A methodology for implementation of mobile robot in adaptive manufacturing environments

Nielsen, Izabela; Dang, Quang-Vinh; Bocewicz, Grzegorz; Banaszak, Zbigniew

doi:10.1007/s10845-015-1072-2

Cited by 81 publications

(34 citation statements)

References 35 publications

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“…The majority of works in this field of research deal with scheduling to determine the best possible strategies for robot movement (Kats and Levner 2009;Ivanov et al 2016Ivanov et al , 2018bSethi et al 1992) . Nielsen et al (2017) assessed the implementation of AMRs in adaptive manufacturing environments, evaluating schedule modification in the mixed-integer programming (MIP) model proposed by Dang et al (2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

et al. 2020

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Manufacturing flexibility improves a firm's ability to react in timely manner to customer demands and to increase production system productivity without incurring excessive costs and expending an excessive amount of resources. The emerging technologies in the Industry 4.0 era, such as cloud operations or industrial Artificial Intelligence, allow for new flexible production systems. We develop and test an analytical model for a throughput analysis and use it to reveal the conditions under which the autonomous mobile robots (AMR)-based flexible production networks are more advantageous as compared to the traditional production lines. Using a circular loop among workstations and inter-operational buffers, our model allows congestion to be avoided by utilizing multiple crosses and analyzing both the flow and the load/unload phases. The sensitivity analysis shows that the cost of the AMRs and the number of shifts are the key factors in improving flexibility and productivity. The outcomes of this research promote a deeper understanding of the role of AMRs in Industry 4.0-based production networks and can be utilized by production planners to determine optimal configurations and the associated performance impact of the AMR-based production networks in as compared to the traditionally balanced lines. This study supports the decision-makers in how the AMR in production systems in process industry can improve manufacturing performance in terms of productivity, flexibility, and costs.

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Section: Literature Reviewmentioning

confidence: 99%

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

et al. 2020

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“…Industrial robot is a re-programmable and multi-useful administrator, intended to move materials, items, items and devices through modified movements. It might shift in like manner with the errand, which additionally get significant data and move cleverly accordingly [26], [27].…”

Section: Industrial Automationmentioning

confidence: 99%

A Self Configurable Edge Computing for Industrial IoT

Chandramohan¹,

Senthilkumaran²

2019

IJEAT

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The proliferation of Industrial Internet of Things (IIoT) introduces the concept of a smarter production environment. The emerging technologies like software defined network (SDN), IIoT and cloud computing will bring great advancements in the modern industrial revolution called Industry 4.0. Therefore, with the rapid development of IIoT technology, the proposed work incorporates with Edge Computing (EC). The current manufacturing process and automation, computing and wireless network reaches out to headways in innovation from easy to the point where all things (devices) and machines can interface through an Internet of Everything (IoE). This paper extends the work carried out in traditional methods, by integrating the cloud layer, Automatic Guided Vehicles (AGV), Industrial Wireless networks (IWN) and Industrial robots through EC is conferred to make autonomous decision-making capabilities. EC is emerging as a significant element in the smart industry to bring legacy in the context of Industrial IoT (IIoT). Finally, our proposed framework demonstrates that the active RFID-enabled AGV and industrial robots are brought in to exploit for effective resource management under the EC-based IIoT architecture, subsequently, it improves the conveyor efficiency and overall energy consumption in the warehouse for material handling.

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“…Hence this stream has received a significant attention from both researchers and industries seeking robots' design improvement. The development of green-aware self-reconfigurable robots is a challenging task Nielsen et al (2017). That means, unleashing the capabilities of self-reconfigurable robots to achieve green-aware manufacturing implies a process including several stages, implementation activities, and principles for design and development of robots or reengineering existing ones.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

A study of influential factors in designing self-reconfigurable robots for green manufacturing

Fahmideh

Lammers

2018

Australasian Conference on Information Systems 2018

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There is incremental growth in adopting self-reconfigurable robots in automating manufacturing conventional product lines. Using this class of robots adapting themselves with ever-changing environmental conditions has been acclaimed as a promising way of reducing energy consumption and environmental impact and thus enabling green manufacturing. Whilst the majority of existing research focuses on highlighting the efficacy of self-reconfigurable robots in energy reduction with technical driven solutions, the research on exploring the salient factors in design and development selfreconfigurable robots that directly enable or hinder green manufacturing is non-extant. This interdisciplinary research contributes to the nascent body of the knowledge by empirical investigation of design-time, run-time, and hardware aspects which should be contingently balanced when developing green-aware self-reconfigurable robots.

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A methodology for implementation of mobile robot in adaptive manufacturing environments

Cited by 81 publications

References 35 publications

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

A Self Configurable Edge Computing for Industrial IoT

A study of influential factors in designing self-reconfigurable robots for green manufacturing

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