Towards Robotic Metal Scrap Cutting: A Novel Workflow and Pipeline for Cutting Path Generation
Towards Robotic Metal Scrap Cutting: A Novel Workflow and Pipeline for Cutting Path Generation
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DOI：
10.1109/case49439.2021.9551645
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
James Akl;Fadi M. Alladkani;B. Çalli
中科院分区：
文献类型：
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作者：
James Akl;Fadi M. Alladkani;B. Çalli
我们提出了一个新的框架机器人金属废料切割在非结构化废料场。在这个框架中，机器人和工人合作：工人用喷漆在废金属上标记切割位置，然后机器人生成切割轨迹。这充分利用了工人的专业知识，同时把枯燥、肮脏、危险的工作交给了机器人。对于机器人来说，这需要一个三维探索和曲线重建阶段来生成路径。我们采用非均匀有理基样条（NURBS）模型和拓扑骨架化方法进行路径生成，并通过仿真实现和比较了这两种方法。这些模拟采用了一个真实的传感器噪声模型和对复杂的、真实的碎片进行高度详细的三维扫描。并给出了三种不同形状的真实机器人实验。
We propose a novel framework for robotic metal scrap cutting in unstructured scrap yards. In this framework the robots and workers collaborate: the worker marks the cutting locations on the scrap metal with spray paint and the robot then generates the cutting trajectories. This leverages worker expertise, while deferring the dull, dirty, dangerous aspects to the robot. For the robot, this requires a 3-D exploration and curve reconstruction stage for path generation. We use a non-uniform rational basis spline (NURBS) model and a topological skeletonization method for path generation, and implement and compare these methods via simulations. These simulations employ a realistic sensor noise model and highly-detailed 3-D scans of complex, real-life scrap pieces. Real-robot experiments with three different shapes are also provided.