Recently, the concept of human-robot collaboration has raised many research interests. Instead of robots replacing human workers in workplaces, human-robot collaboration allows human workers and ...robots working together in a shared manufacturing environment. Human-robot collaboration can release human workers from heavy tasks with assistive robots if effective communication channels between humans and robots are established. Although the communication channels between human workers and robots are still limited, gesture recognition has been effectively applied as the interface between humans and computers for long time. Covering some of the most important technologies and algorithms of gesture recognition, this paper is intended to provide an overview of the gesture recognition research and explore the possibility to apply gesture recognition in human-robot collaborative manufacturing. In this paper, an overall model of gesture recognition for human-robot collaboration is also proposed. There are four essential technical components in the model of gesture recognition for human-robot collaboration: sensor technologies, gesture identification, gesture tracking and gesture classification. Reviewed approaches are classified according to the four essential technical components. Statistical analysis is also presented after technical analysis. Towards the end of this paper, future research trends are outlined.
•A concise account of Industry 4.0 and Industry 5.0 is given.•Questions are raised about the situation of Industry 4.0 and Industry 5.0 co-existing.•Discussions, guided by the questions, are given to ...clarify the two terminologies.
Industry 4.0, an initiative from Germany, has become a globally adopted term in the past decade. Many countries have introduced similar strategic initiatives, and a considerable research effort has been spent on developing and implementing some of the Industry 4.0 technologies. At the ten-year mark of the introduction of Industry 4.0, the European Commission announced Industry 5.0. Industry 4.0 is considered to be technology-driven, whereas Industry 5.0 is value-driven. The co-existence of two Industrial Revolutions invites questions and hence demands discussions and clarifications. We have elected to use five of these questions to structure our arguments and tried to be unbiased for the selection of the sources of information and for the discussions around the key issues. It is our intention that this article will spark and encourage continued debate and discussion around these topics.
•Traced the authentic concept and vision of smart manufacturing and its impact on future manufacturing automation.•Proposed that future manufacturing automation should focus on two fundamental themes ...– personalized-product-based manufacturing process automation and networked self-organizing manufacturing system automation.•Reviewed existing standards for enabling smart manufacturing process and system automation.•Proposed three envisioned future-proofing manufacturing automation scenarios – manufacturing digital thread, self-organizing manufacturing network, and cloud-based manufacturing equipment as service.
Smart manufacturing is arriving. It promises a future of mass-producing highly personalized products via responsive autonomous manufacturing operations at a competitive cost. Of utmost importance, smart manufacturing requires end-to-end integration of intra-business and inter-business manufacturing processes and systems. Such end-to-end integration relies on standards-compliant and interoperable interfaces between different manufacturing stages and systems. In this paper, we present a comprehensive review of the current landscape of manufacturing automation standards, with a focus on end-to-end integrated manufacturing processes and systems towards mass personalization and responsive factory automation. First, we present an authentic vision of smart manufacturing and the unique needs for next-generation manufacturing automation. A comprehensive review of existing standards for enabling manufacturing process automation and manufacturing system automation is presented. Subsequently, focusing on meeting changing demands of efficient production of highly personalized products, we detail several future-proofing manufacturing automation scenarios via integrating various existing standards. We believe that existing automation standards have provided a solid foundation for developing smart manufacturing solutions. Faster, broader and deeper implementation of smart manufacturing automation can be anticipated via the dissemination, adoption, and improvement of relevant standards in a need-driven approach.
•The authors introduced a remote HRC system inspired by the concept of CPS.•The authors designed a remote robot control system and an AR feedback system.•The system can flexibly work in four ...different modes.•The system is implemented and tested in different scenarios.
Collaborative robot's lead-through is a key feature towards human–robot collaborative manufacturing. The lead-through feature can release human operators from debugging complex robot control codes. In a hazard manufacturing environment, human operators are not allowed to enter, but the lead-through feature is still desired in many circumstances. To target the problem, the authors introduce a remote human–robot collaboration system that follows the concept of cyber–physical systems. The introduced system can flexibly work in four different modes according to different scenarios. With the utilisation of a collaborative robot and an industrial robot, a remote robot control system and a model-driven display system is designed. The designed system is also implemented and tested in different scenarios. The final analysis indicates a great potential to adopt the developed system in hazard manufacturing environment.
•Proactive human–robot collaboration as a foreseeable informatics-based cognitive manufacturing.•Inter-collaboration cognition by establishing bi-directional empathy in the execution loop based on a ...holistic understanding of humans and robots’ situations.•Spatio-temporal cooperation prediction by estimating human–robot–object interaction of hierarchical sub-tasks/activities over time for the proactive planning.•Self-organizing teamwork by converging knowledge of distributed HRC systems for self-organization learning and task allocation.
Human–robot collaboration (HRC) has attracted strong interests from researchers and engineers for improved operational flexibility and efficiency towards mass personalization. Nevertheless, existing HRC development mainly undertakes either human-centered or robot-centered manner reactively, where operations are conducted by following the pre-defined instructions, thus far from an efficient integration of robotic automation and human cognitions. The prevailing research on human-level information processing of cognitive computing, the industrial IoT, and robot learning creates the possibility of bridging the gap of knowledge distilling and information sharing between onsite operators, robots and other manufacturing systems. Hence, a foreseeable informatics-based cognitive manufacturing paradigm, Proactive HRC, is introduced as an advanced form of Symbiotic HRC with high-level cognitive teamwork skills to be achieved stepwise, including: (1) inter-collaboration cognition, establishing bi-directional empathy in the execution loop based on a holistic understanding of humans and robots’ situations; (2) spatio-temporal cooperation prediction, estimating human–robot–object interaction of hierarchical sub-tasks/activities over time for the proactive planning; and (3) self-organizing teamwork, converging knowledge of distributed HRC systems for self-organization learning and task allocation. Except for the description of their technical cores, the main challenges and potential opportunities are further discussed to enable the readiness towards Proactive HRC.
In order to solve the problem of huge energy consumption of building central air conditioning, the water system of building central air conditioning was taken as the research object and the genetic ...algorithm of energy-saving operation and control of building central air conditioning based on MATLAB was designed, so as to build the energy-saving control system model of central air conditioning. On the one hand, the feasibility of the optimized algorithm to solve the energy-saving problem of central air conditioning was proved through the optimization of genetic algorithm. On the other hand, the energy-saving effect was demonstrated through the operation parameters and the variation of air-conditioning energy consumption before and after the experiment. The feasibility and rationality of genetic algorithm to improve the energy-saving effect of central air conditioning was verified by the comparison of the experiment. The results showed that in the design load range of 30%–70%, the average energy-saving rate of central air-conditioning compressor was 16.8%. The maximum energy-saving efficiency was 37.4%, and the minimum energy-saving efficiency was 15.5%. The average energy-saving rate of water system was 26.8%. Therefore, the genetic algorithm had fast convergence speed and high solving accuracy, which was beneficial to improve the energy-saving ability of central air-conditioning water system.
Timely context awareness is key to improving operation efficiency and safety in human-robot collaboration (HRC) for intelligent manufacturing. Visual observation of human workers’ motion provides ...informative clues about the specific tasks to be performed, thus can be explored for establishing accurate and reliable context awareness. Towards this goal, this paper investigates deep learning as a data driven technique for continuous human motion analysis and future HRC needs prediction, leading to improved robot planning and control in accomplishing a shared task. A case study in engine assembly is carried out to validate the feasibility of the proposed method.
Cloud-based design manufacturing (CBDM) refers to a service-oriented networked product development model in which service consumers are enabled to configure, select, and utilize customized product ...realization resources and services ranging from computer-aided engineering software to reconfigurable manufacturing systems. An ongoing debate on CBDM in the research community revolves around several aspects such as definitions, key characteristics, computing architectures, communication and collaboration processes, crowdsourcing processes, information and communication infrastructure, programming models, data storage, and new business models pertaining to CBDM. One question, in particular, has often been raised: is cloud-based design and manufacturing actually a new paradigm, or is it just “old wine in new bottles”? To answer this question, we discuss and compare the existing definitions for CBDM, identify the essential characteristics of CBDM, define a systematic requirements checklist that an idealized CBDM system should satisfy, and compare CBDM to other relevant but more traditional collaborative design and distributed manufacturing systems such as web- and agent-based design and manufacturing systems. To justify the conclusion that CBDM can be considered as a new paradigm that is anticipated to drive digital manufacturing and design innovation, we present the development of a smart delivery drone as an idealized CBDM example scenario and propose a corresponding CBDM system architecture that incorporates CBDM-based design processes, integrated manufacturing services, information and supply chain management in a holistic sense.
•We present a new paradigm in digital manufacturing and design innovation, namely cloud-based design and manufacturing (CBDM).•We identify the common key characteristics of CBDM.•We define a requirement checklist that any idealized CBDM system should satisfy.•We compare CBDM with other relevant but more traditional collaborative design and distributed manufacturing systems.•We describe an idealized CBDM application example scenario.
This article applies multiple nonlinear regression methods to establish a forecasting model for the load characteristics of air conditioning in shopping malls at different times. Based on Python ...data, determine the functional relationship of refrigerant parameters concerning pressure and temperature. The article uses kernel smoothing estimation technology to calculate the room temperature probability density distribution of users participating in DLC to characterize the user’s comfort. The article’s research results show that the average error between the regression analysis results of refrigerant parameters and the reference value is within 1%. This model is suitable for medium and long-term load forecasting. It has high prediction accuracy for the sudden change trend with a turning point.
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