ROBOTIC ARM COLLISION REACTION STRATEGIES FOR SAFE HUMAN–ROBOT INTERACTION WITHOUT TORQUE SENSORS

Author:

XU TIAN1,FAN JIZHUANG1,FANG QIANQIAN1,ZHAO JIE1,ZHU YANHE1

Affiliation:

1. State Key Laboratory of Robotics and System, School of Mechatronics Engineering, Harbin Institute of Technology Harbin, Heilongjiang 150080, P. R. China

Abstract

Three kinds of collision reaction strategies for increasing safety during human and robot interactions without relying on torque sensors are proposed in this paper. In the proposed algorithms, motor torque is estimated by driver current. The generalized momentum observer is used for collision detection, which does not need joints acceleration information and calculates the inverse of the inertia matrix. Three different collision reaction strategies, going away, dragging by hands and mechanical impedance developed in this paper, aim to enhance safety to humans during physical interaction with robots. For verifying the efficiency of the proposed algorithms, experiments are tested between a 1-DOF manipulator system and a human being. At last, the experiments’ results show that the proposed collision reaction algorithms are effective.

Funder

National Natural Science Foundation of China

National Science and Technology Major 04 Special Project

Publisher

World Scientific Pub Co Pte Lt

Subject

Biomedical Engineering

Cited by 4 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. Extensions to Dynamically-Consistent Collision Reaction Control for Collaborative Robots;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

2. Robot Contact Reflexes: Adaptive Maneuvers in the Contact Reflex Space;2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2022-10-23

3. Increasing the safety of a device using the TRIZ methodology;Scientific Journal of Silesian University of Technology. Series Transport;2021-06-30

4. Flexible Joints of Picking Manipulator Based on Current Feedback;IEEE Access;2020

全球学者库

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

"全球学者库"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,当前全球学者库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 © 2019-2023 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