Hernandez-Vicen Juan, Martinez Santiago, Garcia-Haro Juan Miguel, Balaguer Carlos
Systems Engineering and Automation Department, Universidad Carlos III de Madrid, Avd. Universidad, 30, Leganés, 28903 Madrid, Spain.
Sensors (Basel). 2018 Mar 25;18(4):972. doi: 10.3390/s18040972.
New applications related to robotic manipulation or transportation tasks, with or without physical grasping, are continuously being developed. To perform these activities, the robot takes advantage of different kinds of perceptions. One of the key perceptions in robotics is vision. However, some problems related to image processing makes the application of visual information within robot control algorithms difficult. Camera-based systems have inherent errors that affect the quality and reliability of the information obtained. The need of correcting image distortion slows down image parameter computing, which decreases performance of control algorithms. In this paper, a new approach to correcting several sources of visual distortions on images in only one computing step is proposed. The goal of this system/algorithm is the computation of the tilt angle of an object transported by a robot, minimizing image inherent errors and increasing computing speed. After capturing the image, the computer system extracts the angle using a Fuzzy filter that corrects at the same time all possible distortions, obtaining the real angle in only one processing step. This filter has been developed by the means of Neuro-Fuzzy learning techniques, using datasets with information obtained from real experiments. In this way, the computing time has been decreased and the performance of the application has been improved. The resulting algorithm has been tried out experimentally in robot transportation tasks in the humanoid robot TEO (Task Environment Operator) from the University Carlos III of Madrid.
与机器人操作或运输任务相关的新应用正在不断开发，无论是否涉及物理抓取。为了执行这些活动，机器人利用了不同类型的感知。机器人技术中的关键感知之一是视觉。然而，一些与图像处理相关的问题使得视觉信息在机器人控制算法中的应用变得困难。基于摄像头的系统存在固有误差，会影响所获取信息的质量和可靠性。校正图像失真的需求减缓了图像参数计算，从而降低了控制算法的性能。本文提出了一种在单个计算步骤中校正图像上多种视觉失真源的新方法。该系统/算法的目标是计算由机器人运输的物体的倾斜角度，最大限度地减少图像固有误差并提高计算速度。在捕获图像后，计算机系统使用模糊滤波器提取角度，该滤波器同时校正所有可能的失真，仅在一个处理步骤中获得真实角度。该滤波器是通过神经模糊学习技术开发的，使用了从实际实验中获得信息的数据集。通过这种方式，计算时间减少了，应用程序的性能得到了提高。所得算法已在马德里卡洛斯三世大学的人形机器人TEO（任务环境操作员）的机器人运输任务中进行了实验验证。
