@inproceedings{Xiao_IFAC19_visionServo,
	Abstract = {Although visual feedback has enabled a wide range of robotic capabilities such as autonomous navigation and robotic surgery, low sampling rate and time delays of visual outputs con- tinue to hinder real-time applications. When partial knowledge of the target dynamics are available, however, we show the poten- tial of significant performance gain in vision-based target follow- ing. Specifically, we propose a new framework with Kalman fil- ters and multirate model-based prediction (1) to reconstruct fast- sampled 3D target position and velocity data, and (2) to com- pensate the time delay for general robotic motion profiles. The proposed framework substantially extend the capability of con- ventional visual servo methods that apply only to static or slowly moving target. Along the path, we study the impact of model- ing choices and the delay duration, build simulation tools, and experimentally verify different algorithms with a robot manipu- lator equipped with an eye-in-hand camera.},
	Annote = {Thank you. The revised paper is attached. For the journal versions, I will consider:
Repetitive model in KF.
Test and analysis the effect of delays, see how delays impact other signal models.
Test other trajectory types.
IIR based MMP.
Maybe add adaptive estimation techniques. That is, the motion type will change during the following task.
Add more details of KF.
Update the experiment setup figure to include the robot controller and the desk computer.
Add figures to show the target trajectory in the image, 
My next priority will be the TIE paper rebuttal. At the meantime, I will do tests and discuss with you about the extensions for target tracking paper.
},
	Author = {Hui Xiao and Xu Chen},
	Booktitle = {Joint 12th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles 1st IFAC Workshop on Robot Control},
	Date-Added = {2019-03-24 17:59:30 -0400},
	Date-Modified = {2019-06-25 09:42:39 -0400},
	Keyword = {repetitive control, irregular sampling, fractional-order control},
	Title = {Following Fast-Dynamic Targets with Slow and Delayed Visual Feedback},
	Year = 2019,
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{"_id":"cP6bQznbifuh8YTPe","bibbaseid":"xiao-chen-followingfastdynamictargetswithslowanddelayedvisualfeedback-2019","authorIDs":["6c6WK2TnGgXAR7F3w"],"author_short":["Xiao, H.","Chen, X."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","abstract":"Although visual feedback has enabled a wide range of robotic capabilities such as autonomous navigation and robotic surgery, low sampling rate and time delays of visual outputs con- tinue to hinder real-time applications. When partial knowledge of the target dynamics are available, however, we show the poten- tial of significant performance gain in vision-based target follow- ing. Specifically, we propose a new framework with Kalman fil- ters and multirate model-based prediction (1) to reconstruct fast- sampled 3D target position and velocity data, and (2) to com- pensate the time delay for general robotic motion profiles. The proposed framework substantially extend the capability of con- ventional visual servo methods that apply only to static or slowly moving target. Along the path, we study the impact of model- ing choices and the delay duration, build simulation tools, and experimentally verify different algorithms with a robot manipu- lator equipped with an eye-in-hand camera.","annote":"Thank you. The revised paper is attached. For the journal versions, I will consider: Repetitive model in KF. Test and analysis the effect of delays, see how delays impact other signal models. Test other trajectory types. IIR based MMP. Maybe add adaptive estimation techniques. That is, the motion type will change during the following task. Add more details of KF. Update the experiment setup figure to include the robot controller and the desk computer. Add figures to show the target trajectory in the image, My next priority will be the TIE paper rebuttal. At the meantime, I will do tests and discuss with you about the extensions for target tracking paper. ","author":[{"firstnames":["Hui"],"propositions":[],"lastnames":["Xiao"],"suffixes":[]},{"firstnames":["Xu"],"propositions":[],"lastnames":["Chen"],"suffixes":[]}],"booktitle":"Joint 12th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles 1st IFAC Workshop on Robot Control","date-added":"2019-03-24 17:59:30 -0400","date-modified":"2019-06-25 09:42:39 -0400","keyword":["repetitive control"," irregular sampling"," fractional-order control"],"title":"Following Fast-Dynamic Targets with Slow and Delayed Visual Feedback","year":"2019","bdsk-file-1":"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","bibtex":"@inproceedings{Xiao_IFAC19_visionServo,\n\tAbstract = {Although visual feedback has enabled a wide range of robotic capabilities such as autonomous navigation and robotic surgery, low sampling rate and time delays of visual outputs con- tinue to hinder real-time applications. When partial knowledge of the target dynamics are available, however, we show the poten- tial of significant performance gain in vision-based target follow- ing. Specifically, we propose a new framework with Kalman fil- ters and multirate model-based prediction (1) to reconstruct fast- sampled 3D target position and velocity data, and (2) to com- pensate the time delay for general robotic motion profiles. The proposed framework substantially extend the capability of con- ventional visual servo methods that apply only to static or slowly moving target. Along the path, we study the impact of model- ing choices and the delay duration, build simulation tools, and experimentally verify different algorithms with a robot manipu- lator equipped with an eye-in-hand camera.},\n\tAnnote = {Thank you. The revised paper is attached. For the journal versions, I will consider:\nRepetitive model in KF.\nTest and analysis the effect of delays, see how delays impact other signal models.\nTest other trajectory types.\nIIR based MMP.\nMaybe add adaptive estimation techniques. That is, the motion type will change during the following task.\nAdd more details of KF.\nUpdate the experiment setup figure to include the robot controller and the desk computer.\nAdd figures to show the target trajectory in the image, \nMy next priority will be the TIE paper rebuttal. At the meantime, I will do tests and discuss with you about the extensions for target tracking paper.\n},\n\tAuthor = {Hui Xiao and Xu Chen},\n\tBooktitle = {Joint 12th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles 1st IFAC Workshop on Robot Control},\n\tDate-Added = {2019-03-24 17:59:30 -0400},\n\tDate-Modified = {2019-06-25 09:42:39 -0400},\n\tKeyword = {repetitive control, irregular sampling, fractional-order control},\n\tTitle = {Following Fast-Dynamic Targets with Slow and Delayed Visual Feedback},\n\tYear = 2019,\n\tBdsk-File-1 = {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}}\n\n","author_short":["Xiao, H.","Chen, X."],"key":"Xiao_IFAC19_visionServo","id":"Xiao_IFAC19_visionServo","bibbaseid":"xiao-chen-followingfastdynamictargetswithslowanddelayedvisualfeedback-2019","role":"author","urls":{},"metadata":{"authorlinks":{"chen, x":"https://xchen-me.github.io/"}}},"bibtype":"inproceedings","biburl":"https://xchen-me.github.io/bibtex_XuChen_UConn.bib","creationDate":"2021-03-19T05:11:49.015Z","downloads":0,"keywords":["repetitive control"," irregular sampling"," fractional-order control"],"search_terms":["following","fast","dynamic","targets","slow","delayed","visual","feedback","xiao","chen"],"title":"Following Fast-Dynamic Targets with Slow and Delayed Visual Feedback","year":2019,"dataSources":["hHZrcJyhveBr8MCqA"]}