Simulation and programming experiences of collaborative robots for small-scale manufacturing

Pieskä, Sakari; Kaarela, Jari; Mäkelä, Jari

doi:10.1109/sims.2018.8355303

Cited by 22 publications

(10 citation statements)

References 3 publications

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“…Depending on the complexity of the task to be undertaken by the cobot, a ran proprietary and third-party solutions are available. For more straightforward cobot t such as the first and second tasks listed in Table 4, teach pendant (also known as through programming), teach or program by demonstration, and offline/simulation gramming are commonly available options [23,26]. Figure 1 shows the teach pend produced by ABB and Universal Robots.…”

Section: Existing Cobot Programming and Control Methodologiesmentioning

confidence: 99%

“…There seems to be a disparity in programming a cobot to perform a particular In most practical scenarios, it is often the person who knows the task well who i competent with programming, and the person who is competent with programming not know the task well [22]. A possible solution is that the person with knowledge o task could also program the cobot [23,24]. Operators with task expertise provide sub tial improvements in the precision and efficiency of cobot operations when compar non-experts [22].…”

Section: Technical Readiness For Cobot Deploymentmentioning

confidence: 99%

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Task Complexity and the Skills Dilemma in the Programming and Control of Collaborative Robots for Manufacturing

et al. 2023

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While traditional industrial robots participate in repetitive manufacturing processes from behind caged safety enclosures, collaborative robots (cobots) offer a highly flexible and human-interactive solution to manufacturing automation. Rather than operating from within cages, safety features such as force and proximity sensors and programmed protection zones allow cobots to work safely, close to human workers. Cobots can be configured to either stop or slow their motion if they come in contact with a human or obstacle or enter a protection zone, which may be a high pedestrian traffic area. In this way, a task can be divided into sub-processes allocated to the cobot or the human based on suitability, capability or human preference. The flexible nature of the cobot makes it ideal for low-volume, ‘just-in-time’ manufacturing; however, this requires frequent reprogramming of the cobot to adapt to the dynamic processes. This paper reviews relevant cobot programming and control methods currently used in the manufacturing industry and alternative solutions proposed in the literature published from 2018 to 2023. The paper aims to (1) study the features and characteristics of existing cobot programming and control methods and those proposed in the literature, (2) compare the complexity of the task that the cobot is to perform with the skills needed to program it, (3) determine who is the ideal person to perform the programming role, and (4) assess whether the cobot programming and control methods are suited to that person’s skillset or if another solution is needed. The study is presented as a guide for potential adopters of cobots for manufacturing and a reference for further research.

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Section: Existing Cobot Programming and Control Methodologiesmentioning

confidence: 99%

Section: Technical Readiness For Cobot Deploymentmentioning

confidence: 99%

Task Complexity and the Skills Dilemma in the Programming and Control of Collaborative Robots for Manufacturing

et al. 2023

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“…Typical research questions explore how programming environments need to be designed to enable low-skilled workers to have a greater role in programming and interacting with robots in the factory. A substantial body of research focuses on end user programming and interface design as well as educational technologies (see, e.g., [29], [39], and [40]). Another focus is exploring intuitive interaction (see, e.g., [41]- [43]).…”

Section: B Programming and Teaching Cobotsmentioning

confidence: 99%

Cobots in Industry 4.0: A Roadmap for Future Practice Studies on Human–Robot Collaboration

Weiss

Wortmeier

Kubicek

2021

IEEE Trans. Human-Mach. Syst.

103

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With the vision of Industry 4.0 and cobots, working conditions in industrial settings are starting to change. We review related literature from the fields of human-robot interaction, work and organizational psychology, and sociology of work, as well as an exemplary project case study, and identify research gaps regarding the implications of cobots for work environments. We argue that we are in a transition phase from automation to actual collaboration with robots in manufacturing, and that this will open up a new problem space for investigations, in which a practice lens will be crucial. Based on this, we propose a research agenda for social practice and workplace studies to explore the sociotechnical environment of Industry 4.0 involving cobots at the individual, team, and organizational levels.

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“…Then, the essence of multimodal fusion is to realize the modeling and analysis of Multivariate Time Series (MTS). For example, in order to allow the robot can perform flexible operations in an uncertain environment, a force/torque sensor is mounted on the end-effector [8], a tactile sensor and a acoustical sensor are installed on the robot claw, and a visual sensor is placed for monitoring the robot workspace [9][10][11], as shown in Fig. 2.1.…”

Section: Related Workmentioning

confidence: 99%

Nonparametric Bayesian Modeling of Multimodal Time Series

Zhou

Rojas

et al. 2020

Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

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In this chapter, we take a Bayesian nonparametric approach in defining a prior on the hidden Markov model that allows for flexibility in addressing the problem of modeling the complex dynamics during robot manipulation task. At first, considering the underlying dynamics that can be well-modeled as a hidden discrete Markov process, but in which there is uncertainty about the cardinality of the state space. Through the use of the hierarchical Dirichlet process (HDP), one can examine an HMM with an unbounded number of possible states. Subsequently, the sticky HDP-HMM is investigated for allowing more robust learning of the complex dynamics through a learned bias by increasing the probability of self-transitions. Additionally, although the HDP-HMM and its sticky extension are very flexible time series models, they make a strong Markovian assumption that observations are conditionally independent given the discrete HMM state. This assumption is often insufficient for capturing the temporal dependencies of the observations in real data. To address this issue, we consider extensions of the sticky HDP-HMM for learning the switching dynamical processes with switching linear dynamical system. In the later chapters of this book, we will verify the performances in modeling mulitmodal time series and present the results of robot movement identification, anomaly monitoring, and anomaly diagnose.

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Simulation and programming experiences of collaborative robots for small-scale manufacturing

Cited by 22 publications

References 3 publications

Task Complexity and the Skills Dilemma in the Programming and Control of Collaborative Robots for Manufacturing

Task Complexity and the Skills Dilemma in the Programming and Control of Collaborative Robots for Manufacturing

Cobots in Industry 4.0: A Roadmap for Future Practice Studies on Human–Robot Collaboration

Nonparametric Bayesian Modeling of Multimodal Time Series

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