STTR Phase II: A Design-Driven Educational Robotics Framework
STTR Phase II: A Design-Driven Educational Robotics Framework
批准号:
2126882
负责人:
Wei Liao
金额:
$100.0万
依托单位:
依托单位国家:
美国
项目类别:
Cooperative Agreement
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-09-15 至 2024-08-31
中文摘要
这个小企业技术转移(STTR)第二阶段项目的更广泛影响是,在充满乐趣的环境中教授K-12和大学生STEM概念,设计、制造和编程机器和机器人,并为他们未来的技术驱动工作做好准备。该项目旨在让所有年级的学生都能负担得起、公平和包容的机器人教育。通过采取多学科和跨课程的方法,通过该项目开发的产品使学生,特别是来自不同背景的妇女和少数群体,有效地参与STEM学科,并帮助他们做出适当的职业选择。该项目的最终目标与NSF的任务相一致,即支持发展一支强大的STEM劳动力队伍,并帮助填补根据当前美国劳工统计局的250万个STEM职位空缺。该项目产生的技术创新将产生一种用于合成机器人运动的计算工具,可广泛用于工业自动化和机器和机器人的发明。拟议的设计驱动型教育机器人产品有可能改善与科学技术的接触,并对美国教育机器人市场产生积极影响,预计到2021年,美国教育机器人市场将增长到27亿美元。这个小企业技术转移第二阶段项目旨在将刚体运动学、机器学习和工程设计结合在一起,为K-12学校、大学新生项目和STEM夏令营创建设计驱动型机器人教育的新产品。领先的商用教育机器人系统强调1)使用许多专门部件对机器人结构进行指令驱动的原型设计,以及2)对它们进行编程,而不提供设计过程背后的任何直觉或为实现机器人运动而创建机构设计概念的指导。在跟上不断发展的技术和社会经济需求、新的科学标准和保持竞争力的需要的推动下,学校和营地越来越多地采用STEM和机器人计划和产品。这款产品将填补目前教育市场存在的空白。如果成功,这个项目将产生一个最先进的动作设计软件,一个新的硬件套件,以及为学校和大学提供的符合标准的课程和学习资源。该软件基于机械设计研究中的机器学习,将允许在构建相应的物理模型之前创建机器人组件及其运动,并在设计过程中为学生提供必要的技能和经验。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
The broader impact of this Small Business Technology Transfer (STTR) Phase II project is in teaching K-12 and college students STEM concepts in the fun-filled context of designing, building, and programming machines and robots and preparing them for the future technology-driven jobs. This project seeks to make robotics education affordable, equitable, and inclusive for students at all grade levels. By taking a multi-disciplinary and cross-curricula approach, the product developed through this project engages students, especially women and minorities from diverse backgrounds, effectively in STEM disciplines and helps them make suitable career choices. The ultimate goals of this project are aligned with the NSF's mandate to support development of a strong STEM workforce and help fill the 2.5 million STEM jobs that are vacant according to the current US Department of Labor Statistics. The technological innovations emerging from this project would result in a computational tool for synthesis of robot motions, which can be used broadly in industrial automation and invention of machines and robots. The proposed design-driven educational robotics product has the potential to improve engagement with science and technology and positively impact the U.S. educational robotics market, which is expected to grow to $2.7 billion by 2021.This Small Business Technology Transfer Phase II project aims to bring together rigid body kinematics, machine learning, and engineering design to create a new product for design-driven robotics education for K-12 schools, freshman college programs, and STEM camps. The leading commercially available educational robotic systems emphasize 1) instruction-driven prototyping of robot structures using many specialized parts, and 2) programming them without providing any intuition behind the design process or guidance to creating mechanism design concepts for the realization of the motion of the robots. Driven by a need to keep pace with the evolving techno- and socio-economic requirements, new science standards, and remain competitive, schools and camps are increasingly adopting STEM and Robotics programs and products. This product would fill that void that currently exists in the educational market. If successful, this project would result in a state-of-the-art motion design software, a novel hardware kit, and standards-aligned curriculum and learning resources for schools and colleges. The software based on the machine learning in mechanism design research will allow creation of robot assemblies and their motions before constructing their corresponding physical models and provide students necessary skills and experience in the design process.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(4)
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A Unified Real-Time Motion Generation Algorithm for Approximate Position Analysis of Planar N-Bar Mechanisms
平面 N 杆机构近似位置分析的统一实时运动生成算法
DOI:
10.1115/1.4064132
发表时间:
2024
期刊:
Journal of Mechanical Design
影响因子:
3.3
作者:
[Lyu, Zhijie, Purwar, Anurag, Liao, Wei]
通讯作者:
Liao, Wei
Transforming Hand-Drawn Sketches of Linkage Mechanisms Into Their Digital Representation
将联动机构的手绘草图转换为数字表示形式
DOI:
10.1115/1.4064037
发表时间:
2024
期刊:
Journal of Computing and Information Science in Engineering
影响因子:
3.1
作者:
[Nurizada, Anar, Purwar, Anurag]
通讯作者:
Purwar, Anurag
An Invariant Representation of Coupler Curves Using a Variational AutoEncoder: Application to Path Synthesis of Four-Bar Mechanisms
使用变分自动编码器的耦合器曲线的不变表示:在四杆机构路径综合中的应用
DOI:
10.1115/1.4063726
发表时间:
2024
期刊:
Journal of Computing and Information Science in Engineering
影响因子:
3.1
作者:
[Nurizada, Anar, Purwar, Anurag]
通讯作者:
Purwar, Anurag
Deep Learning-Driven Design of Robot Mechanisms
深度学习驱动的机器人机构设计
DOI:
10.1115/1.4062542
发表时间:
2023
期刊:
Journal of Computing and Information Science in Engineering
影响因子:
3.1
作者:
[Purwar, Anurag, Chakraborty, Nilanjan]
通讯作者:
Chakraborty, Nilanjan
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