Robotic Manipulation Planning for Automatic Peeling of Glass Substrate Based on Online Learning Model Predictive Path Integral.

Liwei Hou, Hengsheng Wang, Haoran Zou, Yalin Zhou

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Liwei HouCollege of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.

Hengsheng WangCollege of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.

Haoran ZouCollege of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.

Yalin ZhouCollege of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.

Funding

Fundamental Research Funds for the Central Universities of Central South University 2019zzts252National Basic Research Program of China 2013CB035504National Natural Science Foundation of China 51975587

6 · The paper itself

Abstract

Autonomous planning robotic contact-rich manipulation has long been a challenging problem. Automatic peeling of glass substrates of LCD flat panel displays is a typical contact-rich manipulation task, which requires extremely high safe handling through the manipulation process. To this end of peeling glass substrates automatically, the system model is established from data and is used for the online planning of the robot motion in this paper. A simulation environment is designed to pretrain the process model with deep learning-based neural network structure to avoid expensive and time-consuming collection of real-time data. Then, an online learning algorithm is introduced to tune the pretrained model according to the real-time data from the peeling process experiments to cover the uncertainties of the real process. Finally, an Online Learning Model Predictive Path Integral (OL-MPPI) algorithm is proposed for the optimal trajectory planning of the robot. The performance of our algorithm was validated through glass substrate peeling tasks of experiments.

Indexed as

deep learningglass substrate peelingmanipulation planningModel Predictive Path Integralonline learningsystem model

Identifiers

PMID35162036

DOI10.3390/s22031292

PMCPMC8839166

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.