Working Together: A Review on Safe Human-Robot Collaboration in Industrial Environments

Robla-Gómez, Sandra; Becerra, Victor M.; Llata, J.R.; Gonzalez‐Sarabia, Esther; Torre-Ferrero, Carlos; Pérez-Oria, Juan

doi:10.1109/access.2017.2773127

Cited by 449 publications

(193 citation statements)

References 97 publications

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“…This strategy has a goal to set up a secure environment for human-robot collaboration. The framework for safety in industrial robot collaborative environments can be found in [20] where CPS is currently included as part of recent development in intelligent manufacturing. The use of CPS helps to bring the sharing of workspace for human-robot collaboration.…”

Section: New Era Of Industrial Robotsmentioning

confidence: 99%

“…This feature may serve to simulate robot-robot collaborations and human-robot collaborations in different scenarios. The main safety system for human-robot collaboration and the essential sensors as discussed in [20] are feasible if constructed using Marilou simulation, because it has similar devices (actuators and sensors) as given in Table 2 that can be incorporated in developed collaborative robots. These sensors include force and torque sensors, touch area, laser range finder, LIDAR, and bumper where these components can be used for safety in collaborative robots.…”

Section: Robot Simulationsmentioning

confidence: 99%

“…Several examples of such robots are Bosch APAS assistant, KUKA LBR iiwa, ABB YuMi, FANUC CR-35iA, MRK Systeme KR 5 SI, and Universal Robots UR5. The importance of collaborative robots is to increase the productivity and efficiency (as demanded in new industrial paradigm) when they work side by side with humans [20]. The deployment of these collaborative robots in the new industrialization era raises the potential for complex human-robot interaction (HRI) to create highly flexible processes by mean of symbiotic human-robot collaborative process [21].…”

Section: Introductionmentioning

confidence: 99%

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Computer Simulation of Human-Robot Collaboration in the Context of Industry Revolution 4.0

Rizal¹

2020

Becoming Human With Humanoid - From Physical Interaction to Social Intelligence

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The essential role of robot simulation for industrial robots, in particular the collaborative robots is presented in this chapter. We begin by discussing the robot utilization in the industry which includes mobile robots, arm robots, and humanoid robots. The author emphasizes the application of collaborative robots in regard to industry revolution 4.0. Then, we present how the collaborative robot utilization in the industry can be achieved through computer simulation by means of virtual robots in simulated environments. The robot simulation presented here is based on open dynamic engine (ODE) using anyKode Marilou. The author surveys on the use of dynamic simulations in application of collaborative robots toward industry 4.0. Due to the challenging problems which related to humanoid robots for collaborative robots and behavior in human-robot collaboration, the use of robot simulation may open the opportunities in collaborative robotic research in the context of industry 4.0. As developing a real collaborative robot is still expensive and time-consuming, while accessing commercial collaborative robots is relatively limited; thus, the development of robot simulation can be an option for collaborative robotic research and education purposes.

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Section: New Era Of Industrial Robotsmentioning

confidence: 99%

Section: Robot Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computer Simulation of Human-Robot Collaboration in the Context of Industry Revolution 4.0

Rizal¹

2020

Becoming Human With Humanoid - From Physical Interaction to Social Intelligence

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“…Therefore, it is not surprising that a lot of effort has gone into identifying and implementing suitable HRC safety measures [1]. Typically, the efforts focus on designing collaborative workspaces to minimize interferences between human and robot activities [2], installing redundant safety protocols and emergency robot activity termination mechanisms through multiple sensors [2], and developing both predictive and reactive collision avoidance strategies [3]. These efforts have resulted in the expanded acceptance and use of industrial robots, both mobile and stationary, leading to increased operational flexibility, productivity, and quality [4].…”

Section: Introductionmentioning

confidence: 99%

Toward Ergonomic Risk Prediction via Segmentation of Indoor Object Manipulation Actions Using Spatiotemporal Convolutional Networks

Parsa

Samani

Hendrix

et al. 2019

IEEE Robot. Autom. Lett.

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Automated real-time prediction of the ergonomic risks of manipulating objects is a key unsolved challenge in developing effective human-robot collaboration systems for logistics and manufacturing applications. We present a foundational paradigm to address this challenge by formulating the problem as one of action segmentation from RGB-D camera videos. Spatial features are first learned using a deep convolutional model from the video frames, which are then fed sequentially to temporal convolutional networks to semantically segment the frames into a hierarchy of actions, which are either ergonomically safe, require monitoring, or need immediate attention. For performance evaluation, in addition to an opensource kitchen dataset, we collected a new dataset comprising twenty individuals picking up and placing objects of varying weights to and from cabinet and table locations at various heights. Results show very high (87-94)% F1 overlap scores among the ground truth and predicted frame labels for videos lasting over two minutes and consisting of a large number of actions.

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“…Industrial robotics with advanced technology for the industrial automation field has received considerable attention in recent years [1][2][3][4][5][6]. Although the development and application of industrial robots are confined-to a certain extent-to the field of advanced manufacturing due to the lack of rigidity and precision of an industrial robot compared to a numerical control machine, the industrial robot, as a common and less expensive alternative to the latter, has great advantages and potentials especially in some applications that require low cutting forces, low precision requirements, large-sized complex shaped parts and/or multi-faces machining in one setup.…”

Section: Introductionmentioning

confidence: 99%

A Robotic Deburring Methodology for Tool Path Planning and Process Parameter Control of a Five-Degree-of-Freedom Robot Manipulator

Guo

Zhu

et al. 2019

Applied Sciences

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Industrial robotics is a continuously developing domain, as industrial robots have demonstrated to possess benefits with regard to robotic automation solutions in the industrial automation field. In this article, a new robotic deburring methodology for tool path planning and process parameter control is presented for a newly developed five-degree-of-freedom hybrid robot manipulator. A hybrid robot manipulator with dexterous manipulation and two experimental platforms of robot manipulators are presented. A robotic deburring tool path planning method is proposed for the robotic deburring tool position and orientation planning and the robotic layered deburring planning. Also, a robotic deburring process parameter control method is proposed based on fuzzy control. Furthermore, a dexterous manipulation verification experiment is conducted to demonstrate the dexterous manipulation and the orientation reachability of the robot manipulator. Additionally, two robotic deburring experiments are conducted to verify the effectiveness of the two proposed methods and demonstrate the highly efficient and dexterous manipulation and deburring capacity of the robot manipulator.

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Working Together: A Review on Safe Human-Robot Collaboration in Industrial Environments

Cited by 449 publications

References 97 publications

Computer Simulation of Human-Robot Collaboration in the Context of Industry Revolution 4.0

Computer Simulation of Human-Robot Collaboration in the Context of Industry Revolution 4.0

Toward Ergonomic Risk Prediction via Segmentation of Indoor Object Manipulation Actions Using Spatiotemporal Convolutional Networks

A Robotic Deburring Methodology for Tool Path Planning and Process Parameter Control of a Five-Degree-of-Freedom Robot Manipulator

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