Showing papers by "Roberto Sabatini published in 2020"

Avionics Systems Panel Research and Innovation Perspectives

Abstract: As current, large-scale research and development initiatives are reshaping the future of aviation and space operations, avionics systems are becoming cyber-physical and progressively evolving into a variety of autonomous, intelligent, and closed-loop human–machine systems. This article provided the IEEE Aerospace & Electronic Systems Society (AESS) Avionics Systems Panel (ASP) views on avionics systems evolutionary pathways, with an identification of key research challenges and industry-focused innovation opportunities. The ever-increasing density of air traffic and the rise of unmanned aircraft systems (UAS) are prompting a rapid evolution of communication, navigation, surveillance/air traffic management (CNS/ATM) and avionics (CNSþA) technologies that will provide unprecedented enhancements in terms of safety and efficiency, thus unleashing additional airspace and airport capacity. Several of the underlying CNS technologies have already hit the market, while other more advanced capabilities and decision support systems are still being researched and developed. The methodological transition to performance-based operations (PBO) is also a quantum shift that will have profound impacts on aviation equipment mandates and standards with very tangible benefits in terms of airspace capacity, safety, access modalities, prioritization, and overall fairness. The PBO transition is well underway for navigation equipment standards and operational arrival/ departure procedures, whereas communication and surveillance equipment is still currently following legacy mandates/ equipage schemes.

A Performance-Based Airspace Model for Unmanned Aircraft Systems Traffic Management

Abstract: Recent evolutions of the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) concept are driving the introduction of new airspace structures and classifications, which must be suitable for low-altitude airspace and provide the required level of safety and flexibility, particularly in dense urban and suburban areas. Therefore, airspace classifications and structures need to evolve based on appropriate performance metrics, while new models and tools are needed to address UTM operational requirements, with an increasing focus on the coexistence of manned and unmanned Urban Air Mobility (UAM) vehicles and associated Communication, Navigation and Surveillance (CNS) infrastructure. This paper presents a novel airspace model for UTM adopting Performance-Based Operation (PBO) criteria, and specifically addressing urban airspace requirements. In particular, a novel airspace discretisation methodology is introduced, which allows dynamic management of airspace resources based on navigation and surveillance performance. Additionally, an airspace sectorisation methodology is developed balancing the trade-off between communication overhead and computational complexity of trajectory planning and re-planning. Two simulation case studies are conducted: over the skyline and below the skyline in Melbourne central business district, utilising Global Navigation Satellite Systems (GNSS) and Automatic Dependent Surveillance-Broadcast (ADS-B). The results confirm that the proposed airspace sectorisation methodology promotes operational safety and efficiency and enhances the UTM operators’ situational awareness under dense traffic conditions introducing a new effective 3D airspace visualisation scheme, which is suitable both for mission planning and pre-tactical UTM operations. Additionally, the proposed performance-based methodology can accommodate the diversity of infrastructure and vehicle performance requirements currently envisaged in the UTM context. This facilitates the adoption of this methodology for low-level airspace integration of UAS (which may differ significantly in terms of their avionics CNS capabilities) and set foundations for future work on tactical online UTM operations.

Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops

Abstract: In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.

Human-Machine Interactions in Very-Low-Level UAS Operations and Traffic Management

Abstract: With the proliferation of Unmanned Aircraft Systems (UAS) in low-altitude airspace and a growing interest in new Urban Air Mobility (UAM) solutions, the Air Traffic Controller (ATCo)'s responsibility to ensure safety and efficiency of operations can no longer be fulfilled with the conventional air traffic control paradigm. Hence, a new increasingly autonomous Decision Support System (DSS) specifically designed for integrated manned/UAS Traffic Management (UTM) is of paramount importance. This DSS makes use of advanced traffic flow and airspace management concepts, but to ensure effective teaming between the human and the system in challenging situations, the nature of their roles and responsibilities is to be analysed in depth and reflected in the design of suitable Human-Machine Interfaces and Interactions (HMI2). The task analysis presented in this paper assessed the interdependencies between the human and the machine following the Observe-Orient-Decide-Act (OODA) framework. The paper focuses on the management of urban airspace, which is partitioned based on navigation performance. The human-machine workflow is presented and discussed, highlighting the proposed interactions in each subtask. To support closed-loop interactions and enhance system integrity, the UTM DSS makes use of the Cognitive HMI2 concept, which is also briefly outlined in this paper.

A Cyber-Physical-Human System for One-to-Many UAS Operations: Cognitive Load Analysis.

Abstract: The continuing development of avionics for Unmanned Aircraft Systems (UASs) is introducing higher levels of intelligence and autonomy both in the flight vehicle and in the ground mission control, allowing new promising operational concepts to emerge. One-to-Many (OTM) UAS operations is one such concept and its implementation will require significant advances in several areas, particularly in the field of Human–Machine Interfaces and Interactions (HMI2). Measuring cognitive load during OTM operations, in particular Mental Workload (MWL), is desirable as it can relieve some of the negative effects of increased automation by providing the ability to dynamically optimize avionics HMI2 to achieve an optimal sharing of tasks between the autonomous flight vehicles and the human operator. The novel Cognitive Human Machine System (CHMS) proposed in this paper is a Cyber-Physical Human (CPH) system that exploits the recent technological developments of affordable physiological sensors. This system focuses on physiological sensing and Artificial Intelligence (AI) techniques that can support a dynamic adaptation of the HMI2 in response to the operators’ cognitive state (including MWL), external/environmental conditions and mission success criteria. However, significant research gaps still exist, one of which relates to a universally valid method for determining MWL that can be applied to UAS operational scenarios. As such, in this paper we present results from a study on measuring MWL on five participants in an OTM UAS wildfire detection scenario, using Electroencephalogram (EEG) and eye tracking measurements. These physiological data are compared with a subjective measure and a task index collected from mission-specific data, which serves as an objective task performance measure. The results show statistically significant differences for all measures including the subjective, performance and physiological measures performed on the various mission phases. Additionally, a good correlation is found between the two physiological measurements and the task index. Fusing the physiological data and correlating with the task index gave the highest correlation coefficient (CC = 0.726 ± 0.14) across all participants. This demonstrates how fusing different physiological measurements can provide a more accurate representation of the operators’ MWL, whilst also allowing for increased integrity and reliability of the system.

A Novel Navigation Performance-based Airspace Model for Urban Air Mobility

Abstract: The emergence of the UAS Traffic Management (UTM) framework has led to the potential to designate new airspace structures and classifications that are suitable for low altitude airspace and particularly in denser urban and suburban areas. This paper presents a novel airspace management approach for Urban Air Mobility (UAM) below the skyline in particular. A robust airspace discretization methodology is developed based on the expected navigation performance. Additionally, a novel methodology is developed to optimally generate airspace sectors from discrete 3D airspace cells. A preliminary verification of the novel airspace structuring methodology is performed based on navigation performance with a reference navigation architecture based on the Global Navigation Satellite System (GNSS). The proposed performance-based discretization methodology promotes operational safety and efficiency and supports enhanced UTM operators' situational awareness under dense traffic conditions. The analysis performed demonstrates that the proposed airspace model can efficiently accommodate unmanned aircraft with varying avionics equipment and can be expanded to a full Communication, Navigation and Surveillance (CNS) performance-based approach, allowing a continuous dynamic optimisation of airspace capacity.

Vehicular Sensor Network and Data Analytics for a Health and Usage Management System

Abstract: Automated collection of on-vehicle sensor data allows the development of artificial intelligence (AI) techniques for vehicular systems' diagnostic and prognostic processes to better assess the state-of-health, predict faults and evaluate residual life of ground vehicle systems. One of the vital subsystems, in terms of safety and mission criticality, is the power train, (comprising the engine, transmission, and final drives), which provides the driving torque required for vehicle acceleration. In this paper, a novel health and usage monitoring system (HUMS) architecture is presented, together with dedicated diagnosis/prognosis algorithms that utilize data gathered from a sensor network embedded in an armoured personnel carrier (APC) vehicle. To model the drivetrain, a virtual dynamometer is introduced, which estimates the engine torque output for successive comparison with the measured torque values taken from the engine control unit. This virtual dynamometer is also used in conjunction with other sensed variables to determine the maximum torque output of the engine, which is considered to be the primary indicator of engine health. Regression analysis is performed to capture the effect of certain variables such as engine hours, oil temperature, and coolant temperature on the degradation of maximum engine torque. Degradations in the final drives system were identified using a comparison of the temperature trends between the left-hand and right-hand final drives. This research lays foundations for the development of real-time diagnosis and prognosis functions for an integrated vehicle health management (IVHM) system suitable for safety critical manned and unmanned vehicle applications.

Network Optimisation and Performance Analysis of a Multistatic Acoustic Navigation Sensor.

Abstract: This paper addresses some of the existing research gaps in the practical use of acoustic waves for navigation of autonomous air and surface vehicles. After providing a characterisation of ultrasonic transducers, a multistatic sensor arrangement is discussed, with multiple transmitters broadcasting their respective signals in a round-robin fashion, following a time division multiple access (TDMA) scheme. In particular, an optimisation methodology for the placement of transmitters in a given test volume is presented with the objective of minimizing the position dilution of precision (PDOP) and maximizing the sensor availability. Additionally, the contribution of platform dynamics to positioning error is also analysed in order to support future ground and flight vehicle test activities. Results are presented of both theoretical and experimental data analysis performed to determine the positioning accuracy attainable from the proposed multistatic acoustic navigation sensor. In particular, the ranging errors due to signal delays and attenuation of sound waves in air are analytically derived, and static indoor positioning tests are performed to determine the positioning accuracy attainable with different transmitter-receiver-relative geometries. Additionally, it is shown that the proposed transmitter placement optimisation methodology leads to increased accuracy and better coverage in an indoor environment, where the required position, velocity, and time (PVT) data cannot be delivered by satellite-based navigation systems.

Facial Expression Analysis for Cognitive State Estimation in Aerospace Human-Machine Systems

Abstract: Growing air traffic and an increasing demand for Unmanned Aircraft System (UAS) services elicit the need for new systems assisting air traffic controllers and UAS operators in performing their tasks in order to manage more aircraft without compromising operational safety. A key challenge is to support One-To-Many (OTM) operations where a single human operator is responsible for command and control of multiple assets. This study focusses on facial expression analysis for cognitive state estimation in a representative OTM scenario. The aim is to support the design of OTM systems and associated Human-Machine Interfaces and Interaction (HMI2), which can dynamically adapt the automation support level as a function of the operator’s cognitive states.

Human-Machine System Design for Autonomous Distributed Satellite Operations

Abstract: Distributed Satellite Systems (DSS) provide a promising solution in increasing the sustainability of both the space and terrestrial environment through responsive Earth Observation (EO) and Space Domain Awareness (SDA) operations. To exploit the advantages of DSS mission architectures, a technical evolution is required from the deliberative methodologies of traditional ground station operations to approaches that are more suited to autonomous, reactive space mission architectures. At its core, this transition is directly reflected in the design, and development of new, more autonomous Mission Planning Systems that adopt the Adaptive Multi-Agent System (AMAS) framework. With a view towards trusted autonomy, this paper explores the required evolution towards a more supervisory role of future ground station operations. In doing so, this paper provides an initial analysis of a conceptual goal-based distributed space-based SDA application within the Observe Orient Decide and Act (OODA) decision loop framework.

Acoustic Positioning and Navigation System for GNSS Denied/Challenged Environments

Abstract: Current navigation sensors mostly rely on electromagnetic signals for getting the position, velocity, and time (PVT) information. But it can be observed that mammals like bats use acoustic waves, mostly ultrasound, for echolocation. Acoustic waves are also used by cetaceans like dolphins and sperm whales for echolocation. This paper investigates the performance of the acoustic positioning and navigation system (APNS). Acoustic sensors have relatively lower cost, size, weight, and power (C-SWAP) and are easy to deploy. Additionally, being based on acoustic signals, this technique is immune to signal-in-space electromagnetic interferences. The attenuation of sound in air is discussed along with potential ranging errors and signal delays. A multistatic arrangement of sensors is discussed in detail, with an optimized arrangement of transmitters in a given test geometry. The transmitters broadcast their respective signals following a Time Division Multiple Access (TDMA) scheme. The receiver position is calculated based on ranging measurements from a minimum of three transmitters. The range is calculated based on the Time of Arrival (TOA) of acoustic waves from the transmitter to the receiver. The transmitters are arranged optimally to minimize Position Dilution of Precision (PDOP) as well as maximizing sensor availability. The error in positioning due to platform dynamics is also discussed. This analysis lead to an optimized arrangement of transmitters, thus supporting subsequent experimental activities.

Acoustic Positioning and Navigation System for Micro Aerial Vehicle Navigation

Abstract: Animals, especially mammals like bats and dolphins, use acoustic waves that vary in frequency, signal duration, and intensity, for navigation and tracking. The directionality of acoustic waves has also been long used for localization by human beings. The term ‘echolocation’ was coined by Donald R. Griffin, where he discusses ship captains exploiting sound to ascertain the ship's surroundings and avoid obstacles in low-visibility environments. Acoustic sensors can provide a low-cost, size, weight, and power (C-SWaP) navigation solution, which is scalable and robust. Moreover, acoustic sensors have the capability to provide high-resolution spatial information at short distance range. This paper presents a novel acoustic positioning and navigation system for a micro aerial vehicle. Flight tests are performed to evaluate the system, where the performance of the acoustic system is compared with a motion capture system.