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Cyber-Physical Systems for Automated Decision Making and Trusted Autonomy
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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: closed (20 December 2022) | Viewed by 32568

Special Issue Editors

Prof. Dr. Roberto Sabatini

E-Mail Website
Guest Editor
Department of Aerospace Engineering, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates
Interests: aerospace vehicle design and testing; avionics and air traffic management systems; spaceflight systems design and operations; aerospace robotics and autonomous systems; guidance, navigation and control systems; Unmanned Aircraft Systems (UAS) and UAS traffic management; advanced air mobility and urban air mobility; distributed and intelligent satellite systems; space domain awareness and space traffic management; GNSS integrity monitoring and augmentation; defense C4ISR and electronic warfare systems; cognitive human-machine systems
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandro Gardi

E-Mail Website
Guest Editor
Senior Research Fellow, Aerospace Engineering and Aviation, Program Lead: Human-Machine Systems and Trusted Autonomy, Cyber-Physical and Autonomous Systems Group, School of Engineering, RMIT University, 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, Collections and Topics in MDPI journals

Special Issue Information

Cyber-physical systems (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 realised. Industry 4.0 fosters what has been called a “smart factory”. Within modular smart factories, CPS monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the Internet of Things (IoT), cyber-physical systems communicate and cooperate with each other and with humans in real-time both internally and across organizational services offered and used by participants of the value chain. 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 CPS (ACPS) and cyber-physical-human systems (CPHS) for automated decision making and trusted autonomous operations. 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.

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 submissions that pass pre-check are 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 2600 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.

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Published Papers (7 papers)

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Research

25 pages, 4709 KiB  
Article
Vision beyond the Field-of-View: A Collaborative Perception System to Improve Safety of Intelligent Cyber-Physical Systems
by Manzoor Hussain, Nazakat Ali and Jang-Eui Hong
Sensors 2022, 22(17), 6610; https://doi.org/10.3390/s22176610 - 1 Sep 2022
Cited by 9 | Viewed by 2432
Abstract
Cyber-physical systems (CPSs) that interact with each other to achieve common goals are known as collaborative CPSs. Collaborative CPSs can achieve complex goals that individual CPSs cannot achieve on their own. One of the examples of collaborative CPSs is the vehicular cyber-physical systems [...] Read more.
Cyber-physical systems (CPSs) that interact with each other to achieve common goals are known as collaborative CPSs. Collaborative CPSs can achieve complex goals that individual CPSs cannot achieve on their own. One of the examples of collaborative CPSs is the vehicular cyber-physical systems (VCPSs), which integrate computing and physical resources to interact with each other to improve traffic safety, situational awareness, and efficiency. The perception system of individual VCPS has limitations on its coverage and detection accuracy. For example, the autonomous vehicle’s sensor cannot detect occluded objects and obstacles beyond its field of view. The VCPS can combine its own data with other collaborative VCPSs to enhance perception, situational awareness, accuracy, and traffic safety. This paper proposes a collaborative perception system to detect occluded objects through the camera sensor’s image fusion and stitching technique. The proposed collaborative perception system combines the perception of surrounding autonomous driving systems (ADSs) that extends the detection range beyond the field of view. We also applied logistic chaos map-based encryption in our collaborative perception system in order to avoid the phantom information shared by malicious vehicles and improve safety in collaboration. It can provide the real-time perception of occluded objects, enabling safer control of ADSs. The proposed collaborative perception can detect occluded objects and obstacles beyond the field of view that individual VCPS perception systems cannot detect, improving the safety of ADSs. We investigated the effectiveness of collaborative perception and its contribution toward extended situational awareness on the road in the simulation environment. Our simulation results showed that the average detection rate of proposed perception systems was 45.4% more than the perception system of an individual ADS. The safety analysis showed that the response time was increased up to 1 s, and the average safety distance was increased to 1.2 m when the ADSs were using collaborative perception compared to those scenarios in which the ADSs were not using collaborative perception. Full article
(This article belongs to the Special Issue Cyber-Physical Systems for Automated Decision Making and Trusted Autonomy)
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35 pages, 7799 KiB  
Article
Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions
by Andreas Holzinger, Anna Saranti, Alessa Angerschmid, Carl Orge Retzlaff, Andreas Gronauer, Vladimir Pejakovic, Francisco Medel-Jimenez, Theresa Krexner, Christoph Gollob and Karl Stampfer
Sensors 2022, 22(8), 3043; https://doi.org/10.3390/s22083043 - 15 Apr 2022
Cited by 73 | Viewed by 11754
Abstract
The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent [...] Read more.
The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline—no AI can do this. Consequently, human-centered AI (HCAI) is a combination of “artificial intelligence” and “natural intelligence” to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art. Full article
(This article belongs to the Special Issue Cyber-Physical Systems for Automated Decision Making and Trusted Autonomy)
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22 pages, 566 KiB  
Article
Fault-Adaptive Autonomy in Systems with Learning-Enabled Components
by Daniel Stojcsics, Dimitrios Boursinos, Nagabhushan Mahadevan, Xenofon Koutsoukos and Gabor Karsai
Sensors 2021, 21(18), 6089; https://doi.org/10.3390/s21186089 - 11 Sep 2021
Cited by 9 | Viewed by 2497
Abstract
Autonomous Cyber-Physical Systems (CPS) must be robust against potential failure modes, including physical degradations and software issues, and are required to self-manage contingency actions for these failures. Physical degradations often have a significant impact on the vehicle dynamics causing irregular behavior that can [...] Read more.
Autonomous Cyber-Physical Systems (CPS) must be robust against potential failure modes, including physical degradations and software issues, and are required to self-manage contingency actions for these failures. Physical degradations often have a significant impact on the vehicle dynamics causing irregular behavior that can jeopardize system safety and mission objectives. The paper presents a novel Behavior Tree-based autonomy architecture that includes a Fault Detection and Isolation Learning-Enabled Component (FDI LEC) with an Assurance Monitor (AM) designed based on Inductive Conformal Prediction (ICP) techniques. The architecture implements real-time contingency-management functions using fault detection, isolation and reconfiguration subsystems. To improve scalability and reduce the false-positive rate of the FDI LEC, the decision-making logic provides adjustable thresholds for the desired fault coverage and acceptable risk. The paper presents the system architecture with the integrated FDI LEC, as well as the data collection and training approach for the LEC and the AM. Lastly, we demonstrate the effectiveness of the proposed architecture using a simulated autonomous underwater vehicle (AUV) based on the BlueROV2 platform. Full article
(This article belongs to the Special Issue Cyber-Physical Systems for Automated Decision Making and Trusted Autonomy)
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19 pages, 9250 KiB  
Article
Cyber-Physical System for Environmental Monitoring Based on Deep Learning
by Íñigo Monedero, Julio Barbancho, Rafael Márquez and Juan F. Beltrán
Sensors 2021, 21(11), 3655; https://doi.org/10.3390/s21113655 - 24 May 2021
Cited by 8 | Viewed by 3545
Abstract
Cyber-physical systems (CPS) constitute a promising paradigm that could fit various applications. Monitoring based on the Internet of Things (IoT) has become a research area with new challenges in which to extract valuable information. This paper proposes a deep learning classification sound system [...] Read more.
Cyber-physical systems (CPS) constitute a promising paradigm that could fit various applications. Monitoring based on the Internet of Things (IoT) has become a research area with new challenges in which to extract valuable information. This paper proposes a deep learning classification sound system for execution over CPS. This system is based on convolutional neural networks (CNNs) and is focused on the different types of vocalization of two species of anurans. CNNs, in conjunction with the use of mel-spectrograms for sounds, are shown to be an adequate tool for the classification of environmental sounds. The classification results obtained are excellent (97.53% overall accuracy) and can be considered a very promising use of the system for classifying other biological acoustic targets as well as analyzing biodiversity indices in the natural environment. The paper concludes by observing that the execution of this type of CNN, involving low-cost and reduced computing resources, are feasible for monitoring extensive natural areas. The use of CPS enables flexible and dynamic configuration and deployment of new CNN updates over remote IoT nodes. Full article
(This article belongs to the Special Issue Cyber-Physical Systems for Automated Decision Making and Trusted Autonomy)
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20 pages, 5743 KiB  
Article
Vehicular Sensor Network and Data Analytics for a Health and Usage Management System
by Kavindu Ranasinghe, Rohan Kapoor, Alessandro Gardi, Roberto Sabatini, Vishwanath Wickramanayake and David Ludovici
Sensors 2020, 20(20), 5892; https://doi.org/10.3390/s20205892 - 17 Oct 2020
Cited by 4 | Viewed by 3978
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
(This article belongs to the Special Issue Cyber-Physical Systems for Automated Decision Making and Trusted Autonomy)
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19 pages, 5953 KiB  
Article
Network Optimisation and Performance Analysis of a Multistatic Acoustic Navigation Sensor
by Rohan Kapoor, Alessandro Gardi and Roberto Sabatini
Sensors 2020, 20(19), 5718; https://doi.org/10.3390/s20195718 - 8 Oct 2020
Cited by 3 | Viewed by 2420
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
(This article belongs to the Special Issue Cyber-Physical Systems for Automated Decision Making and Trusted Autonomy)
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21 pages, 2180 KiB  
Article
A Cyber-Physical-Human System for One-to-Many UAS Operations: Cognitive Load Analysis
by Lars J. Planke, Yixiang Lim, Alessandro Gardi, Roberto Sabatini, Trevor Kistan and Neta Ezer
Sensors 2020, 20(19), 5467; https://doi.org/10.3390/s20195467 - 23 Sep 2020
Cited by 11 | Viewed by 3623
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
(This article belongs to the Special Issue Cyber-Physical Systems for Automated Decision Making and Trusted Autonomy)
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