Human-Centered Gamification Framework for Manufacturing Systems

Ulmer, Jessica; Braun, Sebastian; Cheng, Chi‐Tsun; Dowey, S.J.; Wollert, Jörg F.

doi:10.1016/j.procir.2020.04.076

Cited by 28 publications

(22 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The developed mobile manipulator OMNIVIL is used in a model factory [ 75 ] for experimental purposes. One example product of the model factory is a Lego car (see Figure 12 ), which is produced according to the customers’ requirements (color and model).…”

Section: Control Systemmentioning

confidence: 99%

OMNIVIL—An Autonomous Mobile Manipulator for Flexible Production

Engemann

Kallweit

et al. 2020

Sensors

View full text Add to dashboard Cite

Flexible production is a key element in modern industrial manufacturing. Autonomous mobile manipulators can be used to execute various tasks: from logistics, to pick and place, or handling. Therefore, autonomous robotic systems can even increase the flexibility of existing production environments. However, the application of robotic systems is challenging due to their complexity and safety concerns. This paper addresses the design and implementation of the autonomous mobile manipulator OMNIVIL. A holonomic kinematic design provides high maneuverability and the implemented sensor setup with the underlying localization strategies are robust against typical static and dynamic uncertainties in industrial environments. For a safe and efficient human–robot collaboration (HRC), a novel workspace monitoring system (WMS) is developed to detect human co-workers and other objects in the workspace. The multilayer sensor setup and the parallel data analyzing capability provide superior accuracy and reliability. An intuitive zone-based navigation concept is implemented, based on the workspace monitoring system. Preventive behaviors are predefined for a conflict-free interaction with human co-workers. A workspace analyzing tool is implemented for adaptive manipulation, which significantly simplifies the determination of suitable platform positions for a manipulation task.

show abstract

Section: Control Systemmentioning

confidence: 99%

OMNIVIL—An Autonomous Mobile Manipulator for Flexible Production

Engemann

Kallweit

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…At the time t 10 , O 1 2 starts to be processed on the machine m 1 . At this time, the processing task O 2 2 is triggered and moved from the waiting pool to the scheduling pool.…”

Section: Definition 2 (Maximum Information Utility)mentioning

confidence: 99%

“…With the increasing demand for product customization, enterprise orders are also developing towards diversification and small-batch [1]. The direct involvement of customers in the whole product life cycle significantly increases the uncertainty of production processes.…”

Section: Introductionmentioning

confidence: 99%

“…The rapid progress of Industry 4.0 has led to the following new features in some modern industrial production organizations [2]. (1) The communication capacity and automation level of Production-Logistics Resources are getting higher and higher. Especially, mobile devices such as Automated Guided Vehicles (AGVs) with the networking capacity and computing capacity becoming new production scheduling factors.…”

Section: Introductionmentioning

confidence: 99%

“…In light of the above-described problems, this paper proposes a real-time reaction scheduling framework for production-logistics resources. The real-time reaction scheduling framework includes three parts: (1) The capacity model of production logistics resources is constructed. The multi-attribute of workshop resource is the basis of multi-resource adaptive scheduling.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Information Theory Inspired Real-Time Self-Adaptive Scheduling for Production-Logistics Resources: Framework, Principle, and Implementation

Yang

Cao

et al. 2020

Sensors

View full text Add to dashboard Cite

The development of industrial-enabling technology, such as the industrial Internet of Things and physical network system, makes it possible to use real-time information in production-logistics scheduling. Real-time information in an intelligent factory is random, such as the arrival of customers’ jobs, and fuzzy, such as the processing time of Production-Logistics Resources. Besides, the coordination of production and logistic resources in a flexible workshop is also a hot issue. The availability of this information will enhance the quality of making scheduling decisions. However, when and how to use this information to realize the adaptive collaboration of Production-Logistics Resources are vital issues. Therefore, this paper studies the above problems by establishing a real-time reaction scheduling framework of Production-Logistics Resources dynamic cooperation. Firstly, a real-time task triggering strategy to maximize information utilization is proposed to explore when to use real-time information. Secondly, a collaborative method for Production-Logistics Resources is studied to explore how to use real-time information. Thirdly, a real-time self-adaptive scheduling algorithm based on information entropy is utilized to obtain a stable and feasible solution. Finally, the effectiveness and advancement of the proposed method are verified by a practical case.

show abstract