Human-Robot Collaboration: Twofold Strategy Algorithm to Avoid Collisions Using ToF Sensor

Plapper, Peter

doi:10.7763/ijmmm.2016.v4.243

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Przykładowo, system z kamerą RGBD [19] pozwala na dynamiczną zmianę trajektorii robota KUKA przy ruchomych przeszkodach. Z kolei system z kamerą ToF [20] skutecznie wykrywa zajętość strefy współpracy między człowiekiem i robotem.…”

Section: Systemy Bezpieczeństwa Cobotówunclassified

Safety System for an Industrial Cooperating Robot Based on Depth Cameras

Masłowski¹,

Czubenko²

2019

PAR

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Section: Systemy Bezpieczeństwa Cobotówunclassified

Safety System for an Industrial Cooperating Robot Based on Depth Cameras

Masłowski¹,

Czubenko²

2019

PAR

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“…Different approaches related to this human-robot collaboration using computer vision are summarized in [1]. In [2], a ToF (time of flight) camera is used to detect obstacles in the robot's path and recalculate the trajectory to avoid contact. Other sensors such as 2D cameras [3] or a RGBD camera, used to recreate a virtual environment for the robot and the human, and monitor possible collisions [4] have been used.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Safe Human-Robot Collaboration

Duque

Mejia

Martinez

et al. 2021

Advances in Automation and Robotics Research

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Recent advances in computer vision and deep learning have lead to implementations in different industrial applications such as collaborative robotics, making robots able to perform harder tasks and giving them consciousness of their environment, easing interaction with humans. With the objective of eliminating physical barriers between humans and robots, a security system for industrial collaborative robots based on computer vision and deep learning is proposed, where an RGBD camera is used to detect and track people located inside the robot's workspace. Detection is made with a previously trained convolutional neural network. The position of every detection is fed to the tracker, that identifies the subjects in scene and keeps record of them in case the detector fails. The detected subject's 3D position and height are represented in a simulation of the workspace, where the robot's speed changes depending on its distance to the manipulator following international safety guidelines. This paper shows the implementation of the detector and tracker algorithms, the subject's 3D position, the security zones definition and the integration of the vision system with the robot and workspace. Results show the system's ability to detect and track subjects in scene, and the robot's capacity to change its speed depending on the subject's location.

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“…A robot manipulator automatic path planning strategy based on 3D-TOF sensor was presented in peg-in-hole assembly process [11]. Similarly, a two fold strategy was presented to automatically generate safe path for robot trajectory based on data from TOF sensor [12]. Kuhn [13] used monocular vision to measure the human-robot distance in manipulator space and thus identified the risk.…”

Section: Introductionmentioning

confidence: 99%

A Robot Collision Avoidance Method Using Kinect and Global Vision

Huang

Yang

et al. 2017

TELKOMNIKA

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This paper introduces a robot collision avoidance method using Kinect and global vision to improve the industrial robot's security. Global vision is installed above the robot, and a combination of the background-difference method and the Otsu algorithm are used. Human skeleton detection is then Keywords: robot safety, human body skeleton detection, Kalman filter, global vision, security controlCopyright © 2017 Universitas Ahmad Dahlan. All rights reserved. IntroductionSafety control strategy must be taken to avoid the collision between the moving robot and the operators in the working space [1]. Strategies used in safe human-robot collaboration (HRC) can be broadly divided into two categories: pre-collision strategy [2-4] and post-collision strategy [5]. The former strategy detects the danger before the collision and takes measures to prevent imminent collision. While the latter one requires higher real-time performance during the collision to suppress the impact force, and ensure the security of operators and robots. Therefore, pre-collision methods can normally achieve safer result in implementation of HRC. Researchers have been working on different pre-collision methods and presenting some important findings and solutions. As one of the pre-collision methods, sensors, such as ultrasonic [6] and photoelectric sensors, are installed on the robot to detect the man-machine position. The sensors identify the danger and then the robots will be immediately stopped. Such security strategy is very simple, and greatly reduce the reliability of collision avoidance and the work efficiency of the robot.The robot should follow the collision avoidance strategy at all run time. A more reliable safety strategy is essential. Sanderud [7] have presented proactive safety strategy based on a quantified measure of risk for human robot collaboration. The risk field is established based on an analysis of the human's movement and the consequence of a collision with different human limbs, combined with a likelihood analysis. Similarly, a simulation tool using real-world geometrical data was proposed to investigate different algorithms and safety strategies [8]. Kulić [9, 10] suggested a method of robot safe trajectory planning based on the mechanical principle of the minimizing danger index. However, the application of this method is limited by the large number of environmental data required.One of the most common and useful technologies used to detect intruding obstacles is robot vision. The robot vision has been developed in recent years, and could be a feasible solution in the collision avoidance strategy. A robot manipulator automatic path planning strategy based on 3D-TOF sensor was presented in peg-in-hole assembly process [11]. Similarly, a two fold strategy was presented to automatically generate safe path for robot trajectory based on data from TOF sensor [12]. Kuhn [13] used monocular vision to measure the human-robot distance in manipulator space and thus identified the risk. Yet this method is not conducive to th...

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Human-Robot Collaboration: Twofold Strategy Algorithm to Avoid Collisions Using ToF Sensor

Cited by 7 publications

References 11 publications

Safety System for an Industrial Cooperating Robot Based on Depth Cameras

Safety System for an Industrial Cooperating Robot Based on Depth Cameras

Deep Learning for Safe Human-Robot Collaboration

A Robot Collision Avoidance Method Using Kinect and Global Vision

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