Experimental Autonomous Deep Learning-Based 3D Path Planning for a 7-DOF Robot Manipulator
Experimental Autonomous Deep Learning-Based 3D Path Planning for a 7-DOF Robot Manipulator
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DOI：
10.1115/dscc2019-8951
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发表时间：
2019-10
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影响因子：
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通讯作者：
Alex Bertino;M. Bagheri;M. Krstić;Peiman Naseradinmousavi
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文献类型：
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作者：
Alex Bertino;M. Bagheri;M. Krstić;Peiman Naseradinmousavi
在本文中，我们研究了高自由度机器人操纵器的自主操作。我们研究了一个拾取和放置任务，其中物体、障碍物和目标垫的位置和方向最初是未知的，需要自主确定。为了完成这项任务，我们结合了计算机视觉、深度学习和控制技术。首先，我们利用基于 HSV 的扫描在两张捕获的图像中定位每件物品的中心。其次，我们利用立体视觉技术来确定每个项目的 3D 位置。第三，我们实现卷积神经网络来确定物体的方向。最后，我们使用计算出的每个物品的 3D 位置来建立避障轨迹，将物品提升到障碍物上方并到达目标垫上。通过我们的研究结果，我们证明我们的技术组合误差最小，能够实时运行，并且能够可靠地执行任务。因此，我们证明，通过结合专门的自主技术，可以推广到复杂的自主任务。
In this paper, we examine the autonomous operation of a high-DOF robot manipulator. We investigate a pick-and-place task where the position and orientation of an object, an obstacle, and a target pad are initially unknown and need to be autonomously determined. In order to complete this task, we employ a combination of computer vision, deep learning, and control techniques. First, we locate the center of each item in two captured images utilizing HSV-based scanning. Second, we utilize stereo vision techniques to determine the 3D position of each item. Third, we implement a Convolutional Neural Network in order to determine the orientation of the object. Finally, we use the calculated 3D positions of each item to establish an obstacle avoidance trajectory lifting the object over the obstacle and onto the target pad. Through the results of our research, we demonstrate that our combination of techniques has minimal error, is capable of running in real-time, and is able to reliably perform the task. Thus, we demonstrate that through the combination of specialized autonomous techniques, generalization to a complex autonomous task is possible.