Cooperation Between a High-Power Robot and a Human by Functional Safety

Morioka, Masahiro; Adachi, Satoshi; Sakakibara, Shinsuke; Tan, Jeffrey Too Chuan; Kato, Ryu; Arai, Tamio

doi:10.20965/jrm.2011.p0926

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…However, MASS mainly focuses on the safety rules category within the hybrid assembly and disregards any collaboration between the human and the robot co-worker. The recent research in [11] addresses a safety-control strategy for a robot co-worker by monitoring the position of the robot end-effector. Speed control for the end-effector is categorized into different stages within the working space.…”

Section: Introductionmentioning

confidence: 99%

“…In this way, the robotic system is also able to take advantage of the human operator's sense, sensibility and resourcefulness to complete a required task. On the other hand, the human can utilize the high-precision, strength and repeatability of the robotic system, and thus reduce fatigue and the risk of injury as well as increase overall work safety [1, 11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models

Chen

Zhong

Cannella

et al. 2015

International Journal of Advanced Robotic Systems

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Gesture recognition is essential for human and robot collaboration. Within an industrial hybrid assembly cell, the performance of such a system significantly affects the safety of human workers. This work presents an approach to recognizing hand gestures accurately during an assembly task while in collaboration with a robot co-worker. We have designed and developed a sensor system for measuring natural human-robot interactions. The position and rotation information of a human worker's hands and fingertips are tracked in 3D space while completing a task. A modified chain-code method is proposed to describe the motion trajectory of the measured hands and fingertips. The Hidden Markov Model (HMM) method is adopted to recognize patterns via data streams and identify workers' gesture patterns and assembly intentions. The effectiveness of the proposed system is verified by experimental results. The outcome demonstrates that the proposed system is able to automatically segment the data streams and recognize the gesture patterns thus represented with a reasonable accuracy ratio.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models

Chen

Zhong

Cannella

et al. 2015

International Journal of Advanced Robotic Systems

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“…Every section will be assigned with a warning and hazard zone based on 3D technology to control the interaction. When no operator is in close proximity to the wall the robots are working without any limitations and at full speed (see (1) in Figure 2). The scenario of a human operator entering the cell to cooperate with robot on a wall segment can be described in steps:…”

Section: Safety Zone Conceptmentioning

confidence: 99%

“…Therefore, new solutions have emerged where the safety issues still are strictly regulated but the opportunity to create a shared space is available. This often includes the usage of speed, force monitoring, cameras, light curtains and usage of potential fields to detect an obstacle and guide the robot away [1][2].…”

Section: Introductionmentioning

confidence: 99%

Human and robot interaction based on safety zones in a shared work environment

Augustsson

Christiernin

Bolmsjö

2014

Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction

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In this paper, early work on how to implement flexible safety zones is presented. In the case study an industrial robot cell emulates the environment at a wall construction site, with a robot performing nailing routines. Tests are performed with humans entering the safety zones of a SafetyEye system. The zone violation is detected, and new warning zones initiated. The robot retracts but continues its work tasks with reduced speed and within a safe distance of the human operator. Interaction is achieved through simultaneous work on the same work piece and the warning zones can be initiated and adjusted in a flexible way.

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“…This does not eliminate the human workforce, only reorienting it, maintaining work flexibility and efficiency and significantly increasing performance [21,22]. In some industrial countries, the introduction of collaborative robots would be a solution to the problem of a decreasing number of skilled workers [23]. Human-robot collaboration is now becoming a major technology of Industry 4.0 and is changing the character of manufacturing companies.…”

Section: Introductionmentioning

confidence: 99%

Ergonomics Evaluation Using Motion Capture Technology—Literature Review

et al. 2022

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Due to the increasingly high proportion of manual activities in production processes, there is a constant risk of musculoskeletal disorders or work-related injuries. The risk of these problems is exacerbated by the trend towards an ageing working population. European legislation is pressing for improved working conditions to eliminate the risks associated with health problems for workers. For this reason, the application of ergonomics in this field is growing. Musculoskeletal disorders, which are most often caused by inappropriate working postures, are a major problem. There are many methods for evaluating working postures. However, there is a high degree of subjectivity in the risk assessment. Motion capture kinematic suits can ensure the objectivity of the assessment. This article discusses research on ergonomics assessment using motion capture technology. A systematic literature search method was used for the research, beginning with the determination of the research procedure, through the definition of the research queries, to the formulation of the research itself to identify relevant sources. The study presents the most widely used methods for assessing the ergonomics of work positions using motion capture technology, their advantages, and disadvantages. It also follows the trend in the number of publications between 2010 and 2022 in countries where the topic is most frequently addressed and in the industries where motion capture technology is used for ergonomics assessment in general. The research showed that this approach is most often used in industry and logistics, and less frequently in healthcare and sport. The authors agree that the most frequently used ergonomics assessment methods are not complex enough to be used in combination with motion capture and that a combination of the two is needed. At the same time, this technology has become very important in the field of ergonomic evaluation of work positions, offering a higher degree of objectivity, or can be combined with the use of virtual reality, but the evaluation systems are still not error-free and there is a need for continuous improvement.

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Cooperation Between a High-Power Robot and a Human by Functional Safety

Cited by 5 publications

References 6 publications

Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models

Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models

Human and robot interaction based on safety zones in a shared work environment

Ergonomics Evaluation Using Motion Capture Technology—Literature Review

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