Effiziente Aufgaben-Allokation  in Roboter-Teams

With the advent of Industry 4.0 era, employing a team of robots within a factory floor or a warehouse is pretty prevalent today as robots can perform a known task with higher accuracy and efficiency if its capability permits. Efficiency and throughput of such a setup depend on careful task assignment and scheduling, which further depend on utility calculation. Though there exists a number of techniques to perform efficient task allocation, they assume the utility values are available and static. They neither consider all the relevant parameters nor the dynamic changes that may occur during task execution. Moreover, methods of automating such dynamic utility calculation (both at the start and at runtime) based on knowledge and semantics are not present and this is a hindrance to building a fully automated robotic workforce. In this article, we explore an avenue of semantic-based dynamic utility calculation and showcase its application for a use-case.

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A Moroccan Leading Use Case for Predictive Maintenance, IoT and Industry 4.0

SSRN Electronic Journal ◽

10.2139/ssrn.3638898 ◽

2020 ◽

Author(s):

Abdenour Jbili ◽

Mounir Lahlimi

Keyword(s):

Industry 4.0 ◽

Predictive Maintenance ◽

Use Case

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A multi-responsibility–oriented coalition formation framework for dynamic task allocation in mobile–distributed multi-agent systems

International Journal of Advanced Robotic Systems ◽

10.1177/1729881418813037 ◽

2018 ◽

Vol 15 (6) ◽

pp. 172988141881303 ◽

Cited By ~ 1

Author(s):

Bing Xie ◽

Xueqiang Gu ◽

Jing Chen ◽

LinCheng Shen

Keyword(s):

Coalition Formation ◽

Task Allocation ◽

Game Model ◽

Multi Agent Systems ◽

Coalition Game ◽

Task Execution ◽

Agent Systems ◽

Dynamic Task ◽

Dynamic Task Allocation ◽

Multi Agent

In this article, we study a problem of dynamic task allocation with multiple agent responsibilities in distributed multi-agent systems. Agents in the research have two responsibilities, communication and task execution. Movements in agent task execution bring changes to the system network structure, which will affect the communication. Thus, agents need to be autonomous on communication network reconstruction for good performance on task execution. First, we analyze the relationships between the two responsibilities of agents. Then, we design a multi-responsibility–oriented coalition formation framework for dynamic task allocation with two parts, namely, task execution and self-adaptation communication. For the former part, we integrate our formerly proposed algorithm in the framework for task execution coalition formation. For the latter part, we develop a constrained Bayesian overlapping coalition game model to formulate the communication network. A task-allocation efficiency–oriented communication coalition utility function is defined to optimize a coalition structure for the constrained Bayesian overlapping coalition game model. Considering the geographical location dependence between the two responsibilities, we define constrained agent strategies to map agent strategies to potential location choices. Based on the abovementioned design, we propose a distributed location pruning self-adaptive algorithm for the constrained Bayesian overlapping coalition formation. Finally, we test the performance of our framework, multi-responsibility–oriented coalition formation framework, with simulation experiments. Experimental results demonstrate that the multi-responsibility oriented coalition formation framework performs better than the other two distributed algorithms on task completion rate (by over 9.4% and over 65% on average, respectively).

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Integration of Cutting-Edge Interoperability Approaches in Cyber-Physical Production Systems and Industry 4.0

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Design, Applications, and Maintenance of Cyber-Physical Systems ◽

10.4018/978-1-7998-6721-0.ch007 ◽

2021 ◽

pp. 144-172

Author(s):

Luis Alberto Estrada-Jimenez ◽

Terrin Pulikottil ◽

Nguyen Ngoc Hien ◽

Agajan Torayev ◽

Hamood Ur Rehman ◽

...

Keyword(s):

Industry 4.0 ◽

Production Systems ◽

Heterogeneous Systems ◽

Smart Manufacturing ◽

Use Case ◽

Integrative Framework ◽

Data Formats ◽

Exchange Information ◽

Cyber Physical Production Systems ◽

Physical Components

Interoperability in smart manufacturing refers to how interconnected cyber-physical components exchange information and interact. This is still an exploratory topic, and despite the increasing number of applications, many challenges remain open. This chapter presents an integrative framework to understand common practices, concepts, and technologies used in trending research to achieve interoperability in production systems. The chapter starts with the question of what interoperability is and provides an alternative answer based on influential works in the field, followed by the presentation of important reference models and their relation to smart manufacturing. It continues by discussing different types of interoperability, data formats, and common ontologies necessary for the integration of heterogeneous systems and the contribution of emerging technologies in achieving interoperability. This chapter ends with a discussion of a recent use case and final remarks.

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Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case

Journal of Manufacturing Systems ◽

10.1016/j.jmsy.2019.11.004 ◽

2020 ◽

Vol 54 ◽

pp. 138-151 ◽

Cited By ~ 26

Author(s):

Radhya Sahal ◽

John G. Breslin ◽

Muhammad Intizar Ali

Keyword(s):

Big Data ◽

Industry 4.0 ◽

Stream Processing ◽

Predictive Maintenance ◽

Use Case

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Comparing Approaches for Evaluating Digital Interventions on the Shop Floor

Technologies ◽

10.3390/technologies6040116 ◽

2018 ◽

Vol 6 (4) ◽

pp. 116 ◽

Cited By ~ 1

Author(s):

Francisco Lacueva-Pérez ◽

Lea Hannola ◽

Jan Nierhoff ◽

Stelios Damalas ◽

Soumyajit Chatterjee ◽

...

Keyword(s):

Industry 4.0 ◽

Shop Floor ◽

Use Case ◽

Individual Level ◽

Ict System ◽

The One ◽

Comparison Of The Results ◽

Future Work ◽

Self Learning ◽

Smart Factories

The introduction of innovative digital tools for supporting manufacturing processes has far-reaching effects at an organizational and individual level due to the development of Industry 4.0. The FACTS4WORKERS project funded by H2020, i.e., Worker-Centric Workplaces in Smart Factories, aims to develop user-centered assistance systems in order to demonstrate their impact and applicability at the shop floor. To achieve this, understanding how to develop such tools is as important as assessing if advantages can be derived from the ICT system created. This study introduces the technology of a workplace solution linked to the industrial challenge of self-learning manufacturing workplaces. Subsequently, a two-step approach to evaluate the presented system is discussed, consisting of the one used in FACTS4WORKERS and the one used in the “Heuristics for Industry 4.0” project. Both approaches and the use case are introduced as a base for presenting the comparison of the results collected in this paper. The comparison of the results for the presented use case is extended with the results for the rest of the FACTS4WORKERS use cases and with future work in the framework.

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A new load balancing strategy by task allocation in edge computing based on intermediary nodes

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1624-9 ◽

2020 ◽

Vol 2020 (1) ◽

Cited By ~ 1

Author(s):

Guangshun Li ◽

Yonghui Yao ◽

Junhua Wu ◽

Xiaoxiao Liu ◽

Xiaofei Sheng ◽

...

Keyword(s):

Load Balancing ◽

Real Time ◽

Task Allocation ◽

Normal Load ◽

Task Assignment ◽

Edge Computing ◽

Heavy Load ◽

Light Load ◽

Assignment Model ◽

Classification Evaluation

AbstractThe latency of cloud computing is high for the reason that it is far from terminal users. Edge computing can transfer computing from the center to the network edge. However, the problem of load balancing among different edge nodes still needs to be solved. In this paper, we propose a load balancing strategy by task allocation in edge computing based on intermediary nodes. The intermediary node is used to monitor the global information to obtain the real-time attributes of the edge nodes and complete the classification evaluation. First, edge nodes can be classified to three categories (light-load, normal-load, and heavy-load), according to their inherent attributes and real-time attributes. Then, we propose a task assignment model and allocate new tasks to the relatively lightest load node. Experiments show that our method can balance load among edge nodes and reduce the completion time of tasks.

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The Use of Artificial Intelligence in Industry 4.0

Problems of Engineering Cybernetics and Robotics ◽

10.7546/pecr.73.20.02 ◽

2020 ◽

Vol 73 ◽

Author(s):

Maksim Sharabov ◽

Georgi Tsochev

Keyword(s):

Artificial Intelligence ◽

Computer Vision ◽

Industry 4.0 ◽

New Technologies ◽

Web Of Science ◽

Predictive Analytics ◽

Use Case ◽

Industrial Robotics ◽

The Past ◽

New Generation

This article presents a brief overview of the effect of new technologies, how they are changing the manufacturing process, and how the machines are starting to get a lot smarter thanks to the artificial intelligence. The focus is over the examination of Industry 4.0 and how it revolutionized the whole manufacturing segment and what promise of a better, more efficient future it brings. This analysis focuses primarily on how artificial intelligence is integrated, what benefits it brings, and how big of an improvement it is over basic programming. Part of the research is based on 771 publications tracked over the past three to five years. Publications are within some of the well-known databases Scopus, Web of Science, and IEEE. We will examine the basic use case scenarios where AI is crucially needed and how a new generation of the factory can look and feel like a living human being. Keywords: Industry 4.0, artificial intelligence, predictive analytics, predictive maintenance, industrial robotics, computer vision.

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