[1]解明扬,吴伟,徐成永,等.基于视觉伺服的协作机器人自适应阻抗控制技术[J].智能系统学报,2025,20(6):1474-1482.[doi:10.11992/tis.202504008]
XIE Mingyang,WU Wei,XU Chengyong,et al.Visual servoing-based adaptive impedance control technology for collaborative robots[J].CAAI Transactions on Intelligent Systems,2025,20(6):1474-1482.[doi:10.11992/tis.202504008]
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XIE Mingyang,WU Wei,XU Chengyong,et al.Visual servoing-based adaptive impedance control technology for collaborative robots[J].CAAI Transactions on Intelligent Systems,2025,20(6):1474-1482.[doi:10.11992/tis.202504008]
基于视觉伺服的协作机器人自适应阻抗控制技术
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
20
期数:
2025年第6期
页码:
1474-1482
栏目:
学术论文—机器人
出版日期:
2025-11-05
- Title:
- Visual servoing-based adaptive impedance control technology for collaborative robots
- Keywords:
- collaborative robot; robot grasping; target detection; compliant manipulation; deep learning; impedance control; optimization of capture posture; visual servo
- 分类号:
- TP241
- 摘要:
- 针对人机共融非结构化环境下协作机器人智能抓取存在目标识别准确率低、抓取框姿态非最优，难以满足动态未知环境下柔顺操作需求的挑战，本文提出基于视觉伺服的协作机器人自适应阻抗控制技术，实现不同刚度与形状物体的柔顺抓取。设计基于残差网络(residual network, ResNet)的改进目标检测算法，通过输入抓取目标的红-绿-蓝-深度 (red-green-blue-depth, RGBD)图生成最优抓取预测框；提出机器人末端夹爪自适应阻抗控制策略，结合模糊控制自适应调节阻抗参数；构建基于视觉伺服的模糊自适应阻抗控制系统，开展不同刚性物体识别与柔顺抓取实验。结果表明，本文所提方法提升了抓取目标识别的泛化性与成功率，提高了操作的柔顺性，相较于现有自适应阻抗控制策略，柔顺指标分别提升了66.3%与45.9%。
- Abstract:
- When operated in human–robot coexistent unstructured environments, collaborative robots face various challenges, such as low target recognition accuracy, suboptimal grasping poses, and difficulties in achieving compliant manipulation under dynamic unknown scenarios. To address these issues, this study proposed a vision servoing–based adaptive impedance control technique. The proposed method enabled compliant grasping of objects with varying stiffness characteristics and geometric configurations by the robot. First, an improved target detection algorithm based on the ResNet (residual network) was developed to generate an optimal capture prediction frame by inputting the RGBD (red-green-blue-depth) image of the captured target. Second, an adaptive impedance control strategy was proposed for the robot end effector, and the impedance parameters were adaptively adjusted using fuzzy control. Finally, a fuzzy adaptive impedance control system based on visual servoing was constructed, and experiments on object recognition and compliant grasping were conducted. Results showed that the proposed method enhanced the generalization capability and success rate of target recognition in grasping tasks and considerably improved manipulation compliance. Compared with existing adaptive impedance control strategies, the proposed method achieved 66.3% and 45.9% improvements in compliant metrics.
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- 相似文献/References:
-
[1]田勇,王洪光,潘新安,等.协作机器人的构型分析研究[J].智能系统学报,2019,14(2):217.[doi:10.11992/tis.201806044]
TIAN Yong,WANG Hongguang,PAN Xinan,et al.Research on configuration analysis of collaborative robots[J].CAAI Transactions on Intelligent Systems,2019,14():217.[doi:10.11992/tis.201806044]
备注/Memo
收稿日期:2025-4-15。
基金项目:国家自然科学基金项目（62373186）；江苏省自然科学基金项目(BK20231440）.
作者简介:解明扬，副研究员，博士研究生，主要研究方向为智能机器人、多智能体深度强化学习。IEEE高级会员，《智能系统学报》《机器人》青年编委，获得国防科技进步二等奖和军事科学技术进步奖二等奖各1项。E-mail：myxie@nuaa.edu.cn。;吴伟，硕士研究生，主要研究方向为协作机器人智能控制。E-mail：wuwei0611@nuaa.edu.cn。;徐成永，硕士研究生，主要研究方向为人机交互、协作机器人智能控制。E-mail：chengyong.xu@nuaa.edu.cn。
通讯作者:解明扬. E-mail：myxie@nuaa.edu.cn
更新日期/Last Update:
1900-01-01