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Special Issue "Cyber-Physical Systems for Automated Decision Making and Trusted Autonomy"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 October 2020.

Special Issue Editors

Prof. Dr. Roberto Sabatini
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Guest Editor
Professor of Aerospace Engineering and Aviation, Chair of the Cyber-Physical and Autonomous Systems Group, Deputy Director for Aerospace of the Sir Lawrence Wackett Centre, RMIT University, School of Engineering, PO Box 71, Bundoora, VIC 3083, Australia
Interests: aerospace sensors and systems; aerospace robotics and automation; cyber-physical systems; intelligent and autonomous systems; aircraft systems; avionics; spaceflight systems; air traffic management; unmanned aircraft systems; defence systems; intelligent transportation systems; space traffic management; sustainable aviation; navigation, guidance and control; satellite navigation; electro-optics and infrared systems; human factors and ergonomics; human-machine systems; human-robotic interactions; multisensor data fusion.
Special Issues and Collections in MDPI journals
Dr. Alessandro Gardi
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Co-Guest Editor
Senior Research Fellow in Air Traffic Management, Cyber-Physical Systems and Trusted Autonomy Group, RMIT University, School of Engineering, PO Box 71, Bundoora, VIC 3083, Australia
Interests: air traffic management; avionics; optimal control; cyberphysical systems; trajectory optimisation; human factors and ergonomics; cognitive ergonomics; guidance, navigation and control; sustainable aviation; LIDAR and electro-optics; trusted autonomous systems; flight dynamics; space traffic management; sense-and-avoid
Special Issues and Collections in MDPI journals

Special Issue Information

Cyber-Physical systems (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, sensor networks, mechatronics, systems engineering, embedded systems, 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 (C3). 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 realised. Major investments are being made worldwide to develop CPS for an increasing number of applications, including aerospace, transport, defence, robotics, communications, security, energy, medical, smart agriculture, humanitarian, etc. 

This Special Issues focusses on innovative sensors, sensor networks, and software architectures supporting the design and operation of CPS, with a focus on autonomous cyber-physical (ACP) and cyber-physical–human (CPH) systems for automated decision making and human–autonomy teaming. 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, wellbeing, economy, etc. A subclass is that of semi-autonomous cyber-physical (S-ACP) systems, which perform autonomous tasks in a specific set of predefined 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 modelling the environment, the systems to be controlled, and the human operators; cyber elements including communication links and software; and human operators who partially monitor the operation of the system and can intervene if and when needed.

Today, several CPS implementations are S-ACP systems. This fact limits the achievable benefits and range of possible applications due to the reduced fault-tolerance and the inability of S-ACP systems 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 application domains. Thus, current research 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 artificial intelligence techniques has led to a new generation of CPS, which is referred to as intelligent (or smart) CPS (iCPS).

Original manuscripts are elicited from researchers active in the following areas:

  • Aeronautical and space cyber-physical systems;
  • Intelligent transport and future mobility systems;
  • Autonomous and robotic guidance, navigation, and control;
  • Human–machine systems and trusted autonomy;
  • Systems for digital and personalised healthcare;
  • Technologies for smart and precision agriculture;
  • Wireless sensors, actuators and IoT;
  • Enabling technologies for the smart energy grid;
  • Geospatial data acquisition, distribution and analysis;
  • Transport safety and accident investigation;
  • Defence, security, and humanitarian mission systems;
  • Cyber-physical system safety and security;
  • Cognitive and cybernetic systems.

Particular consideration will be given to manuscripts that bridge the existing research gaps in multiple industry sectors, addressing the fundamental role of CPS and iCPS in the evolution of Internet of Things (IoT), Industry 4.0, and Industry 5.0 technologies, businesses, and policies.

Prof. Dr. Roberto Sabatini
Dr. Alessandro Gardi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

Open AccessArticle
Vehicular Sensor Network and Data Analytics for a Health and Usage Management System
Sensors 2020, 20(20), 5892; https://doi.org/10.3390/s20205892 - 17 Oct 2020
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 [...] Read more.
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. Full article
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Open AccessFeature PaperArticle
Network Optimisation and Performance Analysis of a Multistatic Acoustic Navigation Sensor
Sensors 2020, 20(19), 5718; https://doi.org/10.3390/s20195718 - 08 Oct 2020
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 [...] Read more.
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. Full article
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Open AccessArticle
A Cyber-Physical-Human System for One-to-Many UAS Operations: Cognitive Load Analysis
Sensors 2020, 20(19), 5467; https://doi.org/10.3390/s20195467 - 23 Sep 2020
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 [...] Read more.
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. Full article
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