Continuous rapid advances in airborne computing, sensors and communication technologies are stimulating the development of integrated multisensor avionics systems for an increasing number of aeronautical and space applications. In particular, intelligent automation and networking technologies are being extensively applied to UAS and space platforms, allowing the development of high-performance multisensor Guidance, Navigation and Control (GNC) systems as well as advanced mission systems with reduced Size, Weight, Power and Cost (SWaP-C). The widespread introduction of Performance-Based Navigation (PBN) is the first step of an evolutionary process from equipment-based to Performance-Based Operations (PBO). PBN specifies that aircraft navigation systems performance requirements shall be defined in terms of accuracy, integrity, availability and continuity for the proposed operations in the context of a particular airspace when supported by an appropriate Air Traffic Management (ATM) infrastructure. The full PBO paradigm shift requires the introduction of suitable metrics for Performance-Based Communication (PBC) and Performance-Based Surveillance (PBS). The proper development of such metrics and a detailed definition of PBN-PBC-PBS interrelationships for manned and unmanned aircraft operations represent one of the most exciting research challenges currently facing the avionics research community with major impacts on air transport safety, airspace capacity and operational efficiency.
In parallel, the International Civil Aviation Organization (ICAO) Aviation System Block Upgrades (ASBUs) rely on a progressive introduction of advanced Communication, Navigation and Surveillance (CNS) technologies, including digital data links, satellite services and Automatic Dependent Surveillance–Broadcast (ADS-B), which will effectively enable the transition to network-centric aviation operations. However, the international aviation community (both civil and military) is now facing important technological and operational challenges to allow a proper development and deployment of the CNS/ATM and Avionics (CNS+A) innovations announced by the FAA Next Generation Air Transportation System (NextGen), the EU Single European Sky ATM Research (SESAR) and other programs such as CARATS (Collaborative Actions for Renovation of Air Traffic Systems) in Japan and OneSky in Australia. In particular, it is essential to address global harmonization issues and to develop a cohesive certification framework for future CNS+A systems simultaneously addressing safety, security and interoperability requirements as an integral part of the Research, Development, Test and Evaluation (RDT&E) process.
In response to these challenges, modern avionics and space systems are becoming more and more cyber-physical, with software and hardware components seamlessly integrated towards performing highly automated/autonomous tasks. These tasks are increasingly demanding and distributed amongst multiple platforms/sub-systems, while recent research trends elicit the introduction of Artificial Intelligence (AI), fault-tolerant architectures and adaptive Human-Machine Interfaces and Interactions (HMI2) to support the development of Trusted Autonomous Systems (TAS).
This lecture addressed key contemporary issues in air and space Cyber-Physical Systems (CPS) research focussing on the key challenges and opportunities currently faced by the global aerospace industry with the pervasive adoption of automation and AI technologies. First of all, automation is becoming more and more complex, with the widespread adoption of heterogeneous sensor networks and the need for optimization algorithms that deal with an increasing amount of input data (including unstructured, semi-structured and asynchronous data), multiple objectives and constraints. A well-known side effect of this complexity is the reduction or loss of situational awareness of the human operator, who is no longer capable of evaluating the validity and quality of the solutions implemented. Secondly, most of the automation we are introducing is deterministic and not adaptive enough and, paradoxically, it may end up by increasing the workload of human operators in certain scenarios instead of alleviating it. This is why instances of cognitive overload are not infrequent despite dealing with highly automated systems. Finally, the kind of automation that is currently being adopted in complex systems is not deeply trusted by humans because it lacks sufficient transparency and/or integrity.
It is therefore essential to develop innovative CPS that address these fundamental challenges by implementing innovative cognitive processing and machine learning techniques towards enhancing human-machine interactions and building trusted autonomy. CPS are at the core of the digital innovation that is transforming our world and redefining the way we interact with intelligent machines in a growing number of industrial sectors and social contexts. Present-day CPS integrate computation and physical processes to perform a variety of mission-essential or safety-critical tasks. From a historical perspective CPS combine elements of cybernetics, mechatronics, control theory, systems engineering, embedded systems, sensor networks, distributed control and communications.
Properly engineered CPS rely on the seamless integration of digital and physical components, with the possibility of including human interactions. This requires three fundamental functions to be present: control, computation and communication. Practical CPS typically combine sensor networks and embedded computing to monitor and control physical processes, with feedback loops that allow physical processes to affect computations and vice-versa. Despite the significant progress in CPS research, the full economic, social and environmental benefits associated to such systems are far from being fully realized. Major investments are being made worldwide to develop CPS for an increasing number of engineering applications, including aerospace, transport, defense, robotics, communications, security, energy, medical, smart agriculture, humanitarian, etc.
Current avionics and space systems research is focusing on two main categories of CPS: Autonomous Cyber-Physical (ACP) systems and Cyber-Physical-Human (CPH) systems. ACP systems operate without the need for human intervention or control. For ACP systems to work, formal reasoning is required as these systems are normally used to accomplish mission/safety-critical tasks and any deviation from the intended behavior may have significant implications on human health, well-being, economy, etc. A sub-class is that of Semi-Autonomous Cyber-Physical (S-ACP) systems, which perform autonomous tasks in a specific set of pre-defined conditions but require a human operator otherwise.
A separate category is that of CPH systems. These are a particular class of CPS where the interaction between the dynamics of the system and the cyber elements of its operation can be influenced by the human operator and the interaction between these three elements is regulated to meet specific objectives. CPH systems consist of three main components: physical elements sensing and modeling the environment, the systems to be controlled and the human operators; cyber elements including the communication links and software; and human operators who partially monitor the operation of the system and can intervene if and when needed.
Today, several aerospace CPS implementations are S-ACP systems. This fact limits the achievable benefits and the range of possible applications due to the reduced fault-tolerance and the inability of S-ACP to dynamically adapt in response to external stimuli. Many S-ACP architectures are progressively evolving to become either ACP or CHP depending on the specific applications. Current research in the aerospace, defense and transport sectors aims at developing robust and fault-tolerant ACP and CPH system architectures that ensure trusted autonomous operations with the given hardware constraints, despite the uncertainties in physical processes, the limited predictability of environmental conditions, the variability of mission requirements (especially in congested or contested scenarios), and the possibility of both cyber and human errors. A key point in these advanced CPS is the control of physical processes from the monitoring of variables and the use of computational intelligence to obtain a deep knowledge of the monitored environment, thus providing timely and more accurate decisions and actions. The growing interconnection of physical and digital elements, and the introduction of highly sophisticated and efficient AI techniques, has led to a new generation of CPS, that is referred to as intelligent (or smart) CPS (iCPS).
By equipping physical objects with interfaces to the virtual world, and incorporating intelligent mechanisms to leverage collaboration between these objects, the boundaries between the physical and virtual worlds become blurred. Interactions occurring in the physical world are capable of changing the processing behavior in the virtual world, in a causal relationship that can be exploited for the constant improvement of processes. Exploiting iCPS, intelligent, self-aware, self-managing and self-configuring systems can be built to improve the quality of industrial process across a variety of application domains.
Advances in aerospace CPS research are accelerating the introduction of intelligent automation (both on platforms and ground control systems) and a progressive transition to trusted autonomous operations. Major benefits of these capabilities include a progressive de-crewing of flight decks and ground control centers, as well as the safe and efficient operations of air and space platforms in a shared, unsegregated environment.
In the commercial aviation context, CPS are supporting the transition from the two-pilot flight crews to single pilot operations, with the co-pilot potentially replaced by a remote pilot on the ground. A single remote pilot on the ground, on the other hand, will no longer be restricted to controlling a single UAS and instead will be allowed to control multiple vehicles, in line with the so-called One-to-Many (OTM) concept.
Important efforts are being devoted to the integration of Unmanned Aircraft Systems (UAS) in all classes of airspace, eliciting the introduction of UAS Traffic Management (UTM) services seamlessly integrated with the existing (and evolving) ATM framework. In particular, UTM requires substantial advances in CNS+A technologies and associated regulatory frameworks, especially to enable low-altitude and Beyond-Line-of-Sight (BLoS) operations. Recent advances in communications, navigation and Sense-and-Avoid (SAA) technology are therefore progressively supporting UTM operations in medium-to-high density operational environments, including urban environments.
Important research efforts are also necessary to demonstrate the feasibility of avionics and CNS/ATM technologies capable of contributing to the emission reduction targets set by the International Civil Aviation Organization (ICAO), national governments and various large-scale international research initiatives. Therefore, growing emphasis is now being placed on environmental performance enhancements, focusing on Air Traffic Flow Management (ATFM), dynamic airspace management, 4-dimensional (4D) trajectory optimization, airport automation and, in the near future, urban flight operations.
In addition to CNS+A technologies for air operations, space CPS are also being researched for a wide range of practical applications including commercial satellites, space transport/tourism, and interplanetary scientific missions. In this context, it is anticipated that economically viable and reliable cyber-physical systems will play a fundamental role in the successful development of the space sector and significant research efforts are needed in the field of reusable space transportation systems, Space Traffic Management (STM), and Intelligent Satellite Systems (SmartSats).
In particular, the operation of space launch and re-entry platforms currently requires considerable airspace segregation provisions, which if continued will become increasingly disruptive to civil air traffic. Moreover, the currently limited space situational awareness is posing significant challenges to the safety and sustainability of spaceflight due to the rapidly growing amount of resident space objects and particularly orbital debris. The deployment of network-centric CNS+A systems and their functional integration with ground-based ATM in a Space Traffic Management (STM) framework will support a much more flexible and efficient use of the airspace with higher levels of safety. These evolutions will support the transition to what the international aerospace and electronic systems research community have started naming “Unified Traffic Management.”