Du Guanglong, Long Shuaiying, Li Fang, Huang Xin
School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.
Guangzhou Start to Sail Industrial Robot Co., Ltd, Guangzhou, China.
Front Robot AI. 2018 Nov 6;5:125. doi: 10.3389/frobt.2018.00125. eCollection 2018.
With the development of Industry 4.0, the cooperation between robots and people is increasing. Therefore, man-machine security is the first problem that must be solved. In this paper, we proposed a novel methodology of active collision avoidance to safeguard the human who enters the robot's workspace. In the conventional approaches of obstacle avoidance, it is not easy for robots and humans to work safely in the common unstructured environment due to the lack of the intelligence. In this system, one Kinect is employed to monitor the workspace of the robot and detect anyone who enters the workspace of the robot. Once someone enters the working space, the human will be detected, and the skeleton of the human can be calculated in real time by the Kinect. The measurement errors increase over time, owing to the tracking error and the noise of the device. Therefore we use an Unscented Kalman Filter (UKF) to estimate the positions of the skeleton points. We employ an expert system to estimate the behavior of the human. Then let the robot avoid the human by taking different measures, such as stopping, bypassing the human or getting away. Finally, when the robot needs to execute bypassing the human in real time, to achieve this, we adopt a method called artificial potential field method to generate a new path for the robot. By using this active collision avoidance, the system can achieve the purpose that the robot is unable to touch on the human. This proposed system highlights the advantage that during the process, it can first detect the human, then analyze the motion of the human and finally safeguard the human. We experimentally tested the active collision avoidance system in real-world applications. The results of the test indicate that it can effectively ensure human security.
随着工业4.0的发展，机器人与人之间的协作日益增加。因此，人机安全是必须首先解决的问题。在本文中，我们提出了一种新颖的主动碰撞避免方法，以保护进入机器人工作空间的人员。在传统的避障方法中，由于缺乏智能，机器人和人类很难在常见的非结构化环境中安全工作。在该系统中，使用一个Kinect来监控机器人的工作空间并检测任何进入机器人工作空间的人。一旦有人进入工作空间，就会检测到该人，并且Kinect可以实时计算出人的骨骼。由于跟踪误差和设备噪声，测量误差会随着时间增加。因此，我们使用无迹卡尔曼滤波器（UKF）来估计骨骼点的位置。我们采用专家系统来估计人的行为。然后让机器人采取不同措施避开人，例如停止、绕过该人或离开。最后，当机器人需要实时执行绕过该人时，为实现此目的，我们采用一种称为人工势场法的方法为机器人生成新路径。通过使用这种主动碰撞避免方法，该系统可以实现机器人不会碰到人的目的。所提出的系统突出了这样的优势，即在这个过程中，它可以首先检测到人，然后分析人的运动，最后保护人。我们在实际应用中对主动碰撞避免系统进行了实验测试。测试结果表明它可以有效地确保人员安全。
