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STTR Phase II: A Design-Driven Educational Robotics Framework

STTR Phase II: A Design-Driven Educational Robotics Framework
STTR 第二阶段:设计驱动的教育机器人框架
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

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中文摘要
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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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科研奖励(0)
会议论文
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
国内基金
海外基金
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