The transition to agile manufacturing, Industry 4.0, and high-mix-low-volume
tasks require robot programming solutions that are flexible. However, most
deployed robot solutions are still statically programmed and use stiff position
control, which limit their usefulness. In this paper, we show how a single
robot skill that utilizes knowledge representation, task planning, and
automatic selection of skill implementations based on the input parameters can
be executed in different contexts. We demonstrate how the skill-based control
platform enables this with contact-rich wiping tasks on different robot
systems. To achieve that in this case study, our approach needs to address
different kinematics, gripper types, vendors, and fundamentally different
control interfaces. We conducted the experiments with a mobile platform that
has a Universal Robots UR5e 6 degree-of-freedom robot arm with position control
and a 7 degree-of-freedom KUKA iiwa with torque control.

该研究论文探讨了灵活制造、工业 4.0 和高混合低容量任务所需的机器人编程解决方案，并展示了如何使用知识表示、任务规划和基于输入参数的技能实现的自动选择，在不同的环境中执行具有接触性擦拭任务的单一机器人技能。

利用知识表示和任务规划实现机器人无关的接触丰富擦拭任务技能

Using Knowledge Representation and Task Planning for Robot-agnostic  Skills on the Example of Contact-Rich Wiping Tasks

It is important to develop sustainable processes in materials science and
manufacturing that are environmentally friendly. AI can play a significant role
in decision support here as evident from our earlier research leading to tools
developed using our proposed machine learning based approaches. Such tools
served the purpose of computational estimation and expert systems. This
research addresses environmental sustainability in materials science via
decision support in agile manufacturing using recycled and reclaimed materials.
It is a safe and responsible way to turn a specific waste stream to value-added
products. We propose to use data-driven methods in AI by applying machine
learning models for predictive analysis to guide decision support in
manufacturing. This includes harnessing artificial neural networks to study
parameters affecting heat treatment of materials and impacts on their
properties; deep learning via advances such as convolutional neural networks to
explore grain size detection; and other classifiers such as Random Forests to
analyze phrase fraction detection. Results with all these methods seem
promising to embark on further work, e.g. ANN yields accuracy around 90\% for
predicting micro-structure development as per quench tempering, a heat
treatment process. Future work entails several challenges: investigating
various computer vision models (VGG, ResNet etc.) to find optimal accuracy,
efficiency and robustness adequate for sustainable processes; creating
domain-specific tools using machine learning for decision support in agile
manufacturing; and assessing impacts on sustainability with metrics
incorporating the appropriate use of recycled materials as well as the
effectiveness of developed products. Our work makes impacts on green technology
for smart manufacturing, and is motivated by related work in the highly
interesting realm of AI for materials science.

本研究通过数据驱动的 AI 方法，应用机器学习模型，探索采用再生和回收材料的敏捷制造决策支持的材料科学中的环境可持续性，通过对热处理参数对材料性质的影响、颗粒尺寸检测和短语分数检测进行预测分析，得出了很多有希望的结果，但是之后的工作仍需面对各种挑战。