A Robot Collision Avoidance Method Using Kinect and Global Vision

Abstract

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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