Development of Integrated Innovation Experiment Platform Based on 3- PRS Parallel Mechanism

计算机科学 机制(生物学) 运动学 职位(财务) 人工智能 模拟 计算机视觉 定位技术 实时计算 哲学 物理 财务 认识论 经典力学 经济
作者
Guoqiang Chen,Hongpeng Zhou,Junjie Huang,Jiao Feng,Hanchao Li,Zhenzhen Liu
出处
期刊:Recent Patents on Mechanical Engineering [Bentham Science Publishers]
卷期号:14 (3): 396-411 被引量:1
标识
DOI:10.2174/2212797613999201228202533
摘要

Background: The parallel mechanism plays an important role in various fields. The multifunctional integrated innovation experiment platform can improve the utilization rate of the mechanism and be applied in many fields. Objective: The main objective of the study is to establish an integrated innovation experiment platform based on the 3-PRS parallel mechanism, which can be used in typical application and related technology development. Methods: The integrated innovation experiment platform is established and analyzed based on the 3-PRS parallel mechanism. According to the 3D model of the experiment platform, the kinematics and dynamics are analyzed. The force/position control strategy of the system is adopted. According to the function of the experiment platform, two kinds of application and the position and pose measurement technology are developed. The experiment platform is developed by the following methods: (1) The XY table is set up on the fixing platform of the 3-PRS parallel mechanism, so that the mechanism has five degrees of freedom, and the many kinds of workpiece can be easily processed. (2) By selecting the impedance parameter, the experiment platform can realize the compliant control of plantar flexion/dorsiflexion and varus/eversion simultaneously. (3) The binocular vision position and pose measurement method is used to obtain the marked images of the experiment platform through dual cameras, and the position and pose is obtained through image processing, 3D reconstruction and stereo matching, etc. (4) The position and pose detection based on deep learning is divided into two parts: one is to detect the slider height using the regression Convolutional Neural Network (CNN); the other is to compute the position and pose using the Back Propagation Neural Network (BPNN). Results: The experiment results show that the function of the 3-PRS parallel mechanism integrated innovation experiment platform can be effectively realized. The position and pose can be accurately measured in real time using the proposed two measurement methods. The impedance parameters are selected to achieve the rehabilitation training function of the 3-PRS ankle rehabilitation robot and the characters are processed to verify the function of the 3-PRS-XY series-parallel machine tool. Conclusion: The integrated innovation experiment platform based on the 3-PRS parallel mechanism can achieve the function of mechanical processing and rehabilitation training, and can also measure the state of motion in real time through machine vision and deep learning.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
1秒前
玉米小胚发布了新的文献求助10
2秒前
2秒前
xiami完成签到,获得积分10
3秒前
4秒前
4秒前
JHzazaza发布了新的文献求助10
4秒前
YYT完成签到,获得积分20
4秒前
4秒前
霸气映之发布了新的文献求助10
4秒前
4秒前
5秒前
Bonnie发布了新的文献求助10
5秒前
perfect完成签到,获得积分10
5秒前
向阳完成签到,获得积分10
5秒前
6秒前
小二郎应助有点儿小库采纳,获得10
6秒前
6秒前
6秒前
6秒前
庄海棠完成签到 ,获得积分10
7秒前
Xenia完成签到,获得积分10
7秒前
Mr.Su发布了新的文献求助10
7秒前
8秒前
徐扬完成签到,获得积分20
8秒前
家伟完成签到,获得积分10
8秒前
大方向真完成签到,获得积分10
8秒前
8秒前
smottom应助典雅的土豆采纳,获得10
9秒前
醉眠完成签到,获得积分10
9秒前
9秒前
9秒前
爱吃粑粑发布了新的文献求助10
10秒前
10秒前
Cuillli完成签到 ,获得积分10
10秒前
zhinian28完成签到,获得积分10
11秒前
yhsohrab发布了新的文献求助10
11秒前
11秒前
uraylong发布了新的文献求助30
12秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969335
求助须知:如何正确求助?哪些是违规求助? 3514162
关于积分的说明 11172430
捐赠科研通 3249456
什么是DOI,文献DOI怎么找? 1794853
邀请新用户注册赠送积分活动 875437
科研通“疑难数据库(出版商)”最低求助积分说明 804809