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The purpose of this study has been to evaluate the technology of autonomous mobile manipulation in a real world industrial manufacturing environment. The objective has been to obtain experience in the integration with existing equipment and determine key challenges in maturing the technology to a level of readiness suitable for industry. Despite much research within the topic of industrial mobile manipulation, the technology has not yet found its way to the industry. To mature the technology to a level of readiness suitable for industry real-world experience is crucial. This paper reports from such a real-world industrial experiment with two mobile manipulators.

In the experiment, autonomous industrial mobile manipulators are integrated into the actual manufacturing environment of the pump manufacturer Grundfos. The two robots together solve the task of producing rotors; a task constituted by several sub-tasks ranging from logistics to complex assembly. With a total duration of 10 days, the experiment includes workspace adaptation, safety regulations, rapid robot instruction and running production.

With a setup time of less than one day, it was possible to program both robots to perform the production scenario in collaboration. Despite the success, the experiment clearly demonstrated several topics in need of further research before the technology can be made available to the industry: robustness and cycle time, safety investigations and possibly standardization, and robot and workstation re-configurability.

Despite the attention of research around the world, the topic of industrial mobile manipulation has only seen a limited number of real-world integrations. This work reports from a comprehensive integration into a real-world running production and thus reports on the key challenges identified from this integration.

The purpose of this paper is to present experience from a real‐world demonstration of autonomous industrial mobile manipulation (AIMM) based on the mobile manipulator “Little Helper” performing multiple part feeding at the pump manufacturer Grundfos A/S.

The necessary AIMM technologies exist at a mature level – the reason that no mobile manipulators have yet been implemented in industrial environments, is that research in the right applications have not been carried out. The paper proposes a pragmatic approach consisting of: a commercial‐off‐the‐shelf (COTS) mobile manipulator system design (“Little Helper”), a suitable and comprehensive industrial application (multiple part feeding), and a general implementation concept for industrial environments (the “Bartender Concept”).

Results from the three days of real‐world demonstration show that “Little Helper” is capable of successfully servicing four part feeders in three production cells using command signals from an Open Process Control (OPC) server. Furthermore, the paper presents future research and development suggestions for AIMM, which contributes to near‐term industrial maturation and implementation.

The paper presents a full‐scale demonstration of a state‐of‐the‐art COTS autonomous mobile manipulator system with particular focus on industrial utilization and application.

The purpose of this paper is to provide a review of the interdisciplinary research field, autonomous industrial mobile manipulation (AIMM), with an emphasis on physical implementations and applications.

Following an introduction to AIMM, this paper investigates the missing links and gaps between the research and developments efforts and the real‐world application requirements, in order to bring the AIMM technology from laboratories to manufacturing environments. The investigation is based on 12 general application requirements for robotics: sustainability, configuration, adaptation, autonomy, positioning, manipulation and grasping, robot‐robot interaction, human‐robot interaction, process quality, dependability, and physical properties.

The concise yet comprehensive review provides both researchers (academia) and practitioners (industry) with a quick and gentle overview of AIMM. Furthermore, the paper identifies key open issues and promising research directions to realize real‐world integration and maturation of the AIMM technology.

This paper reviews the interdisciplinary research field, autonomous industrial mobile manipulation (AIMM).

Scarcity of resources, ecological imbalance, global warming, rising energy prices and the ever-changing need for variety have attracted the government and manufacturers for sustainable development of the industries. The integrated sustainable-green-lean-six sigma-agile manufacturing system (ISGLSAMS) provides a solid platform for meeting both the customers’ variety needs and business sustainability requirements. Many organizations opted for ISGLSAMS, but still due to various barriers organizations are not able to fully implement ISGLSAMS. The purpose of this paper is to identify the barriers to the ISGLSAMS, so that a more sustainable industrial manufacturing system and industrial symbiosis can be developed.

A literature review, from the Web of Science and Google Scholar database, has been carried out to identify the various barriers to the implementation of ISGLSAMS in the entire value chain. A total of 168 research papers have been reviewed for identifying the ISGLSAMS barriers.

This paper elaborates the concept of the ISGLSAMS, its attributes and various barriers and contributes to a better understanding and successful implementation of ISGLSAMS to meet business’ sustainability and market performance goals in the entire value chain. The paper also projects the future research framework and directions for the ISGLSAMS, integrated sustainable-green-lean-six sigma-agile (ISGLSA) product and ISGLSA supply and value chain.

The study contributes to a better understanding of ISGLSAMS’ barriers. The government, stakeholders and policymakers may plan the policy, road map and strategies to overcome the ISGLSAMS’ barriers. In-depth knowledge of subclauses of ISGLSAMS’ barriers will help the practitioners to overcome the ISGLSAMS’ barriers strategically. By overcoming the ISGLSAMS barriers, a more sustainable 7 Rs based market focused manufacturing system can be designed. This will also increase the opportunities to enhance the industrial ecology, industrial symbiosis and better recovery of the product, process and supply chain residual value. This will reduce the waste to the ecosystem.

This work has been carried out in search of a more sustainable manufacturing system, i.e. ISGLSAMS (which is 7 Rs based, i.e. 6 Rs of sustainability with 7th R, reconfiguration) to meet the customer variety needs along with sustainability in the ever-changing customer market. This study adds value to the practitioners to identify and prioritize the ISGLSAMS’ industry-specific barriers and design the solution for the more sustainable development of (1) industries, (2) the industrial symbiosis system and (3) the ISGLSA product, process, system and supply value chain with minimum resource consumption and environmental impact. The research also contributes to the (a) ISGLSAMS (b) ISGLSA supply chain (c) reconfigurable, sustainable and modular products and (d) redesign, recovery and refurbishing of the product to increase the product life cycle.

Discusses experiences on the development and use of horizontal and vertical prototypes. Explains the difference. Resolves that horizontal prototypes can be developed with ′little effort′, but end users are reluctant to become involved in the development process. Contrastingly resolves that vertical prototypes appear to stimulate constructive response. Reasons that developers should be aware of the tacit knowledge which plays an important part in users′ work practices and should be involved early in the development process. Proposes three techniques to meet the requirements – participation, simulation and evaluation.

The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.

In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.

The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.

A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.

The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.

The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.

The present paper aims to demonstrate the potential of integration between industrial robotics and Lean Manufacturing (LM) approach to increase the efficiency of an assembly line.

Based on a case study performed in an Italian company, this paper reports a comparative analysis of the results produced on a line balancing study involving a semi-automated production line, aided by an industrial robot.

The results suggest the possibility of implementing industrial robotics in line balancing studies highlighting efficiency gains and idle reduction. Further, it also addresses some concepts directly related to industry 4.0, such as collaborative robotics, artificial intelligence, and lean automation.

Line balancing studies may include advanced robotics in order to extend traditional lean practices toward Digital LM.

This study adds contributions to the operational excellence literature, demonstrating the symbiosis between industrial robotics and LM practices.