The paper presents a method of robot skateboard edging grinding based on pneumatic constant force device. To solve the problem of modeling and complex algorithm of robot end active compliance control during the grinding process, a hybrid position/force control is proposed by controlling position and force separately. That is, a kind of 2 degrees of freedom pneumatic constant force grinding device is designed to realize the constant force control in the horizontal and vertical directions, while a robot is used only for position control. Then a fuzzy PID control algorithm is proposed for smooth grinding based on the dynamic modeling of the grinding device. Experiments show that the use of fuzzy PID can effectively improve the dynamic adjustment performance of the pneumatic constant force grinding system and reduce the steady-state error. Compared with normal PID, fuzzy PID reduces the step response adjustment time of the grinding system from 400ms to within 200ms. In addition, when the constant force control is disturbed, the response adjustment time is less than 100ms and there is no oscillation, and the standard deviation of the grinding force is within 1N, which has good robustness and fulfills the requirements of edging control.
The rapid entire body assessment (REBA) is a rapid and semi-quantitative ergonomic assessment method for those who engaged in manual handling and/or standing work for a long time. In order to provide a self-assessment tool for operators, an App software based on Mask RCNN is proposed in this paper. The software is developed by adopting the architecture of a mobile terminal App combined with an imaging processing server. The main functions of the App are video capturing of work process, the operation of the process of REBA, human-machine interaction, etc., while the server is work for processing the video imaging transmitted from the App, key-points extraction of worker’s body from the images for work posture identification. One thousand working scene photos marked by VIA are used for training and testing based on the Microsoft COCO dataset to obtain a reliable target detection model. Experiment of container handling scene shows that the App evaluation software has achieved higher evaluation efficiency and accuracy. The validation of this method has been proved compared with manual evaluation based on REBA. And other work posture evaluation methods will be developed in the future to form an ergonomic evaluation software system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.