O. Manyar, Alec Kanyuck, Bharat Deshkulkarni, S. Gupta
{"title":"Visual Servo Based Trajectory Planning for Fast and Accurate Sheet Pick and Place Operations","authors":"O. Manyar, Alec Kanyuck, Bharat Deshkulkarni, S. Gupta","doi":"10.1115/msec2022-85952","DOIUrl":null,"url":null,"abstract":"\n In industry, several operations require sheet-like materials to be transported from a loading station to the desired location. Such applications are prevalent in the aerospace and textile industry where composite prepreg sheets or fabrics are placed over a tool or fed to a machine. Using robots for sheet transport operations offers a flexible solution for such highly complex tasks. To create high-quality parts, sheets need to be accurately placed at the correct location. This paper presents automated trajectory planning and control algorithms for a robot to pick up sheets from the input station using suction grippers and, transport and place them over the tool surface. Machine vision is used at the pick location for estimating the sheet pose. Unfortunately, pick-up accuracy is not sufficiently high due to sheet movement during suction-based grasping and localization errors. We employ ideas inspired by visual servo techniques to accurately place the sheet on the tool. Our method uses an Eye-to-Hand camera configuration to align the desired image features with the reference markings on the tool. We introduce a sampling-based Jacobian estimation scheme that can reliably achieve the desired accuracy while minimizing the operation time. Experiments are performed to validate our methodology and compute the placement accuracy on an industrial tool.","PeriodicalId":45459,"journal":{"name":"Journal of Micro and Nano-Manufacturing","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Micro and Nano-Manufacturing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/msec2022-85952","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
In industry, several operations require sheet-like materials to be transported from a loading station to the desired location. Such applications are prevalent in the aerospace and textile industry where composite prepreg sheets or fabrics are placed over a tool or fed to a machine. Using robots for sheet transport operations offers a flexible solution for such highly complex tasks. To create high-quality parts, sheets need to be accurately placed at the correct location. This paper presents automated trajectory planning and control algorithms for a robot to pick up sheets from the input station using suction grippers and, transport and place them over the tool surface. Machine vision is used at the pick location for estimating the sheet pose. Unfortunately, pick-up accuracy is not sufficiently high due to sheet movement during suction-based grasping and localization errors. We employ ideas inspired by visual servo techniques to accurately place the sheet on the tool. Our method uses an Eye-to-Hand camera configuration to align the desired image features with the reference markings on the tool. We introduce a sampling-based Jacobian estimation scheme that can reliably achieve the desired accuracy while minimizing the operation time. Experiments are performed to validate our methodology and compute the placement accuracy on an industrial tool.
期刊介绍:
The Journal of Micro and Nano-Manufacturing provides a forum for the rapid dissemination of original theoretical and applied research in the areas of micro- and nano-manufacturing that are related to process innovation, accuracy, and precision, throughput enhancement, material utilization, compact equipment development, environmental and life-cycle analysis, and predictive modeling of manufacturing processes with feature sizes less than one hundred micrometers. Papers addressing special needs in emerging areas, such as biomedical devices, drug manufacturing, water and energy, are also encouraged. Areas of interest including, but not limited to: Unit micro- and nano-manufacturing processes; Hybrid manufacturing processes combining bottom-up and top-down processes; Hybrid manufacturing processes utilizing various energy sources (optical, mechanical, electrical, solar, etc.) to achieve multi-scale features and resolution; High-throughput micro- and nano-manufacturing processes; Equipment development; Predictive modeling and simulation of materials and/or systems enabling point-of-need or scaled-up micro- and nano-manufacturing; Metrology at the micro- and nano-scales over large areas; Sensors and sensor integration; Design algorithms for multi-scale manufacturing; Life cycle analysis; Logistics and material handling related to micro- and nano-manufacturing.