A universal algorithm for sensorless collision detection of robot actuator faults

Author:

Chen Saixuan1,Luo Minzhou123,He Feng2

Affiliation:

1. School of Engineering Science, University of Science and Technology of China, Hefei, China

2. Institute of Intelligent Manufacturing Technology, Jiangsu Industrial Technology Research Institute, Nanjing, China

3. Key Laboratory of Special Robot Technology of Jiangsu Province, Hohai University, Changzhou, China

Abstract

When a robot is working properly, it is possible to collide with people or objects entering its working space. This research is different than usual control algorithm. It proposes a universal algorithm for sensorless collision detection of robot actuator faults to enhance the security of the robot. On the basis of the dynamic model, a classical friction model to ensure the accuracy of the whole dynamic model is introduced. This collision detection algorithm can conduct without any external sensors or acceleration and realize the real-time detection just needs to measure the motor current and the location information from the encoder of the robot joint. The value of external torque τext was used to compare with the threshold to detect the collision. After using the proposed collision detection method, the two rotational (2R) planar manipulators can detect the slight collision reliably. The experimental results and performance comparisons show that this sensorless collision detection algorithm is simple and effective. It can be promoted to any other type of robot arm with more degrees of freedom.

Publisher

SAGE Publications

Subject

Mechanical Engineering

Cited by 44 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Survey of the Present Landscape and Prospective Trajectories in Rescue Robots;2024 International Symposium on Intelligent Robotics and Systems (ISoIRS);2024-06-14

2. An exploration system to effectively analyze the visual metaphor used in sentiment visualization;Information Visualization;2024-02-15

3. Isomorphic Graph Embedding for Progressive Maximal Frequent Subgraph Mining;ACM Transactions on Intelligent Systems and Technology;2023-12-19

4. LSTM-based external torque prediction for 6-DOF robot collision detection;Journal of Mechanical Science and Technology;2023-09

5. SocioPedia+: a visual analytics system for social knowledge graph-based event exploration;PeerJ Computer Science;2023-03-20

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3