The TAPAS Project: Facilitating Cooperation in Hybrid Combat Air Patrols Including Autonomous UCAVs

Rauffet, Philippe; Lassalle, Julie; Leroy, Baptiste; Coppin, Gilles; Chauvin, Christine

doi:10.1016/j.promfg.2015.07.152

Cited by 14 publications

(8 citation statements)

References 5 publications

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“…In these cases, humans usually adopt a passive control role (supervision; Rauffet et al. ). These applications are at the doorstep of industrial and real‐world application, in production (autonomously moving robots with human interaction), traffic (autonomous cars and trucks) as well as health care (surgery as well as care robotics).…”

Section: Theory Framework Developmentmentioning

confidence: 99%

“…AI autonomy: Finally, AI applications are taking a multitude of differentiated decisions, leading to autonomous behavior, for instance, when actively steering cars and trucks for longer periods and in interaction with other road users. In these cases, humans usually adopt a passive control role (supervision; Rauffet et al 2015). These applications are at the doorstep of industrial and real-world application, in production (autonomously moving robots with human interaction), traffic (autonomous cars and trucks) as well as health care (surgery as well as care robotics).…”

Section: Human-machine Cooperation In Logisticsmentioning

confidence: 99%

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Logistics Innovation and Social Sustainability: How to Prevent an Artificial Divide in Human–Computer Interaction

Klumpp

Zijm

2019

J of Business Logistics

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Human–computer interaction (HCI) is a cornerstone for the success of technical innovation in the logistics and supply chain sector. As a major part of social sustainability, this interaction is changing as artificial intelligence applications (Internet of Things, autonomous transport, Physical Internet) are implemented, leading to larger machine autonomy, and hence the transition from a primary executive to a supervisory role of human operators. A fundamental question concerns the level of control transferred to machines, such as autonomous vehicles and automatic materials handling devices. Problems include a lack of human trust toward automatic decision making or an inclination to override the system in case automated decisions are misperceived. This paper outlines a theoretical framework, describing different levels of acceptance and trust as a key HCI element of technology innovation, and points to the possible danger of an artificial divide at both the individual and firm level. Based upon the findings of four benchmark cases, a classification of the roles of human employees in adopting innovations is developed. Measures at operational, tactical, and strategic level are discussed to improve HCI, more in particular the capacity of individuals and firms to apply state‐of‐the‐art techniques and to prevent an artificial divide, thereby increasing social sustainability.

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Section: Theory Framework Developmentmentioning

confidence: 99%

Section: Human-machine Cooperation In Logisticsmentioning

confidence: 99%

Logistics Innovation and Social Sustainability: How to Prevent an Artificial Divide in Human–Computer Interaction

Klumpp

Zijm

2019

J of Business Logistics

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“…(iii) Level of AI autonomy: In a final stage, AI applications are responsible for a bunch of different decisions, leading to autonomous behavior as for instance in fully automated manufacturing and shop floor environments [29,89]. In these cases, humans usually adopt a passive supervision role [90]. These applications are at the doorstep to industrial and real-world application, in production and warehousing [12,91] or road traffic (fully autonomous cars and trucks).…”

Section: Ai Adoption and Acceptancementioning

confidence: 99%

Production logistics and human-computer interaction—state-of-the-art, challenges and requirements for the future

Klumpp¹,

Hesenius

Meyer

et al. 2019

Int J Adv Manuf Technol

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Current concept, development, and testing applications in production concerning Cyber-Physical Systems (CPS), Industry 4.0 (I40), and Internet of Things (IoT) are mainly addressing fully autonomous systems, fostered by an increase in available technologies regarding distributed decision-making, sensors, and actuators for robotics systems. This is applied also to production logistics settings with a multitude of transport tasks, e.g., between warehousing or material supply stations and production locations within larger production sites as for example in the automotive industry. In most cases, mixed environments where automated systems and humans collaborate (e.g., cobots) are not in the center of analysis and development endeavors although the worker's adoption and acceptance of new technologies are of crucial relevance. From an interdisciplinary research perspective, this constitutes an important research gap, as the future challenges for successful automated systems will rely mainly on human-computer interaction (HCI) in connection with an efficient collaboration between motivated workers, automated robotics, and transportation systems. We develop a HCI efficiency description in production logistics based on an interdisciplinary analysis consisting of three interdependent parts: (i) a production logistics literature review and process study, (ii) a computer science literature review and simulation study for an existing autonomous traffic control algorithm applicable to production logistics Purpose This paper addresses three approaches determined to analyze the crucial role of human interaction in automated environments in production logistics and Industry 4.0 settings. The methods stem from different disciplines as successful automation concepts also have to consider computer science, economics and work science perspectives. Contribution We answer the question of human intuition and its development within a digitalized production logistics setting as well as automated algorithm reaction to human actions from an interdisciplinary perspective. So far, existing research contributions are mainly focusing on technical aspects and automation concepts as solely computer science optimization aspects. However, feasible and sustainable concepts for automated production, e.g., within production transport will only work out if the human factor is included as for a long time to come production environments will be mixed settings of robotics and human workers. Thus, we develop a HCI efficiency description in production logistics for future research and business applications.

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“…AI Autonomy: Finally, AI applications are taking a multitude of differentiated decisions, leading to autonomous behavior as e.g., in actively steering cars and trucks for longer periods and with interaction towards other participants in road traffic. In these cases, humans usually take over a passive controlling role (supervision; [75]). These applications are at the doorstep to industrial and real-world application, in production (autonomously moving robots with human interaction), traffic (autonomous cars and trucks) as well as health care (surgery as well as care robotics).…”

Section: Description Status Quomentioning

confidence: 99%

Innovation Potentials and Pathways Merging AI, CPS, and IoT

Klumpp

2018

ASI

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Recent advances in the areas of Artificial Intelligence (AI) in the informatics field, Cyber-Physical Systems (CPS) in the production field, and Internet of Things (IoT) in the logistics and transportation field have induced a tremendous growth and innovation potential for global value chain setups. The question is not if further innovation and automation will happen but when-sooner than later-and how. Independent of physical production innovations (additive manufacturing) the information integration and decision autonomy tendencies themselves will drive new supply chain and customer interaction designs and business models. This article presents a technology forecast model based on extensive descriptions of developments by field as well as interaction traits. Results suggest that the crucial element in AI and technology application in logistics will be the human factor and human-artificial cooperation capacities and attitudes.

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The TAPAS Project: Facilitating Cooperation in Hybrid Combat Air Patrols Including Autonomous UCAVs

Cited by 14 publications

References 5 publications

Logistics Innovation and Social Sustainability: How to Prevent an Artificial Divide in Human–Computer Interaction

Logistics Innovation and Social Sustainability: How to Prevent an Artificial Divide in Human–Computer Interaction

Production logistics and human-computer interaction—state-of-the-art, challenges and requirements for the future

Innovation Potentials and Pathways Merging AI, CPS, and IoT

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