous and autonomous systems. A variety of Artificial Intelligence (AI) and Machine Learning (ML) tools are utilized in the development of the diagnostic and prognostic algorithms. The limitations of the inference processes developed in each case study is considered. These are mainly associated with the fidelity and assumptions made in the models used to represent the behaviour of systems, as systems operating in the real-world are subject to many external environmental and operational factors with complex interactions that are difficult to account for in physical models. This suggests that the optimal approach is to capitalize on the complementary advantages of both model-based and data-driven approaches to maximise the accuracy, timeliness and reliability of integrity assessments as well as predictions. The integration of IHMM frameworks within selected case-studies are presented, considering the flow of sensor measurements, data and commands. The benefits of the mission reconfiguration capability brought forward by IHMM in response to a detected or predicted fault or performance degradation is demonstrated. This supports future development of more complex forms of IHMM based mission reconfiguration.

Airspace Risk Modelling Research Program:  Evolutions for ATM and low-level UAS operations

Collision risk modelling has a long history in the aviation industry, with mature models currently utilized in the strategic planning of airspace sectors and air routes. However, the progressive introduction of Unmanned Aircraft Systems (UAS) and other forms of air mobility poses new challenges, compounded by a growing need to address both offline and online operational requirements. To address the existing gaps in the current airspace risk assessment models, this research proposes a comprehensive risk management framework, which relies on a novel methodology to model UAS collision risk in all classes of airspace. This methodology inherently accounts for the performance of Communication, Navigation and Surveillance (CNS) systems and, as such, it can be applied to both strategic and tactical operational timeframes. Additionally, the proposed approach can be applied inversely to determine CNS performance requirements given a target value of collision probability. This new risk assessment methodology is based on a rigorous analysis of the CNS error characteristics and the transformation of the associated models into the spatial domain, in order to generate a protection volume around each predicted collision. Additionally, a novel methodology to rapidly and conservatively evaluate the multi-integral formulation of collision probability is being developed in this project. The validity of the proposed framework will be being tested in both simulation and flight test case studies.

Cognitive Human-Machine Systems for Air and Space Transport Operations

This research project addresses the conceptual design, prototyping and verification of a Cognitive Human-Machine Interfaces and Interactions (CHMI2) system to drive adaptive automation based on sensing of the user’s cognitive states. The adaptive automation capability offered by the CHMI2 system provides a pathway towards higher levels of human-machine teaming to support trusted autonomous air and space transport operations. Three potential applications are considered: (1) Remote Pilot Station (RPS) supporting multiple simultaneous operations of Unmanned Aircraft Systems (UAS); (2) Virtual Pilot Assistant (VPA) system for commercial Single-Pilot Operated (SiPO) aircraft; and (3) Avionics and Ground Control Segment (GCS) evolutions for point-to-point suborbital space transport. The CHMI2 architecture comprises three modules, namely: sensing, estimation and adaptation. The sensing module consists of a suite of sensors and algorithms for observing and extracting suitable physiological features of the user. The estimation module contains models that translate the features from the sensing module into measures of the user’s cognitive state. The adaptation module contains the logics that drive adaptation in the Human-Machine Interface (HMI) and system automation modes based on the estimated cognitive states. Development and test activities are currently ongoing, focused on verifying the performance of each individual module in the intended operational environment and will be followed by Human-in-the-Loop (HITL) testing of the prototype systems.

Design Methods and Digital Control of Advanced Distributed Propulsion Systems 

The integration of advanced Distributed Propulsion (DP) systems within various aircraft configurations holds the potential to greatly increase aircraft performance, particularly in terms of fuel efficiency, reduction of harmful emissions and reduction of take-off field length requirements. This has been enabled by modern analysis tools, materials technology and control systems which take advantage of the positive interactions between the propulsion system and the aerodynamics of the aircraft. Synergy between these two systems is maximized when the propulsion system is distributed about the aircraft through the use of distributed nozzles, crossflow fans and multiple distributed fans. Recent advances in electric propulsion have encouraged the hybridization of propulsive systems, with airliners having multiple electric fans powered by one or two gas turbine engines. Furthermore, due to recent advances in airframe integration solutions, the propulsive element can become an integral part of the control and stability augmentation capabilities of the aircraft. Thereby, the digital control of advanced DP systems is crucial for the purpose of thrust modulation and intelligent management of engine resources and health. This not only aids in mission optimization but also supports the case for airworthiness certification of novel aircraft configurations integrating advanced DP systems. This project addresses contemporary advances in hybrid electric technology, aeroelasticity research and the fundamental design steps to integrate advanced DP systems in fixed-wing aircraft. Additionally, an evolutionary approach to the digital control of DP systems is proposed, with a focus on advancing the techniques for mission optimization and engine health management (i.e., diagnosis and prognosis) for enhanced efficiency, safety and sustainability. Based on the proposed design and integration methodologies, conclusions are drawn about the suitability of specific DP technologies for various applications and recommendations are formulated for future research and development.

GNSS Performance Monitoring and Augmentation for Safety-Critical Applications

In an era of significant air traffic expansion characterized by a rising congestion of the radiofrequency spectrum and a widespread introduction of Unmanned Aircraft Systems (UAS), Global Navigation Satellite Systems (GNSS) are being exposed to a variety of threats including signal interferences, adverse propagation effects and challenging platform-satellite relative dynamics. Thus, there is a need to characterize GNSS signal degradations and assess the effects of interfering sources on the performance of avionics GNSS receivers and augmentation systems used for an increasing number of mission-essential and safety-critical aviation tasks (e.g., experimental flight testing, flight inspection/certification of ground-based radio navigation aids, wide area navigation and precision approach). GNSS signal deteriorations typically occur due to antenna obscuration caused by natural and man-made obstructions present in the environment (e.g., elevated terrain and tall buildings when flying at low altitude) or by the aircraft itself during manoeuvring (e.g., aircraft wings and empennage masking the on-board GNSS antenna), ionospheric scintillation, Doppler shift, multipath, jamming and spurious satellite transmissions. Anyone of these phenomena can result in partial to total loss of tracking and possible tracking errors, depending on the severity of the effect and the receiver characteristics. After identifying GNSS performance threats, the various augmentation strategies adopted in the Communication, Navigation, Surveillance/Air Traffic Management and Avionics (CNS+A) context are addressed in this project. GNSS augmentation can take many forms but all strategies share the same fundamental principle of providing supplementary information whose objective is improving the performance and/or trustworthiness of the system. Hence it is of paramount importance to consider the synergies offered by different augmentation strategies including Space Based Augmentation System (SBAS), Ground Based Augmentation System (GBAS), Aircraft Based Augmentation System (ABAS) and Receiver Autonomous Integrity Monitoring (RAIM). Furthermore, by employing multi-GNSS constellations and multi-sensor data fusion techniques, improvements in availability and continuity can be obtained. SBAS is designed to improve GNSS system integrity and accuracy for aircraft navigation and landing, while an alternative approach to GNSS augmentation is to transmit integrity and differential correction messages from ground-based augmentation systems (GBAS). In addition to existing space and ground based augmentation systems, GNSS augmentation may take the form of additional information being provided by other on-board avionics systems, such as in ABAS. As these on-board systems normally operate via separate principles than GNSS, they are not subject to the same sources of error or interference. Using suitable data link and data processing technologies on the ground, a certified ABAS capability could be a core element of a future GNSS Space-Ground-Aircraft Augmentation Network (SGAAN). Although current augmentation systems can provide significant improvement of GNSS navigation performance, a properly designed and flight-certified SGAAN could play a key role in trusted autonomous systems and other cyber-physical system applications such as UAS Sense-and-Avoid (SAA).

Hybrid-Electric Propulsion Integration in Unmanned Aircraft

Hybrid-Electric Propulsion Systems (HEPS) have emerged as a promising area of research in aerospace engineering as they combine the complementary advantages of internal combustion and electric propulsion technologies while limiting the environmental emissions. Despite the promising benefits, the insufficient energy densities and specific energies of electrical storage devices are major challenges as they induce severe weight and volume penalties. Significant opportunities are nonetheless emerging thanks to optimised propulsive profiles, energy harvesting techniques and more electric aircraft technologies. To support further research on hybrid electric aircraft, the aim of this project is to develop a HEPS retrofit design methodology for existing Remotely Piloted Aircraft Systems (RPAS). The implemented HEPS models use power state variables, allowing more accurate predictions of energy converter efficiency than with traditional approaches. Data from state-of-the-art and commercally available components are used in association with selected RPAS platforms to perform a detailed parametric analysis for traditional, electric and hybrid configurations. Range and endurance performances are investigated in depth and the most significant interdependencies between design parameters are identified. The results suggest that HEPS technology represents a viable trade-off solution in small-to-medium size RPAS, promoting the mitigation of noxious and greenhouse emissions while providing adequate range and endurance performance. Current research is addressing the optimal thrust control strategies applicable to different aircraft configurations and flight/mission profiles.

Multiobjective Optimisation of Aircraft Flight Trajectories in the ATM and Avionics Context

The continuous increase of air transport demand worldwide and the push for a more economically viable and environmentally sustainable aviation are driving significant evolutions of aircraft, airspace and airport systems design and operations. Although extensive research has been performed on the optimisation of aircraft trajectories and very efficient algorithms were widely adopted for the optimisation of vertical flight profiles, it is only in the last few years that higher levels of automation were proposed for integrated flight planning and re-routing functionalities of innovative Communication Navigation and Surveillance/Air Traffic Management (CNS/ATM) and Avionics (CNS+A) systems. In this context, the implementation of additional environmental targets and of multiple operational constraints introduces the need to efficiently deal with multiple objectives as part of the trajectory optimisation algorithm. This project aims to develop innovative Multi-Objective Trajectory Optimisation (MOTO) techniques for transport aircraft flight operations, taking advantage of the most recent advances introduced in the CNS+A research context. The most suitable MOTO mathematical formulation and numerical solution techniques are identified (e.g., discretisation and optimisation methods), together with the strategies to articulate preferences and to select optimal trajectories when multiple conflicting objectives are present. Appropriate models are also developed to define optimality criteria and constraints in specific MOTO studies, including fuel consumption, air pollutant and noise emissions, operational costs, condensation trails, airspace and airport operations. Relevant atmospheric and weather modelling approaches are also covered, with a focus on the latest advancements in the respective application areas. Simulation tools are developed and dedicated cases studies are performed to validate the MOTO algorithms in the both strategic and tactical ATM operational tasks. Considering the significant ongoing evolutions of CNS+A technologies for low-level ATM, UAS Traffic Management (UTM) and Advanced Air Mobility (AAM), useful guidelines for future MOTO research are also formulated.

StopRotor – A New VTOL Aircraft Configuration

The StopRotor Unmanned Aerial Vehicle (UAV) is a new aircraft configuration capable of both rotary and fixed wing flight. The design combines the versatility of the rotary-wing system with fixed wing efficiency by in flight configuration changes. The StopRotor is capable of carrying a 40% payload relative to its empty flying weight. The optimization of the aircraft propulsion and Guidance, Navigation and Control (GNC) systems for an expanded range, endurance and mission capability relies on the ability to accurately model the platform aerodynamics. Wind tunnel and aerodynamic analysis using Vortex Lattice Methods (VLM) were therefore performed in the initial phases of this project to extract and validate the aerodynamic 6DOF model (project funded by the Australian DoD and conducted in collaboration between StopRotor Pty Ltd, GNC Solutions Pty Ltd and RMIT University). A “Proof-of-concept” StopRotor aircraft has also demonstrated the platform basic operational capability. In particular, the following primary flight modes/manoeuvres were accomplished: (1) Vertical Take Off and Landing (VTOL); (2)  Hover; (3) Conventional & Short TOL; (4) Fixed wing flight; and (5) Compound/rotary flight. Flight demonstrations have also uncovered how the StopRotor transitions between rotary and fixed wing configurations through specific manoeuvres. These manoeuvres have been progressively refined through flight trials and associated data analysis. Despite this significant progress, the success of the platform relies on the effective automatic control in its 3 primary flight modes: fixed wing, rotary wing and the transition between the two. Research in transitional manoeuvres is novel and only a limited literature is currently available in the engineering body of knowledge. Therefore, further research in this area will be highly instrumental in the StopRotor’s future development.

Flight Guidance and Mission Management Systems for Unmanned Reusable Space Vehicles

Unrestricted access to all classes of airspace, traditionally a prerogative of commercial transport aircraft, is gradually being extended to Unmanned Aircraft Systems (UAS) and space transportation vehicles. The coexistence of various conventional, remotely piloted and autonomous aerospace vehicles in the same airspace dictates higher levels of automation of flight tasks and airspace management functions, towards ensuring mission success while guaranteeing the required levels of safety, efficiency and sustainability. Such higher levels of automation for both manned aircraft and autonomous flying robots, can only be achieved through the development and deployment of on-board Flight Guidance Systems (FGS) and associated ground-based Mission Management Systems (MMS) implementing essential Command, Control and Coordination (C3) functions. In this context, our primary research focus is on the Design, Development, Test and Evaluation (DDT&E) of advanced FGS and MMS architectures supporting Two-Stage to Orbit (TSTO) operations of Unmanned Reusable Space Vehicles (URSV). The primary objective of the FGS is autonomous trajectory planning and execution, while the MMS enables human-autonomy teaming though AI-based decision support algorithms and Cognitive Human-Machine Interfaces and Interactions (CHMI2) in a data-driven Multi-Domain Traffic Management (MDTM) environment. The SL-12 unmanned space vehicle was initially used as the reference URSV platform and a detailed architecture of the FGS was developed. Dedicated algorithms were also implemented for launch and re-entry trajectory planning. Current research is addressing all flight phases of various TSTO platforms (including VTOL, HTOL and hybrid configurations) and the associated development of MMS architectures for trusted autonomous space transport operations.

GNSS Augmentation Strategies for Urban Air Mobility and UAS Traffic Management

This project investigates the vulnerabilities of Global Navigation Satellite Systems (GNSS) in Urban Air Mobility (UAM) and Unmanned Aircraft System (UAS) applications and focusses on the possible strategies for online and offline navigation performance monitoring and augmentation, which can contribute to trusted autonomy in low-altitude Air Traffic Management (ATM) operations. In previous research, the concept of Vehicle/Avionics Based Integrity Augmentation (ABIA) was introduced and the associated system/software architecture was developed with a focus on predictive Integrity Flag Generation (IFG) and Flight Path Optimization (FPO) functions. Simulation case studies and flight test activities were also performed, addressing the synergies of ABIA with Space/Ground Based Augmentation Systems (SBAS and GBAS) and with Cooperative/Non-Cooperative Sense-and-Avoid (SAA) systems. Based on these case studies, it was concluded that the ABIA system is capable of generating both predictive and reactive integrity flags (i.e., caution and warning signals) when GNSS data are degraded or lost, and it can be successfully integrated with SBAS and GBAS to enhance integrity levels in the relvant UAS/UAM flight tasks. Additionally, in the SAA scenarios investigated and in the dynamic conditions explored, all mid-air collision threats were successfully avoided by implementing real-time software algorithms for navigation/tracking uncertainty analysis and trajectory optimisation. Current research is focusing on the development of a more detailed approach to airspace risk analysis and dynamic geofencing, which also takes into account the effects of weather (wind, clear-air turbulence, precipitation, etc.), wake turbulence, and other natural/human-induced disturbances. Prototype systems are being developed and flight test activities are being conducted on various classes of UAS to assess the potential application of this technology to next generation Mission Management Systems (MMS) and Decision Support Systems (DSS) for UAS Traffic Management (UTM), Urban Air Mobility (UAM), and operations in GNSS-challenged environments.