Cooperation of human and machines in assembly lines
TL;DR: In this article, a survey about forms of human-machine cooperation in assembly and available technologies that support the cooperation is presented, including organizational and economic aspects of cooperative assembly including efficient component supply and logistics.
About: This article is published in CIRP Annals.The article was published on 2009-01-01. It has received 667 citations till now. The article focuses on the topics: Flexibility (engineering) & Robot.
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TL;DR: An extensive review on human–robot collaboration in industrial environment is provided, with specific focus on issues related to physical and cognitive interaction, and the commercially available solutions are presented.
632 citations
TL;DR: A review of the state of the art research in the areas of assembly system design, planning and operations in the presence of product variety is presented in this article, where methods for assembly representation, sequence generation and assembly line balancing are reviewed and summarized.
479 citations
TL;DR: The main purpose of this paper is to review the state-of-the-art on intermediate human–robot interfaces (bi-directional), robot control modalities, system stability, benchmarking and relevant use cases, and to extend views on the required future developments in the realm of human-robot collaboration.
Abstract: Recent technological advances in hardware design of the robotic platforms enabled the implementation of various control modalities for improved interactions with humans and unstructured environments. An important application area for the integration of robots with such advanced interaction capabilities is human---robot collaboration. This aspect represents high socio-economic impacts and maintains the sense of purpose of the involved people, as the robots do not completely replace the humans from the work process. The research community's recent surge of interest in this area has been devoted to the implementation of various methodologies to achieve intuitive and seamless human---robot-environment interactions by incorporating the collaborative partners' superior capabilities, e.g. human's cognitive and robot's physical power generation capacity. In fact, the main purpose of this paper is to review the state-of-the-art on intermediate human---robot interfaces (bi-directional), robot control modalities, system stability, benchmarking and relevant use cases, and to extend views on the required future developments in the realm of human---robot collaboration.
452 citations
Cites background from "Cooperation of human and machines i..."
...…2009,Evrard et al., 2009], object placing [Tsumugiwa et al., 2002a,Gams et al., 2014], object swinging [Donner and Buss, 2016a,Palunko et al., 2014], posture assistance [Ikemoto et al., 2012, Peternel and Babič, 2013], and industrial complex assembly processes [Krüger et al., 2009] (see also Fig....
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...…of physical human-robot interaction (see [De Santis et al., 2008, Murphy, 2004, Alami et al., 2006]), is defined when human(s), robot(s) and the environment come to contact with each other and form a tightly coupled dynamical system to accomplish a task [Bauer et al., 2008, Krüger et al., 2009]....
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Abstract: Although the concept of industrial cobots dates back to 1999, most present day hybrid human-machine assembly systems are merely weight compensators. Here, we present results on the development of a collaborative human-robot manufacturing cell for homokinetic joint assembly. The robot alternates active and passive behaviours during assembly, to lighten the burden on the operator in the first case, and to comply to his/her needs in the latter. Our approach can successfully manage direct physical contact between robot and human, and between robot and environment. Furthermore, it can be applied to standard position (and not torque) controlled robots, common in the industry. The approach is validated in a series of assembly experiments. The human workload is reduced, diminishing the risk of strain injuries. Besides, a complete risk analysis indicates that the proposed setup is compatible with the safety standards, and could be certified.
449 citations
Cites background from "Cooperation of human and machines i..."
...• In contrast with most existing human-machine manufacturing applications, where collision avoidance is guaranteed by a minimum security distance [2], our framework successfully manages direct physical contact between robot and human, and between robot and environment....
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...A thorough state-of-theart on human-machine cooperation in manufacturing lines is provided in [2]....
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...Although the welding assistance application targeted by these works also falls in the shared workplace paradigm evoked in [2], it differs from the one treated here, since the robot motion is driven by the human worker....
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...But as in the previously cited survey [2], the paper exposes the absence of high level human-robot collaboration, apart from “Intelligent Lift Assistants”....
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TL;DR: The progress made in robotics to emulate humans’ ability to grab, hold, and manipulate objects is reviewed, with a focus on designing humanlike hands capable of using tools.
Abstract: BACKGROUND Humans have a fantastic ability to manipulate objects of various shapes, sizes, and materials and can control the objects’ position in confined spaces with the advanced dexterity capabilities of our hands. Building machines inspired by human hands, with the functionality to autonomously pick up and manipulate objects, has always been an essential component of robotics. The first robot manipulators date back to the 1960s and are some of the first robotic devices ever constructed. In these early days, robotic manipulation consisted of carefully prescribed movement sequences that a robot would execute with no ability to adapt to a changing environment. As time passed, robots gradually gained the ability to automatically generate movement sequences, drawing on artificial intelligence and automated reasoning. Robots would stack boxes according to size, weight, and so forth, extending beyond geometric reasoning. This task also required robots to handle errors and uncertainty in sensing at run time, given that the slightest imprecision in the position and orientation of stacked boxes might cause the entire tower to topple. Methods from control theory also became instrumental for enabling robots to comply with the environment’s natural uncertainty by empowering them to adapt exerted forces upon contact. The ability to stably vary forces upon contact expanded robots’ manipulation repertoire to more-complex tasks, such as inserting pegs in holes or hammering. However, none of these actions truly demonstrated fine or in-hand manipulation capabilities, and they were commonly performed using simple two-fingered grippers. To enable multipurpose fine manipulation, roboticists focused their efforts on designing humanlike hands capable of using tools. Wielding a tool in-hand became a problem of its own, and a variety of advanced algorithms were developed to facilitate stable holding of objects and provide optimality guarantees. Because optimality was difficult to achieve in a stochastic environment, from the 1990s onward researchers aimed to increase the robustness of object manipulation at all levels. These efforts initiated the design of sensors and hardware for improved control of hand–object contacts. Studies that followed were focused on robust perception for coping with object occlusion and noisy measurements, as well as on adaptive control approaches to infer an object’s physical properties, so as to handle objects whose properties are unknown or change as a result of manipulation. ADVANCES Roboticists are still working to develop robots capable of sorting and packaging objects, chopping vegetables, and folding clothes in unstructured and dynamic environments. Robots used for modern manufacturing have accomplished some of these tasks in structured settings that still require fences between the robots and human operators to ensure safety. Ideally, robots should be able to work side by side with humans, offering their strength to carry heavy loads while presenting no danger. Over the past decade, robots have gained new levels of dexterity. This enhancement is due to breakthroughs in mechanics with sensors for perceiving touch along a robot’s body and new mechanics for soft actuation to offer natural compliance. Most notably, this development leverages the immense progress in machine learning to encapsulate models of uncertainty and support further advances in adaptive and robust control. Learning to manipulate in real-world settings is costly in terms of both time and hardware. To further elaborate on data-driven methods but avoid generating examples with real, physical systems, many researchers use simulation environments. Still, grasping and dexterous manipulation require a level of reality that existing simulators are not yet able to deliver—for example, in the case of modeling contacts for soft and deformable objects. Two roads are hence pursued: The first draws inspiration from the way humans acquire interaction skills and prompts robots to learn skills from observing humans performing complex manipulation. This allows robots to acquire manipulation capabilities in only a few trials. However, generalizing the acquired knowledge to apply to actions that differ from those previously demonstrated remains difficult. The second road constructs databases of real object manipulation, with the goal to better inform the simulators and generate examples that are as realistic as possible. Yet achieving realistic simulation of friction, material deformation, and other physical properties may not be possible anytime soon, and real experimental evaluation will be unavoidable for learning to manipulate highly deformable objects. OUTLOOK Despite many years of software and hardware development, achieving dexterous manipulation capabilities in robots remains an open problem—albeit an interesting one, given that it necessitates improved understanding of human grasping and manipulation techniques. We build robots to automate tasks but also to provide tools for humans to easily perform repetitive and dangerous tasks while avoiding harm. Achieving robust and flexible collaboration between humans and robots is hence the next major challenge. Fences that currently separate humans from robots will gradually disappear, and robots will start manipulating objects jointly with humans. To achieve this objective, robots must become smooth and trustable partners that interpret humans’ intentions and respond accordingly. Furthermore, robots must acquire a better understanding of how humans interact and must attain real-time adaptation capabilities. There is also a need to develop robots that are safe by design, with an emphasis on soft and lightweight structures as well as control and planning methodologies based on multisensory feedback.
371 citations
Cites background from "Cooperation of human and machines i..."
...Manipulating objects in interaction and collaboration with humans: Reality and challenges Human–robot collaboration in manufacturing setups has been deemed crucial for the industry (85, 86)....
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References
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Proceedings Article•
01 Jan 2000
TL;DR: This paper addresses the mechanism design methodologies, specification, and control strategies of a mobile manipulation system for the humanoid robot ARMAR, that has to work autonomously or interactively in cooperation with humans in dynamic unstructured environments such as workshops or homes.
Abstract: This paper addresses the mechanism design methodologies, specification, and control strategies of a mobile manipulation system for the humanoid robot ARMAR, that has to work autonomously or interactively in cooperation with humans in dynamic unstructured environments such as workshops or homes.
76 citations
TL;DR: For applying an evolutionary approach for this multicriteria case of AGV problem, priority-based encoding method and Interactive Adaptive-weight GA (i-awGA) were proposed and numerical analyses show the effectiveness of proposed approach.
Abstract: Automated guided vehicles (AGVs), are the state-of-the-art, and are often used to facilitate automatic storage and retrieval systems (AS/RS). In this paper, we focus on the dispatching of AGVs in a flexible manufacturing system (FMS). A FMS environment requires a flexible and adaptable material handling system. We model an AGV system by using network structure. This network model of an AGV dispatching has simplexes decision variables with considering most AGV problem’s constraints, for example capacity of AGVs, precedence constraints among the processes, deadlock control. Furthermore, these problems can be solved by using a lot of heuristic algorithms as network optimization problems. We are also proposed an effective evolutionary approach for solving a kind of AGV’s problems in which minimizing time required to complete all jobs (i.e. makespan) and minimizing the number of AGVs, simultaneously. For applying an evolutionary approach for this multicriteria case of AGV problem, priority-based encoding method and Interactive Adaptive-weight GA (i-awGA) were proposed. Numerical analyses for case study show the effectiveness of proposed approach.
71 citations
TL;DR: In this paper, a system based on digital 3D image analysis has been developed, which supervises the common working area of robot and man, especially for complex assembly and handling tasks, and this combination is useful.
71 citations
20 Dec 2001
TL;DR: In this paper, the Dynamics of the Class 1 Shell Tensegrity Structure, an Introduction to the Mechanics of Tense-grity Structures, Robert E. Skelton, J. William Helton, Rajesh Adhikari, Jean-Paul Pinaud, and Waileung Chan
Abstract: MANUFACTURING, Edited by Galip Ulsoy Manufacturing Systems and Their Design Principles, M.G. Mehrabi, A.G. Ulsoy, and Y.Koren Computer-Aided Process Planning For Machining, Derek Yip-Hoi Discrete Event Control of Manufacturing Systems, D.M. Tilbury and P.P. Khargonekar Machine Tool Vibrations, Yusuf Altintas Machine Tool Monitoring and Control, Kourosh Danai Process Monitoring and Control of Machining Operations, Robert G. Landers, A. Galip Ulso, and Richard J. Furness Forming Processes: Monitoring and Control, David E. Hardt Assembly and Welding Processes and their Monitoring and Control, S. Jack Hu and Elijah Kannatey-Asibu, Jr. Control of Polymer Processing, David Kazmer and Kourosh Danai Precision Manufacturing, Thomas R. Kurfess VIBRATION CONTROL, Edited by Nejat Olgac Active Damping of Large Trusses, Preumont, F. Bossens, and N. Loix Semi-Active Suspension Systems, Nader Jalili Semi-Active Suspension Systems II, Michael Valasek and Willi Kortum Active Vibration and Absorption and Delayed Feedback Tuning, Nejat Olgac and Martin Hosek Vibration Suppression Utilizing Piezoelectric Networks, K.W. Wang Vibration Reduction via the Boundary Control Method, Siddharth P. Nagarkatti and Darren M. Dawson DYNAMICS AND CONTROL OF AEROSPACE SYSTEMS: AN INTRODUCTION TO TENSEGRITY STRUCTURES, Edited by Robert E. Skelton An Introduction to the Mechanics of Tensegrity Structures, Robert E. Skelton, J. William Helton, Rajesh Adhikari, Jean-Paul P. Pinaud, and Waileung Chan The Dynamics of the Class 1 Shell Tensegrity Structure, Robert E. Skelton and Jean-Paul Pinaud, D.L. Mingori ROBOTICS, Edited by Miomir Vukobratovic Robot Kinematics, Bruno Siciliano Robot Dynamics ,Miomir Vukobratovic and V. Potkonjak Actuators and Computer-Aided Design of Robots, Miomir Vukobratovic, V. Potkonjak, Masaharu Takano, and Kenji Inoue Control of Robots, Miomir Vukobratovic and D. Stokic Control of Robotic Systems in Contact Tasks, Miomir Vukobratovic andD. Surdilovic Intelligent Soft-Computing Techniques in Robotics, D. Katic and B. Karan Teleoperation, Telerobotics, A. Bejczy Mobile Robotic Systems, Nenad M. Kircanski Humanoid Robots, Miomir Vukobratovic , Branislav Borovac, D. Stokic, and D. Surdilovic Present State and Future Trends in Robot Applications, M. Hagele and Dieter Schraft
70 citations
10 Apr 2007
TL;DR: In this article, a simulation setup with the dummy FAT ES-2 representing the operator and an industrial robot is realized within LS-DYNA to conduct impacts of the robot arm against the head and the chest.
Abstract: New technologies and processes enhance the need for direct human-robot-interaction, to fully exploit the potential of robots' accuracy and humans' adaptability. Therefore, the hazardous potential of the involved robot manipulator needs to be minimised. Limits must be set, so that only an acceptable severity of injury for the human will remain in case of an unintended contact between the robot and a human. Current standardisation for industrial robot systems does not sufficiently address the subject of close human-robot-cooperation, thereby restricting the implementation of the newest technology. The aim of the reported research is to demonstrate the possibilities to assess the safety performance of robot systems by robot-dummy impact evaluation. In this paper, methods from the automotive industry are investigated on their transferability to the situation in robotics. Anthropomorphic test devices, so called crash test dummies, that resemble the human's kinematic response in car crashes are analysed during a robot-dummy impact. A simulation setup with the dummy FAT ES-2 representing the operator and an industrial robot is realised within LS-DYNA to conduct impacts of the robot arm against the head and the chest. The resulting head injury index (HIC), the viscous criteria (VC) for the chest and the pubic symphysis peak force (PSPF) for the pelvis are discussed, showing their potential and limitations for the situation in robotics.
69 citations