Triple stereo vision system for safety monitoring of human-robot collaboration in cellular manufacturing

Tan, Jeffrey Too Chuan; Arai, Tamio

doi:10.1109/isam.2011.5942335

Cited by 37 publications

(11 citation statements)

References 9 publications

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“…One method involved a multi-camera system to detect obstacles [19]. Tan et al [20] used a triple stereo-vision system to track the movement of a sitting operator who was wearing a color marker. References [21][22][23] implemented collision detection and avoidance based on the depth images.…”

Section: Related Workmentioning

confidence: 99%

Active Collision Avoidance for Human-Manipulator Safety

Liang

Yao

et al. 2022

IEEE Access

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This paper proposes a novel method for active collision avoidance to protect the human who enters a robot's workspace in a human-robot collaborative environment. The proposed method uses a somatosensory sensor to monitor the robot's workspace and detect anyone attempting to enter it. When someone enters the workspace, a Kinect detects and calculates the position of his or her skeleton points in real-time. However, due to the measurement errors and noise of the device, the tracking error increases over time. Therefore, the proposed method applies an improved particle filter (IPF) to accurately estimate the position of the skeleton points. In order to detect the human-robot collision in real-time, the proposed method uses cylinders to establish the bounding box model for human bones and robots and the human-robot collision is replaced by the collision between the cylinders, greatly improving the efficiency of collision detection. Moreover, taking human safety and productivity into account, the robot velocity control is carried out based on the distance between the robot and human. Then, the proposed method uses a rule-based logic system to analyze human motion so that the robot can take appropriate measures to avoid humans. Finally, the dynamic roadmap (DRM) approach plans new paths in real-time to allow robots to bypass humans. By actively avoiding collisions, the proposed method ensures that the robot will never touch the human body. The significant advantage of the proposed method is that it can detect humans in real-time, analyze their behavior and protect humans without any modification to the robot. The proposed method has been tested in practical applications, and the results show that it can successfully guarantee the safety of people entering the robot's workspace.

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Section: Related Workmentioning

confidence: 99%

Active Collision Avoidance for Human-Manipulator Safety

Liang

Yao

et al. 2022

IEEE Access

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“…The approach is able to dynamically and visually detect any safety interruption. Tan and Arai [228] reported a triple stereovision system for capturing the motion of a seated operator (upper-body only) by wearing colour markers. Nonetheless, relying on the colour consistency may not be suitable in uneven environmental lighting conditions.…”

Section: Active Collision Avoidancementioning

confidence: 99%

Symbiotic human-robot collaborative assembly

et al. 2019

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In human-robot collaborative assembly, robots are often required to dynamically change their pre-planned tasks to collaborate with human operators in a shared workspace. However, the robots used today are controlled by pre-generated rigid codes that cannot support effective human-robot collaboration. In response to this need, multi-modal yet symbiotic communication and control methods have been a focus in recent years. These methods include voice processing, gesture recognition, haptic interaction, and brainwave perception. Deep learning is used for classification, recognition and context awareness identification. Within this context, this keynote provides an overview of symbiotic human-robot collaborative assembly and highlights future research directions.

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“…A throughout treatment of the problem of object recognition in smart manufacturing is found in (Riordan et al 2019). • Operator's motion tracking: due to the stochastic nature of the operator's body, head, arms, etc., movements while executing an assembly task, the problem of monitoring and predicting the operator's motion can be considered as a particular instance of a filtering problem (Tan and Arai 2011). That is, based on a set of past possibly noisy observations of the operator's pose determine the best estimate of the current operator's motion.…”

Section: Human-robot Cooperation In Assemblymentioning

confidence: 99%

AI and ML for Human-Robot Cooperation in Intelligent and Flexible Manufacturing

Garcia

Rauch

Vidoni

et al. 2021

Implementing Industry 4.0 in SMEs

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Human–robot cooperation aims to increase the flexibilization of manufacturing systems. This requires safe human–machine interaction (e.g. with collaborative robots) as well as self and environment awareness capabilities to interact autonomously and smartly between humans and machines. Therefore, the goal of this chapter is to conceptualize and identify the set of real-time information processing and decision-making capabilities required for collaborative robots to be considered as a safe companion in the context of human–robot cooperation (HRC). In particular, the chapter provides an overview of appropriate artificial intelligence (AI) and machine learning (ML) concepts, formally introduces the concept of a safety-aware cyber-physical system and defines a general taxonomy for the perceptive and cognitive problems arising in the context of intelligent and flexible HRC.

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Triple stereo vision system for safety monitoring of human-robot collaboration in cellular manufacturing

Cited by 37 publications

References 9 publications

Active Collision Avoidance for Human-Manipulator Safety

Active Collision Avoidance for Human-Manipulator Safety

Symbiotic human-robot collaborative assembly

AI and ML for Human-Robot Cooperation in Intelligent and Flexible Manufacturing

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