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Anthropocentric Approach for Smart Assembly: Integration and Collaboration

Anthropocentric Approach for Smart Assembly: Integration and Collaboration Hindawi Journal of Robotics Volume 2019, Article ID 3146782, 8 pages https://doi.org/10.1155/2019/3146782 Review Article Anthropocentric Approach for Smart Assembly: Integration and Collaboration Qingmeng Tan, Yifei Tong , Shaofeng Wu, and Dongbo Li School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China Correspondence should be addressed to Yifei Tong; tyf51129@aliyun.com Received 29 October 2018; Accepted 30 December 2018; Published 3 February 2019 Academic Editor: Gordon R. Pennock Copyright © 2019 Qingmeng Tan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Recently, anthropocentric or human-centric approaches renewed its importance in smart manufacturing especially for assembly applications where human dexterity and informal knowledge are dispensable at present and in the near future. This paper analyzes the integration, design, and collaboration issues regarding anthropocentric researches in the past few years mainly in assembly domain. First of all, towards closed-loop system integration, the researches on integrating human in the cyber-physical system are elaborated and summarized. en, Th human-centric designs (HCD) es pecially in shop-floor assembly field are analyzed. To emphasize human-robot hybrid assembly, several related issues including collaboration paradigm, task planning and assignment, interaction interfaces, and safety are discussed. At last, a survey on human cognitive and social limitation management is elaborated. Related research challenges and directions are discussed. 1. Introduction humans’ dexterity, informal expertise, and tacit knowledge are indispensable [2]. Furthermore, introducing novel tech- In recent years, as innovations led to high usage of automation nologies makes manufacturing system much more compli- and information technologies in industries, from Industry cated. eTh refore, highly skilled, well-trained people are more important to make systems sustainable and resilient [2]. 4.0, Smart Plant to “Dark Factory”, lots of people believed that there would be less humans involved in the man- Human roles in complex manufacturing system have ufacturing sectors in the future. Consequently, industrial not been a new issue since the introduction of automa- researches mainly focused on the development of manu- tion [3]. The anthropocentric approach changes the ques- facturing system with enabling novel technologies, includ- tion from how to replace humans to how to better com- ing Internet of Things, Big Data, Cyber-Physical Systems, plement and assist humans. In the European Factories Machine Learning, Additive Manufacturing, and Robotics of the Future Research Association (EFFRA) Roadmap [1]. To some extent, humans once as key elements in 2020, human-centricity is prerequisite to cope with social challenges. Anthropocentric or human-centric (human- production system such as assembly lines are somehow neglected. centered) approaches are getting more and more attention in Apparently, industrial robots have made great progress in the past few years [4–6]. Based on the academic publications, replacing repetitive, monotonous tasks including handling, this paper focuses on the recent researches towards anthro- sorting, and welding. However, with the applications and pocentric manufacturing approaches for smart assembly implementations of new manufacturing paradigms, many system including human-CPS integration, human-centric have realized that humans as key roles cannot be easily design, human-robot collaboration, and human limitations replaced by advanced technologies at least in the foreseeable management. future. Particularly in the efi ld of assembly, a great amount of eTh rest of the paper organized as follows: Section 2 tasks are not simple routine work but subtle operations where discusses the integration of human in cyber-physical system. 2 Journal of Robotics The framework of human-in-the-loop CPS (HCPS) is pre- From the perspective of anthropocentric approach, CPS sented. Section 3 reviews the human-centric design in the may impact or improve the health, learning, and operative shop-floor level. In Section 4, human-robot collaboration in performance of human workers [15]. Integrating human in hybrid assembly is elaborated, and collaboration paradigms, CPS framework will make manual operations more secure task plan and assignment, immersed vision based inter- and ecffi ient which will also enhance process control and action interfaces, and human safety issues are reviewed. improve quality especially in product assembly domain Section 5 analyzes the human limitations towards manufac- [16]. A framework called “CyFL-Matrix” which can help turing requirements in future generation. Section 6 discusses both industrial stakeholders and researchers to navigate the challenges and concludes the research. operational and social sustainability performance impacts of improvement projects towards HCPS is proposed in [15]. An adaptive human-centric reference architecture for 2. Integrating Human in automation system is proposed in [17], in which machines Cyber-Physical System and automation systems adapt to the cognitive and physical Cyber-physical system (CPS) is one of the most important demands of humans in a momentary and dynamic manner. enabling technologies in Industry 4.0 era. CPS is a closed- Anthropocentric CPS represents the trend of CPS both looped system which integrates dynamic physical processes objectively and methodologically. However, compared to traditional manufacturing environments, the degree of com- with communication, controlling, computing, and other novel information technologies [7]. Despite different angles plexity dramatically increases among all domains of HCPS of den fi itions and interpretations, the core characteristics [18]. For one thing, human behaviors are hard to be regulated and anticipated, causing disturbances and exceptions in of CPS are the same, including the connection between cyber and physical world, real-time data exchange, and the control loop, which further influence the stability and bidirectional information flows with closed loops [8]. In harmony of HCPS. To enhance the resilience of HCPS, these manufacturing domain, CPS are sometimes referred to CPPS dissonance caused by human behaviors must be interpreted, (Cyber-Physical Production System) [9]. modelled, and managed [19]. For another, human role in HCPS alters as application domain changes from design, Generally, there are two major development routes of CPS [10], namely, “technocentric” and “anthropocentric” operation, maintenance, and service. er Th efore, from deci- approaches. In “technocentric” route, manufacturing process sional level to operational level, or both, the corresponding reference framework should be established to model such is determined by enabling technologies of CPS, such as advanced robots and machine tools, Artificial Intelligence variations. (AI), and Digital Twins (DW). Under this circumstance, the autonomy of skilled workers is limited. While in “anthro- 3. Human-Centered Design for pocentric”scenario,ontheonehand, theskilledworkers Shop-Floor Assembly guide the implementation of CPS, on the other hand, CPS supports the decision-making process of skilled workers. As briefed in the last section, the design of smart manu- Introducing “anthropocentric” CPS in smart factories facturing system favored a technocentric approach which will result in mutual transitions from human-machine coop- gave priority to the definition and allocation of tasks with eration to active collaboration, which is characterized by automated system and computing resources, only taking cyber-physical-social interactions, knowledge exchange, and human operators into consideration at the end of the design reciprocal learning [11]. Recent researches have renewed the process [20]. This kind of design approach assumes human focus on human either as an operator or supervisor in CPS as “magic” who must behave perfectly when unexpected related manufacturing. Human as a composite factor in the situations happened [21]. However, as the technological CPS defines a new type of CPS-based systems, either called accidents are caused primarily by human errors (63%) [20], Cyber-Physical-Human System (CPHS) [12], Anthropocen- putting human in the control loop of smart manufacturing tric Cyber-Physical System (ACPS) [13], or Human-Cyber- system implies human-centered design (HCD) framework Physical- System (HCPS) [14]. In this article, the abbreviation andmethodtocopewithmoreandmore complexproblems. of HCPS is preferred to symbol human as the center aspect in HCD approach is not only required in human-machine the CPS loop. interactive interfaces but also in shop-floor level system The HCPS can be characterized by the features as inter- design. Shop-floor assembly system design is usually required operability, sociability, flexibility, adaptability, autonomy, etc. when a new manufacturing system is to be build or an [13]. In HCPS [14], intelligent machines are supposed to existing one needs to be renewed. By placing the human in replace a great portion of human physical and mental tasks the center of the Industry 4.0 design [22], three perspectives which makes humans more concentrated on creative work. (abstraction, decision-making, and innovativeness) as well as From HPS (Human-Physical system), CPS to HCPS, humans two components (human and CPS) constructed the analysis transfer parts of cognitive and learning work to cyber system, and design framework. er Th efore, a set of key performance whichenablethecybersystemwiththeabilityto“cognizeand indicators (KPI) can be introduced to analyze and assess learn”. With human-in-the-loop hybrid enhance intelligence, design scheme [22]. the capability of the manufacturing system to cope complex, Taking workplace design for example, current shop-floor uncertain problems will be essentially improved [14]. eTh level industrial robots (machinery) applications separate framework of HCPS is presented in Figure 1. the workplace of human and robots (machinery) cells due Journal of Robotics 3 to safety considerations. In human-centered mode, human will promote the overall performances of assembly systems, operators and robots coexist in the same space to execute especially in Small and Medium-sized Enterprises (SMEs), assembly taskseitherindividuallyorcooperatively[23]. where the balance of productivity, flexibility, and adaptability Towards a fulfilled HCD of workplaces, not only the physical are of high significance [27]. In this case, the hybrid assembly aspects but also the cognitive ergonomic aspects need to be paradigm has risen as a feasible and effective paradigm for considered during the design processes. Concerning the cog- human-robot collaborations. nitive workload related to manufacturing operations at differ- Generally, in hybrid assembly paradigm, manufacturing ent decisional levels, in [24], a human-in-the-loop framework resourcesashumans,robots,sensors,and otherdevices share concerning the design of workplaces was proposed to classify thesameworkspace.Basedonthespace andtimeperspec- the fabrication tasks of production processes according to tives [28]: the classica fi tion of human-robot collaboration can their cognitive complexity. In the ergonomic analyses phase, be categorized as in Figure 2. unlike traditional methods, which only observe operators in the actual running workplace, the impact of design plans 4.1.1. Humans Share Stationary Workplace with Robots but including human actions and reactions can be simulated and Their Activity Time Does Not Overlap. For example, in [29], veriefi dbyimmersedvisionbased approaches.Toapply a an interactive cooperation between human supervisor and HCD approach, the improvement of human posture, stress, the welding robot is presented. eTh human operator is and satisfaction is possible by assessing different setups responsible for teaching the robot by demonstration; the in Mixed Reality (MR) environment [6]. Likewise, in the robot executes the welding process. Because the human design phase, the concept virtual ergonomics [25] applied operatorandtherobotsharethesameworkplacein dieff rent Digital Manufacturing (DM) tools with the ability to insert time periods, the risk of human operator is minimized. digital human models and other virtual resource models in theproductionplant.Theabove approachisable to simu- 4.1.2. Close Collaboration Where Humans and Robots Con- late assembly tasks in workplaces, making human-centered tacted Directly Both in a Shared Workspace and Time. Tra- design possible. ditional industrial robots (mostly multijoint robots) are not Note that HCD simply considers not only the human considered autonomous enough to allow close interaction factor aspects such as workload, posture, and stress but also with humans [23]. Recently, a new type of robots—cobots higher anthropocentric aspects such as human satisfaction (from collaborative robots) is designed for collaboration with [17] and emotion. Both human knowledge and cognitive human operators which allow safely physical human-robot limitations need to be represented, analyzed, and integrated contact. Based on cobots, collaborative human-robot man- in the design phase. Furthermore, HCD method should ufacturing framework for homokinetic joint assembly was be considered through the life cycle of the system. As presented in [30]. In the proposed approach, direct physical the Industry 4.0 solutions proposed by Bosch, the people contact is successfully managed with sensor based control; including designers, workers, and users are the key roles the cobots can rfi stly lighten the burden of the operator in the connected plant supporting decisions based on con- and secondly comply his needs in the latter. A Portable textual digital information, assistance functions, and ability Assembly Demonstration (PAD) system for robots to learn ampliefi r for human operators as well as adaptive workplace complex assembly skills from human was presented in [31]. ergonomics. eTh assembly script generated by PAD is implemented on a Baxter robot. Using a RGB-D camera, motions, tools, and 4. Human-Robot Collaboration for parts in the assembly process can be recognized. Moreover, Hybrid Assembly assembly states are estimated based on the 3D part models created by a 3D scanner. Human-robot collaboration (HRC) concerning the interac- tion, communication, and collaboration between humans 4.1.3. Humans and Robots (Mainly AGVs) Collaborate in a androbotsreceivedalotofattention recently.TheEU project ROBO-PARTNER has promoted a hybrid solution Dynamic Space and Time. In this paradigm, humans are responsible for critical assembly tasks, while AGVs support concerning the safe cooperation of human operators with autonomous and self-learning/adapting robotic systems [26]. the human operators with correct components and materials HRC has a wide range of research topics such as safety, in the correct time. eTh human operators may move from one assembly station to another followed by AGVs with necessary learning-by demonstrations, imitation learning, and cogni- tive systems. This section focuses on the human-centered resources. In some applications, AGV and multijoint cobot are integrated together not only for transportation but also approach mainly for shop-floor level assembly, covering for assisting human with picking or sorting abilities, which domains as human-robot collaboration paradigm, task plan- ning and assignment, multimodal interaction interfaces, and isverypracticalinlargescaleproduct assembly orsystem maintenance. interaction safety. Although the introduction of cobots is anticipated as 4.1. eTh Evolution of Human-Robot Collaboration Paradigm. a promising approach to facilitate close human-robot col- Recently, researches have come to realize that the fusion of laboration, the cobot itself still has limitations. First of all, therobot’sprecision,repeatability,strength,anddurancewith theworkloadofcobotsisrelativelylow compared totypical human’s dexterity, perception, intelligence, and flexibility industrial robots. Secondly, cobots’ speed is controlled for 4 Journal of Robotics safety considerations which will decrease the efficiency of the behaviors, unified modal control framework is always needed operation. Last but not least, the working range and accuracy to adapt signal alteration from heterogeneous sensor sources, of cobots are insufficient for some application scenarios. For such as vision, position, and force [39]. However, in actual this reason, while the development of cobots highlights new assembly scenarios, environmental noises are always too big compound materials, mechanical structures and heteroge- to distinguish the verbal commands by humans. While haptic neous sensors to improve accuracy, speed, workload, and and force control are more feasible, the reliability of actual intelligence, traditional robots and HRC paradigms are still usage is always worried by industrial users. In addition, haptic required in various industrial application demands. and force sensors and controllers are generally integrated in the product design of cobots in recent years. er Th efore, in this sector, the immersed visual based interfaces including 4.2. Task Planning and Assignment. In HRC, both human VR (Virtual Reality), AR (Augmented Reality), and wearable and robot can perform assembly tasks within their respective devices are highlighted. abilities. Consequently, task planning and assignment rises VR and AR interactive technologies enable reproduction as an important issue for HRC process control. Proper task of the main characteristics of HRC, highlighting or even assignment plans will increase productivity, maximize system emphasizing particular aspects of the collaboration [30]. performance, and even minimize human operators’ physical Especially, AR can provide digital information for increased as well as cognitive workload at the same time. situational awareness, emphasizing different objects, recom- A task can be decomposed into several subtasks in mending optimized motions, and improving the trust and a hybrid assembly, then the subtasks will be assigned to context-awareness of HRC [40, 41]. Also, AR can be used humans or robots based on criteria concerning their dieff rent as an assistant to generate assembly sequence and visual advantages. Based on dual Generalized Stochastic Petri Net instructions for each assembly steps; therefore, the burden of (GSPN), the assembly task allocation process for human- operators is alleviated [42]. robot coordinated cell manufacturing was modelled in [32], In addition, wearable devices such as smart-watches can and Monte Carlo method and Cost-effectiveness analysis for be developed as assistive means with AR applications in order Multiple-Objective Optimization are proposed for strategy to help operators provide feedback and interact with the AR generation and optimization. In [33], an intelligent decision- system [43]. Also, with sensors and Apps in smart-phones, making algorithm was proposed which allows human-robot smart-watches, and other wearable devices, the position of task allocation in the same workplace, where schedules are humans’ arms and legs is monitored in real-time. er Th efore, automatically generated and evaluated using multiple criteria. eTh result assigned task needs to be automatically the ergonomics effects are able to be evaluated and improved [44]. A human-robot interaction architecture for intuitive transformed into robots and human respectively. Towards seamless human-robot collaboration, a two-level hierarchical control of dual armed robot is proposed in [45]. eTh human representation ofthetaskisproposedin[34]whichcouldbe body gesture can be recognized using Kinect Xbox and directly translated into robotic commands. The assignment mapped to robot control command based on ROS. An AR results are correspondingly generated aer ft assessment and tool is presented in [46] to support human operators in a evaluation in different application scenarios. Based on Robot shared assembly workplace. With immersion capabilities of Operating System (ROS) [35], the assembly sequence data AR technology, not only the assembly processes, status, and exported from the Off-Line Programming (OLP) tools to a instructions are visualized, but also the operator’s safety is neutral XML format, graphical interfaces for human-robot enhanced. In [47], interactive assembly virtual environment tasks coordination is developed with the ability to review the basedonphysicalmodelsisdeveloped,theresultsofassembly previous and upcoming tasks. operations can be displayed in the form of 3D visualiza- Human factors (ergonomics) are closely relative issues tion, and human can correct the obvious errors by HRC which must be considered in task assignments. A multicri- mechanism to ensure the n fi al assembly plan is feasible. An teria approach and algorithm are proposed in [27] to plan the immersed VR environment for large parts assembly process human-robothybridcelllayoutand taskinthesametime. assessment was built in [48] by recording and analyzing In a skill-based task assignment approach proposed in [28], human’s main movement with the help of Oculus and Kinect. with the assembly task description model, the skills of human and robots are compared according to the requirements. 4.4. Interaction Safety. Safety is the most crucial issue when An integrated (feed-forward & feedback) optimum subtask introducing robots to the shop-floor. In the traditional allocation scheme is proposed in [36] for the assembly robot application scenarios, human and industrial robots triggered by two-way trust (human’s trust in robot and are strictly separated with fences. However, accidents still robot’s trust in human). A set of ergonomics, quality, and happened due to misconduct or malfunction. While in HRC, productivity criteria is designed to choose the most suitable the safety issues become more complicated, as the degree plans. of complexity dramatically increases in the operation and maintenance domains [18]. 4.3. Immersed Multimodal Interaction Interfaces. Multimodal TomakeHRCsafetyavailable,multiplesensorsneedtobe interaction interfaces including visual guidance [37], voice integrated to avoid or prevent collisions either externally or commands [38], and haptic and force control are popular internally. In external sensors based safety model, visual sen- research topics in HRC. Due to the unpredictability of human sors and motion detectors are integrated into robots’ control Journal of Robotics 5 Human Experience sharing Mixed AI Cognition Enforcement Cyber Knowledge Graph Supervising Assisting Artificial intelligence Operating Amplifying Monitoring Cloud Computing Digital Twin Resources Dynamic Sensing and Controlling Physical Figure1:The frameworkofintegrating humaninthe CPSloop. Figure 2: HRC paradigms. system to avoid collision with human. Virtual 3D models potential risks of collisions is introduced in [49]. eTh safe of robots and real camera images of operators for real-time detour trajectory will be automatically generated within one are used for collision avoidance in [37]. With the collision second during assembly process. detection result, the robot can be adaptively controlled in Although the external sensors based model has been human-robot assembly scenarios to keep human operator proved effectively in many researches by demonstrations safe. A 3D ToF (Time of Flight) vision sensor to detect and experiments, the actual industrial implementations are 6 Journal of Robotics quite low. One of the reasons is this kind of safety cannot human have dropped rapidly. However, the cognitive burdens be completely trusted by human in the complex industrial are increasing because of the growing complexity of systems, application areas where even small chance of failures in ever-changing manufacturing requirements, and the frequent sensors and control system may still cause serious damage human-machine interactions. Stress is one major cognitive to humans. Besides the external safety model, the internal limitation. In HRC assembly paradigm, the moving industrial safety will improve the safety level during HRC. eTh internal robots mayinducestressonhumanoperators.Aframework safetyis implementedwhenthecobots aredeveloped,as to integrate human cognitive factors (fatigue, stress, skill new kinematics, materials, and internal sensors are integrated level, emotional state, etc.) is proposed in [53]; the cognitive in the cobots to avoid injury to humans. Safety cobots are layer is presented enabling intelligent response to the changes already used in automobile assembly lines in several leading from the data perceived from the environment. When com- manufacturers [50]. Because the safety is the primary goal in ing near the robots, the stress of human operator rises, which cobots’ design objectives, such safety is inherent and intrinsic, can be measured by skin potential values [54]. Hence, the despite the sacrifices in workload, working radius, and speed. proper parameters and principles can be established for HRC. In recent years, several standards regarding robot safety Aging is another important issue regarding sustainable use are published by ISO, including ISO 10218, ISO/TS 15066. manufacturing. One of trends in Industry 4.0 or smart While ISO 10218 focuses on the standards for safety of manufacturing is that fewer operators have to be employed industrial robots, ISO/TS 15066 provides guidelines for the in manufacturing shop-floors. However, skilled and expe- design and implementation of a collaborative workspace. rienced workers are always required in highly automation Note that the safety factors varies among different robot types, factories to manage and maintain the growing complexity of workloads, powers, geometric shapes, assembly processes, intelligent system. By the year 2025, it has been estimated that multiple aspects of safety which still need to be considered the proportion of working individuals over the age of 50 years and analyzed. will be 32%inEurope, 30%inNorth America, 21%inAsia, and 17% in Latin America. In 2050, around half of workers will be aged over 50 in developed countries [55]. Because 5. Human Limitation Management of the decreasing physical and cognitive capacities of aging workers, including decrease of visual ability, acoustic ability, eTh anthropocentric approaches which put humans in the musculoskeletal force, and motion capabilities, the resilience center also have its challenges. One of the reasons is that to the job workload will reduce correspondingly. Therefore, human has limitation comparing to robots and automation the manufacturing framework must be adaptive to cope with systems. Only by managing both technology and human the aging issue [55]. The adaptive manufacturing can com- limitations can the smart manufacturing system be reliable, pensate the aging limitations to guarantee job performances. flexible, and effective. Furthermore, the senior experts’ knowledge and experience Human limitations are the main causes of human related are needed to improve the learning curve of new operators errors and failures. Categorized from memory, perception to [17]. motion aspect of human, about 16 types of errors are pro- Besides the general limitation management of human posed in [51]. In smart manufacturing environment, although workers, here we argue that the human limitation should be many operations are executed automatically, human errors modelled individually in the future research because each are still of great importance, especially omissions and the incorrect selection of variants. Human failure modes include person has limitations in different aspects and degrees. eTh unique profile of each worker needs to be established by data misuse, false indication, and mode confusion [52]. Based on these modes, a CPS safety analysis and simulation platform mining or big data analysis. Such profile can be extracted as called CP-SAP is developed and a cyber-physical human input to the anthropocentric manufacturing system, in which dynamic fault tree is designed in [52]. In order to access the congfi uration varies respectively for each person in each human errors, human errors probability (HEP) and human context. For example, when a left-handed operator is logging reliability probability (HRP) are proposed as indicators for in, the information display and operation interfaces will therelativeoccurrenceoferrorsandrespectively faultless correspondingly change for this new parameter to achieve actions [51]. eTh den fi ition of the above two concepts is as better anthropocentric performances. follows: 𝑓𝑜 V 6. Discussion and Conclusion HEP= 𝑓𝑜𝑡ℎ𝑒 𝑓 The relation of human and technology in the manufacturing (1) domain has been a concerning issue along with the perva- = , sive applications of advanced automation and information technologies including robots, IoT, and AI. By analyzing HRP=1− HEP the publications and latest research findings, we find that besides the advancement of technology, the “anthropocen- From the ergonomic points of view, the limitations of tric” approaches are attracting more and more attention human in the manufacturing system exist both physically and in the past few years. Many have realized that human cognitively. For the past few years, as automatic machines and robots replaced lots of heavy work, the physical burdens of having a key role should not be replaced or weakened in 𝑒𝑟𝑟𝑜𝑟𝑎𝑛𝑜𝑟𝑖𝑒𝑠𝑖𝑡𝑖𝑙𝑖𝑏𝑝𝑜𝑠𝑠 𝑛𝑢𝑚𝑏𝑒𝑟 𝑒𝑟𝑟𝑜𝑟𝑠𝑒𝑑 𝑒𝑟𝑜𝑏𝑠 𝑛𝑢𝑚𝑏𝑒𝑟 Journal of Robotics 7 smart manufacturing or other new manufacturing paradigm, designing an ergonomic work system,” Procedia Manufacturing, vol. 3, pp. 510–517, 2015. especially in assembly domain. However, that is not to say that the progress of technology implementations should [6] M. Peruzzini, M. Pellicciari, and M. Gadaleta, “A comparative study on computer-integrated set-ups to design human-centred be decelerated. On the contrary, the importance of novel manufacturing systems,” Robotics and Computer-Integrated technologies is more highlighted to better help and assist Manufacturing,vol.55, pp.265–278,2019. human. [7] L.Monostori,B.Kad ´ ar ´ , T. 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Anthropocentric Approach for Smart Assembly: Integration and Collaboration

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Hindawi Publishing Corporation
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Copyright © 2019 Qingmeng Tan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Hindawi Journal of Robotics Volume 2019, Article ID 3146782, 8 pages https://doi.org/10.1155/2019/3146782 Review Article Anthropocentric Approach for Smart Assembly: Integration and Collaboration Qingmeng Tan, Yifei Tong , Shaofeng Wu, and Dongbo Li School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China Correspondence should be addressed to Yifei Tong; tyf51129@aliyun.com Received 29 October 2018; Accepted 30 December 2018; Published 3 February 2019 Academic Editor: Gordon R. Pennock Copyright © 2019 Qingmeng Tan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Recently, anthropocentric or human-centric approaches renewed its importance in smart manufacturing especially for assembly applications where human dexterity and informal knowledge are dispensable at present and in the near future. This paper analyzes the integration, design, and collaboration issues regarding anthropocentric researches in the past few years mainly in assembly domain. First of all, towards closed-loop system integration, the researches on integrating human in the cyber-physical system are elaborated and summarized. en, Th human-centric designs (HCD) es pecially in shop-floor assembly field are analyzed. To emphasize human-robot hybrid assembly, several related issues including collaboration paradigm, task planning and assignment, interaction interfaces, and safety are discussed. At last, a survey on human cognitive and social limitation management is elaborated. Related research challenges and directions are discussed. 1. Introduction humans’ dexterity, informal expertise, and tacit knowledge are indispensable [2]. Furthermore, introducing novel tech- In recent years, as innovations led to high usage of automation nologies makes manufacturing system much more compli- and information technologies in industries, from Industry cated. eTh refore, highly skilled, well-trained people are more important to make systems sustainable and resilient [2]. 4.0, Smart Plant to “Dark Factory”, lots of people believed that there would be less humans involved in the man- Human roles in complex manufacturing system have ufacturing sectors in the future. Consequently, industrial not been a new issue since the introduction of automa- researches mainly focused on the development of manu- tion [3]. The anthropocentric approach changes the ques- facturing system with enabling novel technologies, includ- tion from how to replace humans to how to better com- ing Internet of Things, Big Data, Cyber-Physical Systems, plement and assist humans. In the European Factories Machine Learning, Additive Manufacturing, and Robotics of the Future Research Association (EFFRA) Roadmap [1]. To some extent, humans once as key elements in 2020, human-centricity is prerequisite to cope with social challenges. Anthropocentric or human-centric (human- production system such as assembly lines are somehow neglected. centered) approaches are getting more and more attention in Apparently, industrial robots have made great progress in the past few years [4–6]. Based on the academic publications, replacing repetitive, monotonous tasks including handling, this paper focuses on the recent researches towards anthro- sorting, and welding. However, with the applications and pocentric manufacturing approaches for smart assembly implementations of new manufacturing paradigms, many system including human-CPS integration, human-centric have realized that humans as key roles cannot be easily design, human-robot collaboration, and human limitations replaced by advanced technologies at least in the foreseeable management. future. Particularly in the efi ld of assembly, a great amount of eTh rest of the paper organized as follows: Section 2 tasks are not simple routine work but subtle operations where discusses the integration of human in cyber-physical system. 2 Journal of Robotics The framework of human-in-the-loop CPS (HCPS) is pre- From the perspective of anthropocentric approach, CPS sented. Section 3 reviews the human-centric design in the may impact or improve the health, learning, and operative shop-floor level. In Section 4, human-robot collaboration in performance of human workers [15]. Integrating human in hybrid assembly is elaborated, and collaboration paradigms, CPS framework will make manual operations more secure task plan and assignment, immersed vision based inter- and ecffi ient which will also enhance process control and action interfaces, and human safety issues are reviewed. improve quality especially in product assembly domain Section 5 analyzes the human limitations towards manufac- [16]. A framework called “CyFL-Matrix” which can help turing requirements in future generation. Section 6 discusses both industrial stakeholders and researchers to navigate the challenges and concludes the research. operational and social sustainability performance impacts of improvement projects towards HCPS is proposed in [15]. An adaptive human-centric reference architecture for 2. Integrating Human in automation system is proposed in [17], in which machines Cyber-Physical System and automation systems adapt to the cognitive and physical Cyber-physical system (CPS) is one of the most important demands of humans in a momentary and dynamic manner. enabling technologies in Industry 4.0 era. CPS is a closed- Anthropocentric CPS represents the trend of CPS both looped system which integrates dynamic physical processes objectively and methodologically. However, compared to traditional manufacturing environments, the degree of com- with communication, controlling, computing, and other novel information technologies [7]. Despite different angles plexity dramatically increases among all domains of HCPS of den fi itions and interpretations, the core characteristics [18]. For one thing, human behaviors are hard to be regulated and anticipated, causing disturbances and exceptions in of CPS are the same, including the connection between cyber and physical world, real-time data exchange, and the control loop, which further influence the stability and bidirectional information flows with closed loops [8]. In harmony of HCPS. To enhance the resilience of HCPS, these manufacturing domain, CPS are sometimes referred to CPPS dissonance caused by human behaviors must be interpreted, (Cyber-Physical Production System) [9]. modelled, and managed [19]. For another, human role in HCPS alters as application domain changes from design, Generally, there are two major development routes of CPS [10], namely, “technocentric” and “anthropocentric” operation, maintenance, and service. er Th efore, from deci- approaches. In “technocentric” route, manufacturing process sional level to operational level, or both, the corresponding reference framework should be established to model such is determined by enabling technologies of CPS, such as advanced robots and machine tools, Artificial Intelligence variations. (AI), and Digital Twins (DW). Under this circumstance, the autonomy of skilled workers is limited. While in “anthro- 3. Human-Centered Design for pocentric”scenario,ontheonehand, theskilledworkers Shop-Floor Assembly guide the implementation of CPS, on the other hand, CPS supports the decision-making process of skilled workers. As briefed in the last section, the design of smart manu- Introducing “anthropocentric” CPS in smart factories facturing system favored a technocentric approach which will result in mutual transitions from human-machine coop- gave priority to the definition and allocation of tasks with eration to active collaboration, which is characterized by automated system and computing resources, only taking cyber-physical-social interactions, knowledge exchange, and human operators into consideration at the end of the design reciprocal learning [11]. Recent researches have renewed the process [20]. This kind of design approach assumes human focus on human either as an operator or supervisor in CPS as “magic” who must behave perfectly when unexpected related manufacturing. Human as a composite factor in the situations happened [21]. However, as the technological CPS defines a new type of CPS-based systems, either called accidents are caused primarily by human errors (63%) [20], Cyber-Physical-Human System (CPHS) [12], Anthropocen- putting human in the control loop of smart manufacturing tric Cyber-Physical System (ACPS) [13], or Human-Cyber- system implies human-centered design (HCD) framework Physical- System (HCPS) [14]. In this article, the abbreviation andmethodtocopewithmoreandmore complexproblems. of HCPS is preferred to symbol human as the center aspect in HCD approach is not only required in human-machine the CPS loop. interactive interfaces but also in shop-floor level system The HCPS can be characterized by the features as inter- design. Shop-floor assembly system design is usually required operability, sociability, flexibility, adaptability, autonomy, etc. when a new manufacturing system is to be build or an [13]. In HCPS [14], intelligent machines are supposed to existing one needs to be renewed. By placing the human in replace a great portion of human physical and mental tasks the center of the Industry 4.0 design [22], three perspectives which makes humans more concentrated on creative work. (abstraction, decision-making, and innovativeness) as well as From HPS (Human-Physical system), CPS to HCPS, humans two components (human and CPS) constructed the analysis transfer parts of cognitive and learning work to cyber system, and design framework. er Th efore, a set of key performance whichenablethecybersystemwiththeabilityto“cognizeand indicators (KPI) can be introduced to analyze and assess learn”. With human-in-the-loop hybrid enhance intelligence, design scheme [22]. the capability of the manufacturing system to cope complex, Taking workplace design for example, current shop-floor uncertain problems will be essentially improved [14]. eTh level industrial robots (machinery) applications separate framework of HCPS is presented in Figure 1. the workplace of human and robots (machinery) cells due Journal of Robotics 3 to safety considerations. In human-centered mode, human will promote the overall performances of assembly systems, operators and robots coexist in the same space to execute especially in Small and Medium-sized Enterprises (SMEs), assembly taskseitherindividuallyorcooperatively[23]. where the balance of productivity, flexibility, and adaptability Towards a fulfilled HCD of workplaces, not only the physical are of high significance [27]. In this case, the hybrid assembly aspects but also the cognitive ergonomic aspects need to be paradigm has risen as a feasible and effective paradigm for considered during the design processes. Concerning the cog- human-robot collaborations. nitive workload related to manufacturing operations at differ- Generally, in hybrid assembly paradigm, manufacturing ent decisional levels, in [24], a human-in-the-loop framework resourcesashumans,robots,sensors,and otherdevices share concerning the design of workplaces was proposed to classify thesameworkspace.Basedonthespace andtimeperspec- the fabrication tasks of production processes according to tives [28]: the classica fi tion of human-robot collaboration can their cognitive complexity. In the ergonomic analyses phase, be categorized as in Figure 2. unlike traditional methods, which only observe operators in the actual running workplace, the impact of design plans 4.1.1. Humans Share Stationary Workplace with Robots but including human actions and reactions can be simulated and Their Activity Time Does Not Overlap. For example, in [29], veriefi dbyimmersedvisionbased approaches.Toapply a an interactive cooperation between human supervisor and HCD approach, the improvement of human posture, stress, the welding robot is presented. eTh human operator is and satisfaction is possible by assessing different setups responsible for teaching the robot by demonstration; the in Mixed Reality (MR) environment [6]. Likewise, in the robot executes the welding process. Because the human design phase, the concept virtual ergonomics [25] applied operatorandtherobotsharethesameworkplacein dieff rent Digital Manufacturing (DM) tools with the ability to insert time periods, the risk of human operator is minimized. digital human models and other virtual resource models in theproductionplant.Theabove approachisable to simu- 4.1.2. Close Collaboration Where Humans and Robots Con- late assembly tasks in workplaces, making human-centered tacted Directly Both in a Shared Workspace and Time. Tra- design possible. ditional industrial robots (mostly multijoint robots) are not Note that HCD simply considers not only the human considered autonomous enough to allow close interaction factor aspects such as workload, posture, and stress but also with humans [23]. Recently, a new type of robots—cobots higher anthropocentric aspects such as human satisfaction (from collaborative robots) is designed for collaboration with [17] and emotion. Both human knowledge and cognitive human operators which allow safely physical human-robot limitations need to be represented, analyzed, and integrated contact. Based on cobots, collaborative human-robot man- in the design phase. Furthermore, HCD method should ufacturing framework for homokinetic joint assembly was be considered through the life cycle of the system. As presented in [30]. In the proposed approach, direct physical the Industry 4.0 solutions proposed by Bosch, the people contact is successfully managed with sensor based control; including designers, workers, and users are the key roles the cobots can rfi stly lighten the burden of the operator in the connected plant supporting decisions based on con- and secondly comply his needs in the latter. A Portable textual digital information, assistance functions, and ability Assembly Demonstration (PAD) system for robots to learn ampliefi r for human operators as well as adaptive workplace complex assembly skills from human was presented in [31]. ergonomics. eTh assembly script generated by PAD is implemented on a Baxter robot. Using a RGB-D camera, motions, tools, and 4. Human-Robot Collaboration for parts in the assembly process can be recognized. Moreover, Hybrid Assembly assembly states are estimated based on the 3D part models created by a 3D scanner. Human-robot collaboration (HRC) concerning the interac- tion, communication, and collaboration between humans 4.1.3. Humans and Robots (Mainly AGVs) Collaborate in a androbotsreceivedalotofattention recently.TheEU project ROBO-PARTNER has promoted a hybrid solution Dynamic Space and Time. In this paradigm, humans are responsible for critical assembly tasks, while AGVs support concerning the safe cooperation of human operators with autonomous and self-learning/adapting robotic systems [26]. the human operators with correct components and materials HRC has a wide range of research topics such as safety, in the correct time. eTh human operators may move from one assembly station to another followed by AGVs with necessary learning-by demonstrations, imitation learning, and cogni- tive systems. This section focuses on the human-centered resources. In some applications, AGV and multijoint cobot are integrated together not only for transportation but also approach mainly for shop-floor level assembly, covering for assisting human with picking or sorting abilities, which domains as human-robot collaboration paradigm, task plan- ning and assignment, multimodal interaction interfaces, and isverypracticalinlargescaleproduct assembly orsystem maintenance. interaction safety. Although the introduction of cobots is anticipated as 4.1. eTh Evolution of Human-Robot Collaboration Paradigm. a promising approach to facilitate close human-robot col- Recently, researches have come to realize that the fusion of laboration, the cobot itself still has limitations. First of all, therobot’sprecision,repeatability,strength,anddurancewith theworkloadofcobotsisrelativelylow compared totypical human’s dexterity, perception, intelligence, and flexibility industrial robots. Secondly, cobots’ speed is controlled for 4 Journal of Robotics safety considerations which will decrease the efficiency of the behaviors, unified modal control framework is always needed operation. Last but not least, the working range and accuracy to adapt signal alteration from heterogeneous sensor sources, of cobots are insufficient for some application scenarios. For such as vision, position, and force [39]. However, in actual this reason, while the development of cobots highlights new assembly scenarios, environmental noises are always too big compound materials, mechanical structures and heteroge- to distinguish the verbal commands by humans. While haptic neous sensors to improve accuracy, speed, workload, and and force control are more feasible, the reliability of actual intelligence, traditional robots and HRC paradigms are still usage is always worried by industrial users. In addition, haptic required in various industrial application demands. and force sensors and controllers are generally integrated in the product design of cobots in recent years. er Th efore, in this sector, the immersed visual based interfaces including 4.2. Task Planning and Assignment. In HRC, both human VR (Virtual Reality), AR (Augmented Reality), and wearable and robot can perform assembly tasks within their respective devices are highlighted. abilities. Consequently, task planning and assignment rises VR and AR interactive technologies enable reproduction as an important issue for HRC process control. Proper task of the main characteristics of HRC, highlighting or even assignment plans will increase productivity, maximize system emphasizing particular aspects of the collaboration [30]. performance, and even minimize human operators’ physical Especially, AR can provide digital information for increased as well as cognitive workload at the same time. situational awareness, emphasizing different objects, recom- A task can be decomposed into several subtasks in mending optimized motions, and improving the trust and a hybrid assembly, then the subtasks will be assigned to context-awareness of HRC [40, 41]. Also, AR can be used humans or robots based on criteria concerning their dieff rent as an assistant to generate assembly sequence and visual advantages. Based on dual Generalized Stochastic Petri Net instructions for each assembly steps; therefore, the burden of (GSPN), the assembly task allocation process for human- operators is alleviated [42]. robot coordinated cell manufacturing was modelled in [32], In addition, wearable devices such as smart-watches can and Monte Carlo method and Cost-effectiveness analysis for be developed as assistive means with AR applications in order Multiple-Objective Optimization are proposed for strategy to help operators provide feedback and interact with the AR generation and optimization. In [33], an intelligent decision- system [43]. Also, with sensors and Apps in smart-phones, making algorithm was proposed which allows human-robot smart-watches, and other wearable devices, the position of task allocation in the same workplace, where schedules are humans’ arms and legs is monitored in real-time. er Th efore, automatically generated and evaluated using multiple criteria. eTh result assigned task needs to be automatically the ergonomics effects are able to be evaluated and improved [44]. A human-robot interaction architecture for intuitive transformed into robots and human respectively. Towards seamless human-robot collaboration, a two-level hierarchical control of dual armed robot is proposed in [45]. eTh human representation ofthetaskisproposedin[34]whichcouldbe body gesture can be recognized using Kinect Xbox and directly translated into robotic commands. The assignment mapped to robot control command based on ROS. An AR results are correspondingly generated aer ft assessment and tool is presented in [46] to support human operators in a evaluation in different application scenarios. Based on Robot shared assembly workplace. With immersion capabilities of Operating System (ROS) [35], the assembly sequence data AR technology, not only the assembly processes, status, and exported from the Off-Line Programming (OLP) tools to a instructions are visualized, but also the operator’s safety is neutral XML format, graphical interfaces for human-robot enhanced. In [47], interactive assembly virtual environment tasks coordination is developed with the ability to review the basedonphysicalmodelsisdeveloped,theresultsofassembly previous and upcoming tasks. operations can be displayed in the form of 3D visualiza- Human factors (ergonomics) are closely relative issues tion, and human can correct the obvious errors by HRC which must be considered in task assignments. A multicri- mechanism to ensure the n fi al assembly plan is feasible. An teria approach and algorithm are proposed in [27] to plan the immersed VR environment for large parts assembly process human-robothybridcelllayoutand taskinthesametime. assessment was built in [48] by recording and analyzing In a skill-based task assignment approach proposed in [28], human’s main movement with the help of Oculus and Kinect. with the assembly task description model, the skills of human and robots are compared according to the requirements. 4.4. Interaction Safety. Safety is the most crucial issue when An integrated (feed-forward & feedback) optimum subtask introducing robots to the shop-floor. In the traditional allocation scheme is proposed in [36] for the assembly robot application scenarios, human and industrial robots triggered by two-way trust (human’s trust in robot and are strictly separated with fences. However, accidents still robot’s trust in human). A set of ergonomics, quality, and happened due to misconduct or malfunction. While in HRC, productivity criteria is designed to choose the most suitable the safety issues become more complicated, as the degree plans. of complexity dramatically increases in the operation and maintenance domains [18]. 4.3. Immersed Multimodal Interaction Interfaces. Multimodal TomakeHRCsafetyavailable,multiplesensorsneedtobe interaction interfaces including visual guidance [37], voice integrated to avoid or prevent collisions either externally or commands [38], and haptic and force control are popular internally. In external sensors based safety model, visual sen- research topics in HRC. Due to the unpredictability of human sors and motion detectors are integrated into robots’ control Journal of Robotics 5 Human Experience sharing Mixed AI Cognition Enforcement Cyber Knowledge Graph Supervising Assisting Artificial intelligence Operating Amplifying Monitoring Cloud Computing Digital Twin Resources Dynamic Sensing and Controlling Physical Figure1:The frameworkofintegrating humaninthe CPSloop. Figure 2: HRC paradigms. system to avoid collision with human. Virtual 3D models potential risks of collisions is introduced in [49]. eTh safe of robots and real camera images of operators for real-time detour trajectory will be automatically generated within one are used for collision avoidance in [37]. With the collision second during assembly process. detection result, the robot can be adaptively controlled in Although the external sensors based model has been human-robot assembly scenarios to keep human operator proved effectively in many researches by demonstrations safe. A 3D ToF (Time of Flight) vision sensor to detect and experiments, the actual industrial implementations are 6 Journal of Robotics quite low. One of the reasons is this kind of safety cannot human have dropped rapidly. However, the cognitive burdens be completely trusted by human in the complex industrial are increasing because of the growing complexity of systems, application areas where even small chance of failures in ever-changing manufacturing requirements, and the frequent sensors and control system may still cause serious damage human-machine interactions. Stress is one major cognitive to humans. Besides the external safety model, the internal limitation. In HRC assembly paradigm, the moving industrial safety will improve the safety level during HRC. eTh internal robots mayinducestressonhumanoperators.Aframework safetyis implementedwhenthecobots aredeveloped,as to integrate human cognitive factors (fatigue, stress, skill new kinematics, materials, and internal sensors are integrated level, emotional state, etc.) is proposed in [53]; the cognitive in the cobots to avoid injury to humans. Safety cobots are layer is presented enabling intelligent response to the changes already used in automobile assembly lines in several leading from the data perceived from the environment. When com- manufacturers [50]. Because the safety is the primary goal in ing near the robots, the stress of human operator rises, which cobots’ design objectives, such safety is inherent and intrinsic, can be measured by skin potential values [54]. Hence, the despite the sacrifices in workload, working radius, and speed. proper parameters and principles can be established for HRC. In recent years, several standards regarding robot safety Aging is another important issue regarding sustainable use are published by ISO, including ISO 10218, ISO/TS 15066. manufacturing. One of trends in Industry 4.0 or smart While ISO 10218 focuses on the standards for safety of manufacturing is that fewer operators have to be employed industrial robots, ISO/TS 15066 provides guidelines for the in manufacturing shop-floors. However, skilled and expe- design and implementation of a collaborative workspace. rienced workers are always required in highly automation Note that the safety factors varies among different robot types, factories to manage and maintain the growing complexity of workloads, powers, geometric shapes, assembly processes, intelligent system. By the year 2025, it has been estimated that multiple aspects of safety which still need to be considered the proportion of working individuals over the age of 50 years and analyzed. will be 32%inEurope, 30%inNorth America, 21%inAsia, and 17% in Latin America. In 2050, around half of workers will be aged over 50 in developed countries [55]. Because 5. Human Limitation Management of the decreasing physical and cognitive capacities of aging workers, including decrease of visual ability, acoustic ability, eTh anthropocentric approaches which put humans in the musculoskeletal force, and motion capabilities, the resilience center also have its challenges. One of the reasons is that to the job workload will reduce correspondingly. Therefore, human has limitation comparing to robots and automation the manufacturing framework must be adaptive to cope with systems. Only by managing both technology and human the aging issue [55]. The adaptive manufacturing can com- limitations can the smart manufacturing system be reliable, pensate the aging limitations to guarantee job performances. flexible, and effective. Furthermore, the senior experts’ knowledge and experience Human limitations are the main causes of human related are needed to improve the learning curve of new operators errors and failures. Categorized from memory, perception to [17]. motion aspect of human, about 16 types of errors are pro- Besides the general limitation management of human posed in [51]. In smart manufacturing environment, although workers, here we argue that the human limitation should be many operations are executed automatically, human errors modelled individually in the future research because each are still of great importance, especially omissions and the incorrect selection of variants. Human failure modes include person has limitations in different aspects and degrees. eTh unique profile of each worker needs to be established by data misuse, false indication, and mode confusion [52]. Based on these modes, a CPS safety analysis and simulation platform mining or big data analysis. Such profile can be extracted as called CP-SAP is developed and a cyber-physical human input to the anthropocentric manufacturing system, in which dynamic fault tree is designed in [52]. In order to access the congfi uration varies respectively for each person in each human errors, human errors probability (HEP) and human context. For example, when a left-handed operator is logging reliability probability (HRP) are proposed as indicators for in, the information display and operation interfaces will therelativeoccurrenceoferrorsandrespectively faultless correspondingly change for this new parameter to achieve actions [51]. eTh den fi ition of the above two concepts is as better anthropocentric performances. follows: 𝑓𝑜 V 6. Discussion and Conclusion HEP= 𝑓𝑜𝑡ℎ𝑒 𝑓 The relation of human and technology in the manufacturing (1) domain has been a concerning issue along with the perva- = , sive applications of advanced automation and information technologies including robots, IoT, and AI. By analyzing HRP=1− HEP the publications and latest research findings, we find that besides the advancement of technology, the “anthropocen- From the ergonomic points of view, the limitations of tric” approaches are attracting more and more attention human in the manufacturing system exist both physically and in the past few years. Many have realized that human cognitively. For the past few years, as automatic machines and robots replaced lots of heavy work, the physical burdens of having a key role should not be replaced or weakened in 𝑒𝑟𝑟𝑜𝑟𝑎𝑛𝑜𝑟𝑖𝑒𝑠𝑖𝑡𝑖𝑙𝑖𝑏𝑝𝑜𝑠𝑠 𝑛𝑢𝑚𝑏𝑒𝑟 𝑒𝑟𝑟𝑜𝑟𝑠𝑒𝑑 𝑒𝑟𝑜𝑏𝑠 𝑛𝑢𝑚𝑏𝑒𝑟 Journal of Robotics 7 smart manufacturing or other new manufacturing paradigm, designing an ergonomic work system,” Procedia Manufacturing, vol. 3, pp. 510–517, 2015. especially in assembly domain. However, that is not to say that the progress of technology implementations should [6] M. Peruzzini, M. Pellicciari, and M. Gadaleta, “A comparative study on computer-integrated set-ups to design human-centred be decelerated. On the contrary, the importance of novel manufacturing systems,” Robotics and Computer-Integrated technologies is more highlighted to better help and assist Manufacturing,vol.55, pp.265–278,2019. human. [7] L.Monostori,B.Kad ´ ar ´ , T. 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