Avionics Human-Machine Interfaces and Interactions for Manned and Unmanned Aircraft

Lim, Yixiang; Gardi, Alessandro; Sabatini, Roberto; Ramasamy, Subramanian; Kistan, Trevor; Ezer, Neta; Vince, Julian; Bolia, Robert S.

doi:10.1016/j.paerosci.2018.05.002

Cited by 82 publications

(57 citation statements)

References 128 publications

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“…A more focus is given on displays of safety critical and function of command and control. A high level description of the functionalities of the remotely piloted aircraft system is given for the real-time decision support system [ 23 ]. Some details are given in the proceedings of the 7th ICSC [ 75 ].…”

Section: Resultsmentioning

confidence: 99%

Augmented Reality Interface for Complex Anatomy Learning in the Central Nervous System: A Systematic Review

Rahmat

Nazir

Khan

et al. 2020

Journal of Healthcare Engineering

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The medical system is facing the transformations with augmentation in the use of medical information systems, electronic records, smart, wearable devices, and handheld. The central nervous system function is to control the activities of the mind and the human body. Modern speedy development in medical and computational growth in the field of the central nervous system enables practitioners and researchers to extract and visualize insight from these systems. The function of augmented reality is to incorporate virtual and real objects, interactively running in a real-time and real environment. The role of augmented reality in the central nervous system becomes a thought-provoking task. Gesture interaction approach-based augmented reality in the central nervous system has enormous impending for reducing the care cost, quality refining of care, and waste and error reducing. To make this process smooth, it would be effective to present a comprehensive study report of the available state-of-the-art-work for enabling doctors and practitioners to easily use it in the decision making process. This comprehensive study will finally summarise the outputs of the published materials associate to gesture interaction-based augmented reality approach in the central nervous system. This research uses the protocol of systematic literature which systematically collects, analyses, and derives facts from the collected papers. The data collected range from the published materials for 10 years. 78 papers were selected and included papers based on the predefined inclusion, exclusion, and quality criteria. The study supports to identify the studies related to augmented reality in the nervous system, application of augmented reality in the nervous system, technique of augmented reality in the nervous system, and the gesture interaction approaches in the nervous system. The derivations from the studies show that there is certain amount of rise-up in yearly wise articles, and numerous studies exist, related to augmented reality and gestures interaction approaches to different systems of the human body, specifically to the nervous system. This research organises and summarises the existing associated work, which is in the form of published materials, and are related to augmented reality. This research will help the practitioners and researchers to sight most of the existing studies subjected to augmented reality-based gestures interaction approaches for the nervous system and then can eventually be followed as support in future for complex anatomy learning.

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Section: Resultsmentioning

confidence: 99%

Augmented Reality Interface for Complex Anatomy Learning in the Central Nervous System: A Systematic Review

Rahmat

Nazir

Khan

et al. 2020

Journal of Healthcare Engineering

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“…Several studies revealed that heart and respiratory parameters are reliable measures of the operator’s cognitive states. Table 6 summarises the various cardiorespiratory variables that used to estimate cognitive states [3,33,34,35,36]. Arrows represent the changes of the variables when there is an augmentation of the cognitive states; an increase (↑) or decrease (↓), and dashes (-) present an uncertain/negligible effect.…”

Section: Neurophysiological Sensorsmentioning

confidence: 99%

“…In terms ofair traffic, advanced CNS+A systems will support the transition from the two-pilot flight crews to a single pilot in commercial transport aircraft, 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, following the so-called One-to-Many (OTM) approach [3]. Figure 1 schematically illustrates these important evolution paths.…”

Section: Introductionmentioning

confidence: 99%

Sensor Networks for Aerospace Human-Machine Systems

Pongsakornsathien

Lim

Gardi

et al. 2019

Sensors

Self Cite

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Intelligent automation and trusted autonomy are being introduced in aerospace cyber-physical systems to support diverse tasks including data processing, decision-making, information sharing and mission execution. Due to the increasing level of integration/collaboration between humans and automation in these tasks, the operational performance of closed-loop human-machine systems can be enhanced when the machine monitors the operator’s cognitive states and adapts to them in order to maximise the effectiveness of the Human-Machine Interfaces and Interactions (HMI2). Technological developments have led to neurophysiological observations becoming a reliable methodology to evaluate the human operator’s states using a variety of wearable and remote sensors. The adoption of sensor networks can be seen as an evolution of this approach, as there are notable advantages if these sensors collect and exchange data in real-time, while their operation is controlled remotely and synchronised. This paper discusses recent advances in sensor networks for aerospace cyber-physical systems, focusing on Cognitive HMI2 (CHMI2) implementations. The key neurophysiological measurements used in this context and their relationship with the operator’s cognitive states are discussed. Suitable data analysis techniques based on machine learning and statistical inference are also presented, as these techniques allow processing both neurophysiological and operational data to obtain accurate cognitive state estimations. Lastly, to support the development of sensor networks for CHMI2 applications, the paper addresses the performance characterisation of various state-of-the-art sensors and the propagation of measurement uncertainties through a machine learning-based inference engine. Results show that a proper sensor selection and integration can support the implementation of effective human-machine systems for various challenging aerospace applications, including Air Traffic Management (ATM), commercial airliner Single-Pilot Operations (SIPO), one-to-many Unmanned Aircraft Systems (UAS), and space operations management.

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“…Levulis 等 [5] 采用多模态交互方法对无人 -有人直升机编队进行控制, 取得了比传统交互手段更加出色的效果. Lim 等 [6] 总结了目前有代表性的无人 -有人机系统交互界面, 并指出认知交互是无人 -有人机系统交互技术的未来发展方向. 随着无人机系统技术的发展, 人们开始对无人机系统赋予越来越多的信任, 无人 -有人机系统能够参与到越来越多的任务类型中, 人机协同方面的研究开始得到普遍重视.…”

Section: 发展现状unclassified

Key scientific problems in cooperation control of unmanned-manned aircraft systems

Niu¹,

Lin-cheng²,

Li³

et al. 2019

Sci. Sin.-Inf.

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Cooperation of unmanned-manned aircraft systems can accelerate technology adaptability, enhance the combat capabilities in adversary conditions, achieve complementary advantages, and form new and efficient combat systems, which are important for guaranteeing arms control. The key to obtaining the "1 + 1 > 2" combat effectiveness is cooperative control, i.e., autonomous cooperation of unmanned and manned aircraft with the minimum supervision of a pilot. This study provides a survey of the development of unmanned-manned cooperation and a systematic review of the technology system based on the "Observation-Orientation-Decision-Action" (OODA) loop. Our study focuses on multi-aircraft autonomous cooperation in the loop and human-robot intelligent cooperation outside the loop. Finally, we summarize the research focus in the future.

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Avionics Human-Machine Interfaces and Interactions for Manned and Unmanned Aircraft

Cited by 82 publications

References 128 publications

Augmented Reality Interface for Complex Anatomy Learning in the Central Nervous System: A Systematic Review

Augmented Reality Interface for Complex Anatomy Learning in the Central Nervous System: A Systematic Review

Sensor Networks for Aerospace Human-Machine Systems

Key scientific problems in cooperation control of unmanned-manned aircraft systems

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