A learning trajectory planning for vibration suppression of industrial robot

IF 1.9 4区 计算机科学 Q3 ENGINEERING, INDUSTRIAL Industrial Robot-The International Journal of Robotics Research and Application Pub Date : 2023-05-10 DOI:10.1108/ir-02-2023-0013
Yanbiao Zou, Tao Liu, Tie Zhang, Hubo Chu
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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.
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工业机器人振动抑制的学习轨迹规划
目的提出一种用于抑制工业机器人残余振动的学习指数型跳振轨迹规划方法。基于有限脉冲响应滤波技术,一个适当幅度的阶跃信号首先通过两个线性滤波器,然后进行指数滤波器整形,得到指数跳振轨迹并抵消振荡模态。采用梯度下降原理设计的迭代学习策略在线调整指数滤波器的参数,达到最大的抑振效果。通过搭建SCARA机器人实验平台,进行了一系列对比实验。结果表明,与零振动整形器和零振动及导数整形器相比,该方法能有效抑制残余振动。采用的迭代学习策略思想简单,降低了控制器的计算能力。一种便宜的加速度传感器是可用的,因为它只需要测量振动能量来反馈。因此,该方法可应用于生产实践。
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来源期刊
CiteScore
4.50
自引率
16.70%
发文量
86
审稿时长
5.7 months
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.
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