In the robotic welding process with thick steel plates,laser vision sensors are widely used to profile the weld seam to implement automatic seam tracking.The weld seam profile extraction(WSPE)result is a crucial step ...In the robotic welding process with thick steel plates,laser vision sensors are widely used to profile the weld seam to implement automatic seam tracking.The weld seam profile extraction(WSPE)result is a crucial step for identifying the feature points of the extracted profile to guide the welding torch in real time.The visual information processing system may collapse when interference data points in the image survive during the phase of feature point identification,which results in low tracking accuracy and poor welding quality.This paper presents a visual attention featurebased method to extract the weld seam profile(WSP)from the strong arc background using clustering results.First,a binary image is obtained through the preprocessing stage.Second,all data points with a gray value 255 are clustered with the nearest neighborhood clustering algorithm.Third,a strategy is developed to discern one cluster belonging to the WSP from the appointed candidate clusters in each loop,and a scheme is proposed to extract the entire WSP using visual continuity.Compared with the previous methods the proposed method in this paper can extract more useful details of the WSP and has better stability in terms of removing the interference data.Considerable WSPE tests with butt joints and T-joints show the anti-interference ability of the proposed method,which contributes to smoothing the welding process and shows its practical value in robotic automated welding with thick steel plates.展开更多
In aerial robots＇ visual navigation, it is essential yet very difficult to detect the attitude and position of the robots operated in real time. By introducing a new parametric model, the problem can be reduced from ...In aerial robots＇ visual navigation, it is essential yet very difficult to detect the attitude and position of the robots operated in real time. By introducing a new parametric model, the problem can be reduced from almost unmanageable to be partly solved, though not fully, as per the requirement. In this parametric approach, a multi-scale least square method is formulated first. By propagating as well as improving the parameters down from layer to layer of the image pyramid, a new global feature line can then be detected to parameterize the attitude of the robots. Furthermore, this approach paves the way for segmenting the image into distinct parts, which can be realized by deploying a Bayesian classifier on the picture cell level. Comparison with the Hough transform based method in terms of robustness and precision shows that this multi-scale least square algorithm is considerably more robust to noises. Some discussions are also given.展开更多
Visual information acquisition is an important component of the AGV robot. The system adopts STM32F4 embedded application of the ARM Cortex-M4 kernel as the main control module,using shift algorithm to finish on a spe...Visual information acquisition is an important component of the AGV robot. The system adopts STM32F4 embedded application of the ARM Cortex-M4 kernel as the main control module,using shift algorithm to finish on a specific color piece of target tracking. For multi-sensor fusion of three methods,quaternion method is used to correct the attitude,the stability of AGV robot visual information acquisition and image clarity are improved.展开更多
Research on intelligent and robotic excavator has become a focus both at home and abroad, and this type of excavator becomes more and more important in application. In this paper, we developed a control system which c...Research on intelligent and robotic excavator has become a focus both at home and abroad, and this type of excavator becomes more and more important in application. In this paper, we developed a control system which can make the intelligent robotic excavator perform excavating operation autonomously. It can recognize the excavating targets by itself, program the operation automatically based on the original parameter, and finish all the tasks. Experimental results indicate the validity in real-time performance and precision of the control system. The intelligent robotic excavator can remarkably ease the labor intensity and enhance the working efficiency.展开更多
To measure the latency between human motion stimulation and stereo image display response in a visual feedback-based minimally invasive surgical(MIS) robotic system,a method was proposed by comparing the orientations ...To measure the latency between human motion stimulation and stereo image display response in a visual feedback-based minimally invasive surgical(MIS) robotic system,a method was proposed by comparing the orientations of input and output events through image-processing technology. This method used a black bar to keep pace with the measured joint rotating at a number of speeds. During tests,an external camera was placed in front of the apparatus with a proper visual field,so that it can simultaneously view orientations of both bars fixed on the corresponding joints. After quantitatively analyzing the accuracy of the proposed measurement method,the method was applied to a visual feedback-based master–slave robotic system with two-degrees-of-freedom. Experimental results show that the latency of the overall system was approximately 250 ms,and the opposite clearance of the measured joint was in the range of 1.7°–1.9°.展开更多
A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without req...A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP Mgorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.展开更多
近年来,深度学习技术在移动机器人同时定位与建图(Simultaneous localization and mapping,SLAM)领域取得了显著进展,为解决传统视觉SLAM在动态环境下面临的挑战提供了新的思路.本文首先总结了传统视觉SLAM在预处理、视觉里程计以及闭...近年来,深度学习技术在移动机器人同时定位与建图(Simultaneous localization and mapping,SLAM)领域取得了显著进展,为解决传统视觉SLAM在动态环境下面临的挑战提供了新的思路.本文首先总结了传统视觉SLAM在预处理、视觉里程计以及闭环检测模块的局限性.随后,聚焦于深度学习在视觉SLAM中的应用,重点介绍了基于深度学习的预处理、视觉里程计和闭环检测模块,以及其如何提升视觉SLAM的鲁棒性和精度.最后,探讨了基于深度学习SLAM面临的挑战并展望了未来研究方向,包括轻量化网络设计、场景的长期建模以及自监督学习等,以推动深度学习SLAM在实际应用中的落地.展开更多
目的:分析国内外机器人辅助手术护理的研究现状、热点及趋势。方法:以中国知网、万方数据、Web of Science核心数据库为数据源,使用CiteSpace 6.4.R1对机器人辅助手术护理相关研究的发表时间、分布、关键词等进行可视化分析。结果:共纳...目的:分析国内外机器人辅助手术护理的研究现状、热点及趋势。方法:以中国知网、万方数据、Web of Science核心数据库为数据源,使用CiteSpace 6.4.R1对机器人辅助手术护理相关研究的发表时间、分布、关键词等进行可视化分析。结果:共纳入中文文献949篇、英文文献3442篇。机器人辅助手术护理研究发文量呈逐年上升趋势,美国为发文量最高的国家,意大利、中国紧随其后,但国内合作不密切。目前,机器人辅助护理研究热点为高风险手术护理流程标准化与安全管理、创新多学科协作下的快速康复护理模式、构建人工智能(AI)围手术期决策支持系统。结论:未来研究应融合深度学习与增强现实技术提升机器人辅助手术围手术期护理的精准性,构建各专科手术护理配合的标准化培训体系,优化术前评估、术中护理协作及术后远程监测的全周期智慧护理模式,促进跨学科技术适配与患者预后改善,提升机器人辅助手术护理质量,推动我国机器人辅助手术护理研究的发展。展开更多
基金This work was supported in part by the Foundation of Guangdong Educational Committee （2014KTSCX191） and the National Natural Science Foundation of China （61201087）.
基金Supported by National Natural Science Foundation of China(Grant Nos.51575349,51665037,51575348)State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System(Grant No.GZ2016KF002).
文摘In the robotic welding process with thick steel plates,laser vision sensors are widely used to profile the weld seam to implement automatic seam tracking.The weld seam profile extraction(WSPE)result is a crucial step for identifying the feature points of the extracted profile to guide the welding torch in real time.The visual information processing system may collapse when interference data points in the image survive during the phase of feature point identification,which results in low tracking accuracy and poor welding quality.This paper presents a visual attention featurebased method to extract the weld seam profile(WSP)from the strong arc background using clustering results.First,a binary image is obtained through the preprocessing stage.Second,all data points with a gray value 255 are clustered with the nearest neighborhood clustering algorithm.Third,a strategy is developed to discern one cluster belonging to the WSP from the appointed candidate clusters in each loop,and a scheme is proposed to extract the entire WSP using visual continuity.Compared with the previous methods the proposed method in this paper can extract more useful details of the WSP and has better stability in terms of removing the interference data.Considerable WSPE tests with butt joints and T-joints show the anti-interference ability of the proposed method,which contributes to smoothing the welding process and shows its practical value in robotic automated welding with thick steel plates.
基金Supported by National Natural Science Foundation of China（60874002） Key Project of Shanghai Education Committee （09ZZ158） Leading Academic Discipline Project of Shanghai Municipal Government （S30501）
文摘In aerial robots＇ visual navigation, it is essential yet very difficult to detect the attitude and position of the robots operated in real time. By introducing a new parametric model, the problem can be reduced from almost unmanageable to be partly solved, though not fully, as per the requirement. In this parametric approach, a multi-scale least square method is formulated first. By propagating as well as improving the parameters down from layer to layer of the image pyramid, a new global feature line can then be detected to parameterize the attitude of the robots. Furthermore, this approach paves the way for segmenting the image into distinct parts, which can be realized by deploying a Bayesian classifier on the picture cell level. Comparison with the Hough transform based method in terms of robustness and precision shows that this multi-scale least square algorithm is considerably more robust to noises. Some discussions are also given.
基金supported by the National Key Technology R&D Program(2015BAK06B04)the key technology R&D Program of Tianjin(14ZCZDSF00022,15ZXZNGX00260)
文摘Visual information acquisition is an important component of the AGV robot. The system adopts STM32F4 embedded application of the ARM Cortex-M4 kernel as the main control module,using shift algorithm to finish on a specific color piece of target tracking. For multi-sensor fusion of three methods,quaternion method is used to correct the attitude,the stability of AGV robot visual information acquisition and image clarity are improved.
文摘Research on intelligent and robotic excavator has become a focus both at home and abroad, and this type of excavator becomes more and more important in application. In this paper, we developed a control system which can make the intelligent robotic excavator perform excavating operation autonomously. It can recognize the excavating targets by itself, program the operation automatically based on the original parameter, and finish all the tasks. Experimental results indicate the validity in real-time performance and precision of the control system. The intelligent robotic excavator can remarkably ease the labor intensity and enhance the working efficiency.
基金supported by the International S&T Cooperation Program of China (No. 2014DFA70710) the National Natural Science Foundation of China (No. 51475323)
文摘To measure the latency between human motion stimulation and stereo image display response in a visual feedback-based minimally invasive surgical(MIS) robotic system,a method was proposed by comparing the orientations of input and output events through image-processing technology. This method used a black bar to keep pace with the measured joint rotating at a number of speeds. During tests,an external camera was placed in front of the apparatus with a proper visual field,so that it can simultaneously view orientations of both bars fixed on the corresponding joints. After quantitatively analyzing the accuracy of the proposed measurement method,the method was applied to a visual feedback-based master–slave robotic system with two-degrees-of-freedom. Experimental results show that the latency of the overall system was approximately 250 ms,and the opposite clearance of the measured joint was in the range of 1.7°–1.9°.
文摘A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP Mgorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.
文摘近年来,深度学习技术在移动机器人同时定位与建图(Simultaneous localization and mapping,SLAM)领域取得了显著进展,为解决传统视觉SLAM在动态环境下面临的挑战提供了新的思路.本文首先总结了传统视觉SLAM在预处理、视觉里程计以及闭环检测模块的局限性.随后,聚焦于深度学习在视觉SLAM中的应用,重点介绍了基于深度学习的预处理、视觉里程计和闭环检测模块,以及其如何提升视觉SLAM的鲁棒性和精度.最后,探讨了基于深度学习SLAM面临的挑战并展望了未来研究方向,包括轻量化网络设计、场景的长期建模以及自监督学习等,以推动深度学习SLAM在实际应用中的落地.
文摘目的:分析国内外机器人辅助手术护理的研究现状、热点及趋势。方法:以中国知网、万方数据、Web of Science核心数据库为数据源,使用CiteSpace 6.4.R1对机器人辅助手术护理相关研究的发表时间、分布、关键词等进行可视化分析。结果:共纳入中文文献949篇、英文文献3442篇。机器人辅助手术护理研究发文量呈逐年上升趋势,美国为发文量最高的国家,意大利、中国紧随其后,但国内合作不密切。目前,机器人辅助护理研究热点为高风险手术护理流程标准化与安全管理、创新多学科协作下的快速康复护理模式、构建人工智能(AI)围手术期决策支持系统。结论:未来研究应融合深度学习与增强现实技术提升机器人辅助手术围手术期护理的精准性,构建各专科手术护理配合的标准化培训体系,优化术前评估、术中护理协作及术后远程监测的全周期智慧护理模式,促进跨学科技术适配与患者预后改善,提升机器人辅助手术护理质量,推动我国机器人辅助手术护理研究的发展。
