2P1-J03 Real time identification with contact force for industrial robots(New Control Theory and Motion Control)

2013 ◽  
Vol 2013 (0) ◽  
pp. _2P1-J03_1-_2P1-J03_4
Author(s):  
Kengo AOKI ◽  
Ryo KIKUCHI ◽  
Yasutaka TAGAWA ◽  
Gentiane VENTURE
Keyword(s):  
Control Theory ◽  
Real Time ◽  
Motion Control ◽  
Contact Force ◽  
Industrial Robots ◽  
Real Time Identification

scholarly journals Robotic Surface Finishing of Curved Surfaces: Real-Time Identification of Surface Profile and Control

2018 ◽  
Author(s):  
Yalun Wen ◽  
Prabhakar R. Pagilla
Keyword(s):  
Real Time ◽  
Force Control ◽  
Surface Profile ◽  
Industrial Robots ◽  
Surface Finishing ◽  
Laser Sensor ◽  
Curved Surfaces ◽  
End Effector ◽  
Real Time Identification ◽  
Finishing Tool

An efficient strategy for robotic surface finishing of curved surfaces that includes real-time identification of the surface profile, control, and implementation is presented in this paper. Real-time identification of the surface profile in the robot base frame is accomplished by employing a proximity laser sensor mounted on the robot end-effector. This surface profile description allows us to generate trajectories for both motion and force control as it provides the surface normal at each point of the surface. Using the surface profile, a trajectory is generated that would orient the surface finishing tool to the local normal of the surface. An algorithm for simultaneous position and force control is developed for surface finishing of curved surfaces. The integrated robotic surface finishing system consists of a UR5 robot and a custom end-effector that includes a force/torque sensor, an electromechanical sander, and the proximity laser sensor. Robot Operating System (ROS) is utilized for real-time implementation, which would enable easy migration of the developed tools if other industrial robots are used. The effectiveness of the strategy is evaluated by conducting a number of experiments for flat and curved surfaces. A representative sample of results on force regulation and surface finishing are presented and discussed.

scholarly journals A Kinematic Error Controller for Real-Time Kinematic Error Correction of Industrial Robots

2021 ◽  
Vol 53 ◽  
pp. 705-715
Author(s):  
Mitchell R. Woodside ◽  
Joseph Fischer ◽  
Patrick Bazzoli ◽  
Douglas A. Bristow ◽  
Robert G. Landers
Keyword(s):  
Error Correction ◽  
Real Time ◽  
Industrial Robots ◽  
Kinematic Error ◽  
Real Time Kinematic

scholarly journals Real-Time Identification of Patients Included in the CMS Bundled Payment Care Improvement (BPCI) Program

2020 ◽  
Vol 41 (S1) ◽  
pp. s367-s368
Author(s):  
Michael Korvink ◽  
John Martin ◽  
Michael Long
Keyword(s):  
Performance Evaluation ◽  
Real Time ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Care Providers ◽  
Meta Model ◽  
Bundled Payment ◽  
Clinical Cohort ◽  
Real Time Identification ◽  
Care Improvement

Background: The Bundled Payment Care Improvement Program is a CMS initiative designed to encourage greater collaboration across settings of care, especially as it relates to an initial set of targeted clinical episodes, which include sepsis and pneumonia. As with many CMS incentive programs, performance evaluation is retrospective in nature, resulting in after-the-fact changes in operational processes to improve both efficiency and quality. Although retrospective performance evaluation is informative, care providers would ideally identify a patient’s potential clinical cohort during the index stay and implement care management procedures as necessary to prevent or reduce the severity of the condition. The primary challenges for real-time identification of a patient’s clinical cohort are CMS-targeted cohorts are based on either MS-DRG (grouping of ICD-10 codes) or HCPCS coding—coding that occurs after discharge by clinical abstractors. Additionally, many informative data elements in the EHR lack standardization and no simple and reliable heuristic rules can be employed to meaningfully identify those cohorts without human review. Objective: To share the results of an ensemble statistical model to predict patient risks of sepsis and pneumonia during their hospital (ie, index) stay. Methods: The predictive model uses a combination of Bernoulli Naïve Bayes natural language processing (NLP) classifiers, to reduce text dimensionality into a single probability value, and an eXtreme Gradient Boosting (XGBoost) algorithm as a meta-model to collectively evaluate both standardized clinical elements alongside the NLP-based text probabilities. Results: Bernoulli Naïve Bayes classifiers have proven to perform well on short text strings and allow for highly explanatory unstructured or semistructured text fields (eg, reason for visit, culture results), to be used in a both comparative and generalizable way within the larger XGBoost model. Conclusions: The choice of XGBoost as the meta-model has the benefits of mitigating concerns of nonlinearity among clinical features, reducing potential of overfitting, while allowing missing values to exist within the data. Both the Bayesian classifier and meta-model were trained using a patient-level integrated dataset extracted from both a patient-billing and EHR data warehouse maintained by Premier. The data set, joined by patient admission-date, medical record number, date of birth, and hospital entity code, allows the presence of both the coded clinical cohort (derived from the MS-DRG) and the explanatory features in the EHR to exist within a single patient encounter record. The resulting model produced F1 performance scores of .65 for the sepsis population and .61 for the pneumonia population.Funding: NoneDisclosures: None

scholarly journals A force/motion control approach based on trajectory planning for industrial robots with closed control architecture

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Alejandro GutierreznGiles ◽  
Luis U. EvangelistanHernandez ◽  
Marco A. Arteaga ◽  
Carlos A. CruznVillar ◽  
Alejandro RodrigueznAngeles
Keyword(s):  
Motion Control ◽  
Trajectory Planning ◽  
Industrial Robots ◽  
Control Architecture ◽  
Control Approach
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