{"title":"A learning trajectory planning for vibration suppression of industrial robot","authors":"Yanbiao Zou, Tao Liu, Tie Zhang, Hubo Chu","doi":"10.1108/ir-02-2023-0013","DOIUrl":null,"url":null,"abstract":"\nPurpose\nThis paper aims to propose a learning exponential jerk trajectory planning to suppress the residual vibrations of industrial robots.\n\n\nDesign/methodology/approach\nBased on finite impulse response filter technology, a step signal with a proper amplitude first passes through two linear filters and then performs exponential filter shaping to obtain an exponential jerk trajectory and cancel oscillation modal. An iterative learning strategy designed by gradient descent principle is used to adjust the parameters of exponential filter online and achieve the maximum vibration suppression effect.\n\n\nFindings\nBy building a SCARA robot experiment platform, a series of contrast experiments are conducted. The results show that the proposed method can effectively suppress residual vibration compared to zero vibration shaper and zero vibration and derivative shaper.\n\n\nOriginality/value\nThe idea of the adopted iterative leaning strategy is simple and reduces the computing power of the controller. A cheap acceleration sensor is available because it just needs to measure vibration energy to feedback. Therefore, the proposed method can be applied to production practice.\n","PeriodicalId":54987,"journal":{"name":"Industrial Robot-The International Journal of Robotics Research and Application","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Industrial Robot-The International Journal of Robotics Research and Application","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1108/ir-02-2023-0013","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose
This paper aims to propose a learning exponential jerk trajectory planning to suppress the residual vibrations of industrial robots.
Design/methodology/approach
Based on finite impulse response filter technology, a step signal with a proper amplitude first passes through two linear filters and then performs exponential filter shaping to obtain an exponential jerk trajectory and cancel oscillation modal. An iterative learning strategy designed by gradient descent principle is used to adjust the parameters of exponential filter online and achieve the maximum vibration suppression effect.
Findings
By building a SCARA robot experiment platform, a series of contrast experiments are conducted. The results show that the proposed method can effectively suppress residual vibration compared to zero vibration shaper and zero vibration and derivative shaper.
Originality/value
The idea of the adopted iterative leaning strategy is simple and reduces the computing power of the controller. A cheap acceleration sensor is available because it just needs to measure vibration energy to feedback. Therefore, the proposed method can be applied to production practice.
期刊介绍:
Industrial Robot publishes peer reviewed research articles, technology reviews and specially commissioned case studies. Each issue includes high quality content covering all aspects of robotic technology, and reflecting the most interesting and strategically important research and development activities from around the world.
The journal’s policy of not publishing work that has only been tested in simulation means that only the very best and most practical research articles are included. This ensures that the material that is published has real relevance and value for commercial manufacturing and research organizations. Industrial Robot''s coverage includes, but is not restricted to:
Automatic assembly
Flexible manufacturing
Programming optimisation
Simulation and offline programming
Service robots
Autonomous robots
Swarm intelligence
Humanoid robots
Prosthetics and exoskeletons
Machine intelligence
Military robots
Underwater and aerial robots
Cooperative robots
Flexible grippers and tactile sensing
Robot vision
Teleoperation
Mobile robots
Search and rescue robots
Robot welding
Collision avoidance
Robotic machining
Surgical robots
Call for Papers 2020
AI for Autonomous Unmanned Systems
Agricultural Robot
Brain-Computer Interfaces for Human-Robot Interaction
Cooperative Robots
Robots for Environmental Monitoring
Rehabilitation Robots
Wearable Robotics/Exoskeletons.