Quantum Cooperative Robotics and Autonomy

The intersection of Quantum Technologies and Robotics Autonomy is explored in the present paper.The two areas are brought together in establishing an interdisciplinary interface that contributes to advancing the field of system autonomy,and pushes the engineering boundaries beyond the existing techniques.The present research adopts the experimental aspects of quantum entanglement and quantum cryptography,and integrates these established quantum capabilities into distributed robotic platforms,to explore the possibility of achieving increased autonomy for the control of multi-agent robotic systems engaged in cooperative tasks.Experimental quantum capabilities are realized by producing single photons(using spontaneous parametric down-conversion process),polarization of photons,detecting vertical and horizontal polarizations,and single photon detecting/counting.Specifically,such quantum aspects are implemented on network of classical agents,i.e.,classical aerial and ground robots/unmanned systems.With respect to classical systems for robotic applications,leveraging quantum technology is expected to lead to guaranteed security,very fast control and communication,and unparalleled quantum capabilities such as entanglement and quantum superposition that will enable novel applications.

Soft Robotics:Morphology and Morphology-inspired Motion Strategy

Robotics has aroused huge attention since the 1950s.Irrespective of the uniqueness that industrial applications exhibit,conventional rigid robots have displayed noticeable limitations,particularly in safe cooperation as well as with environmental adaption.Accordingly,scientists have shifted their focus on soft robotics to apply this type of robots more effectively in unstructured environments.For decades,they have been committed to exploring sub-fields of soft robotics(e.g.,cutting-edge techniques in design and fabrication,accurate modeling,as well as advanced control algorithms).Although scientists have made many different efforts,they share the common goal of enhancing applicability.The presented paper aims to brief the progress of soft robotic research for readers interested in this field,and clarify how an appropriate control algorithm can be produced for soft robots with specific morphologies.This paper,instead of enumerating existing modeling or control methods of a certain soft robot prototype,interprets for the relationship between morphology and morphology-dependent motion strategy,attempts to delve into the common issues in a particular class of soft robots,and elucidates a generic solution to enhance their performance.

Robotics： From Automation to Intelligent Systems

本文简述了在过去的几年中我们在自动机器人研制领域所取得的进展,并介绍了在工业环境中,为成功实现机器人自动化部署所解决的基本问题。本文还将为我们描述在更复杂的场景中,自动化技术落地应用所面临的一些挑战,如城市车辆自动化等。机器人操作器最初在20世纪50年代末期取得应用,它率先被人们引入汽车制造领域。在随后的发展中,液压系统逐渐被电动机取代,这使得机器人更加灵活可控。起初。

Discrete-event modeling for internet multi-robotics

Internet multi-robotics is a typical discrete-event system. In order to describe joint activities between multiple operators and multiple robots, a 4-level discrete-event model is proposed in this paper based on the controlled condition/event Petri nets (CCEP). On the first or mission level, the task splitting of the system is defined; on the second or multi-operator level, a precedence graph is introduced for every operator to plan his or her robotic actions; on the third or coordination level, the above precedence graphs are translated and integrated into the corresponding CCEPs in terms of specific rules; and on the last or multi-robot level, operators can select their control range by setting the corresponding control marks of the obtained CCEPs. As a consequence, a clear mechanism of operator-robot collaboration is obtained to conduct the development of the system.

Experimental study of Internet telerobotics based on virtual guides

For the virtual environment (VE) to control remote robots through Internet, this paper proposes an operational idea based on virtual guides (VGs). The VGs consist of three types, line VG, plane VG, and body VG, which can greatly enhance the safety and flexibility of the man-machine system. Through the analysis of several common cases, the idea of the VGs are first introduced and described. Second, detailed descriptions of the three VGs are given, and then a uniform representation of all VGs is given. Finally, a typical experiment is designed and implemented according to the modes of video feedback, video&VE feedback, and video&VE&VG feedback. Experimental results show that the operation based on VEs and VGs have significant advantages over that based on video feedback, and VGs can greatly improve the ability of the VE to control remote robots, thus obtaining high and efficient man-machine cooperation.

Robotics-The answer to the Achilles' heel of VATS pulmonary resection

During the past decade, robotic surgical systems have been increasingly utilized to perform highly complex surgical procedures. In fact, the robotic-assisted approach to the treatment of many gynecological and urological surgical procedures has become the standard-of-care. The use of robotic surgical system in thoracic surgery is in its early development. Video-assisted thoracoscopic surgery （VATS） was propelled forward in the early 1990＇s without full appreciation for long-term consequences, increased cost or therapeutic benefit. The trend in robotic-assisted thoracic surgery is projected to surpass the adoption of VATS and to mirror that of other robotic surgical subspecialties.

Rethink Robotics在全球推出Sawyer机器人

Rethink Robotics新一代高性能协作机器人正式上市美国波士顿2015年9月24日电/--Rethink Robotics公司今日宣布其第二代智能协作机器人Sawyer现已开始接受订购,Sawyer广泛适用于各类工厂生产环境,目前已经被部署在世界各地多家制造商的生产厂中。Sawyer在今年三月面世,它是一款单臂、具备高性能的机器人,可完成机器操控、电路板测试以及其它难以通过现有机器人进行自动化操作的高精度任务。

人机协作赢取“智”高点——Rethink Robotics提升威胜集团核心竞争力

Rethink Robotics的Sawyer智能协作机器人凭借灵活、易部署、擅长高精度任务等优势，助力威胜集团的单相表自动装配线提升核心竞争力，实现智能制造。

Rethink Robotics:我们需要更理性看待人工智能技术

“我们现在被人工智能和机器人技术的话题包围,热议机器人发展将有多快、多强大。但是错误的推断、有限的想象力和其它常见的谬误,使我们无法真正地、更有效地思考未来。”“错误的推测会导致人们对那些不会发生的事情产生恐惧,比如机器人会造成大规模的失业,甚至是机器人会反抗人类等等。我们需要更理性看待这些技术,避免错误的预测。我们需要反思一下为什么会出现这些错误的推测”。

Applying Double Skin Facade with ETFE Membrane Assembly for Energy Saving and Acoustic Protection for the Building of the Czech Institute of Informatics,Robotics and Cybernetics in Prague

Multidisciplinary, integrated planning approach by architects, engineers, scientists and manufacturers to reduce energy consumption of buildings. The CIIRC Complex, located on the main campus of Czech Technical University in Prague consists of two buildings, newly constructed building and adaptive reuse of existing building. CIIRC—Czech Institute of Informatics, Robotics and Cybernetics is a contemporary teaching facility of new generation and use for scientific research teams. New building has ten above-ground floors, on the bottom 4 floors of laboratories, scientist modules, classrooms, above are offices, meeting rooms, teaching and research modules for professors and students. Offices of the rector are on the last two floors of the building. On the top floor is congress type auditorium, in the basement is fully automatic car park. Double skin pneumatic cushions facade. In the project are introduced series of architectural and technical features and innovations. Probably the most visible is the double skin facade facing south-transparent double layer membrane ETFE (Ethylen-TetraFluorEthylen) cushions with triple glazed modular system assembly. Acting as solar collector, recuperating of hot air on the top floors, saving up to 30% of an energy consumption.

Single-center experience with Knee+^(TM) augmented reality navigation system in primary total knee arthroplasty

BACKGROUND Computer-assisted systems obtained an increased interest in orthopaedic surgery over the last years,as they enhance precision compared to conventional hardware.The expansion of computer assistance is evolving with the employment of augmented reality.Yet,the accuracy of augmented reality navigation systems has not been determined.AIM To examine the accuracy of component alignment and restoration of the affected limb’s mechanical axis in primary total knee arthroplasty(TKA),utilizing an augmented reality navigation system and to assess whether such systems are conspicuously fruitful for an accomplished knee surgeon.METHODS From May 2021 to December 2021,30 patients,25 women and five men,under-went a primary unilateral TKA.Revision cases were excluded.A preoperative radiographic procedure was performed to evaluate the limb’s axial alignment.All patients were operated on by the same team,without a tourniquet,utilizing three distinct prostheses with the assistance of the Knee+™augmented reality navigation system in every operation.Postoperatively,the same radiographic exam protocol was executed to evaluate the implants’position,orientation and coronal plane alignment.We recorded measurements in 3 stages regarding femoral varus and flexion,tibial varus and posterior slope.Firstly,the expected values from the Augmented Reality system were documented.Then we calculated the same values after each cut and finally,the same measurements were recorded radiolo-gically after the operations.Concerning statistical analysis,Lin’s concordance correlation coefficient was estimated,while Wilcoxon Signed Rank Test was performed when needed.RESULTS A statistically significant difference was observed regarding mean expected values and radiographic mea-surements for femoral flexion measurements only(Z score=2.67,P value=0.01).Nonetheless,this difference was statistically significantly lower than 1 degree(Z score=-4.21,P value<0.01).In terms of discrepancies in the calculations of expected values and controlled measurements,a statistically significant difference between tibial varus values was detected(Z score=-2.33,P value=0.02),which was also statistically significantly lower than 1 degree(Z score=-4.99,P value<0.01).CONCLUSION The results indicate satisfactory postoperative coronal alignment without outliers across all three different implants utilized.Augmented reality navigation systems can bolster orthopaedic surgeons’accuracy in achieving precise axial alignment.However,further research is required to further evaluate their efficacy and potential.

Recent Developments in Metallic Degradable Micromotors for Biomedical and Environmental Remediation Applications

Synthetic micromotor has gained substantial attention in biomedicine and environmental remediation.Metal-based degradable micromotor composed of magnesium(Mg),zinc(Zn),and iron(Fe)have promise due to their nontoxic fuel-free propulsion,favorable biocompatibility,and safe excretion of degradation products Recent advances in degradable metallic micromotor have shown their fast movement in complex biological media,efficient cargo delivery and favorable biocompatibility.A noteworthy number of degradable metal-based micromotors employ bubble propulsion,utilizing water as fuel to generate hydrogen bubbles.This novel feature has projected degradable metallic micromotors for active in vivo drug delivery applications.In addition,understanding the degradation mechanism of these micromotors is also a key parameter for their design and performance.Its propulsion efficiency and life span govern the overall performance of a degradable metallic micromotor.Here we review the design and recent advancements of metallic degradable micromotors.Furthermore,we describe the controlled degradation,efficient in vivo drug delivery,and built-in acid neutralization capabilities of degradable micromotors with versatile biomedical applications.Moreover,we discuss micromotors’efficacy in detecting and destroying environmental pollutants.Finally,we address the limitations and future research directions of degradable metallic micromotors.

New Second-Level-Discrete Zeroing Neural Network for Solving Dynamic Linear System

Dear Editor,This letter deals with a new second-level-discretization method with higher precision than the traditional first-level-discretization method.Specifically,the traditional discretization method utilizes the first-order time derivative information,and it is termed first-level-discretization method.By contrast,the new discretization method not only utilizes the first-order time derivative information,but also makes use of the second-order derivative information.By combining the new second-level-discretization method with zeroing neural network(ZNN),the second-level-discrete ZNN(SLDZNN)model is proposed to solve dynamic(i.e.,time-variant or time-dependent)linear system.Numerical experiments and application to angle-of-arrival(AoA)localization show the effectiveness and superiority of the SLDZNN model.

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

基于数据驱动的冗余机器人末端执行器位姿控制方案

模型未知的冗余机器人执行任务的过程中会产生较大的控制误差,其末端执行器的位置与姿态也需要针对不同任务进行修正.为解决该问题,提出一种基于数据驱动的冗余机器人末端执行器位置与姿态控制方案.该方案使用在线学习技术,能够应用于模型未知的冗余机器人控制.同时引入四元数表示法将控制机器人末端执行器姿态问题转化为基于四元数表示的控制方法.随后,设计一种神经动力学求解器对所提方案进行求解.相关的理论分析、仿真及对比体现了所提方案的可行性、有效性与新颖性.

Characteristics of laser-induced breakdown spectroscopy of liquid slag

Rapid online analysis of liquid slag is essential for optimizing the quality and energy efficiency of steel production. To investigate the key factors that affect the online measurement of refined slag using laser-induced breakdown spectroscopy(LIBS), this study examined the effects of slag composition and temperature on the intensity and stability of the LIBS spectra. The experimental temperature was controlled at three levels: 1350℃, 1400℃, and 1450℃. The results showed that slag composition and temperature significantly affected the intensity and stability of the LIBS spectra. Increasing the Fe content and temperature in the slag reduces its viscosity, resulting in an enhanced intensity and stability of the LIBS spectra. Additionally, 42 refined slag samples were quantitatively analyzed for Fe, Si, Ca, Mg, Al, and Mn at 1350℃, 1400℃, and 1450℃.The normalized full spectrum combined with partial least squares(PLS) quantification modeling was used, using the Ca Ⅱ 317.91 nm spectral line as an internal standard. The results show that using the internal standard normalization method can significantly reduce the influence of spectral fluctuations. Meanwhile, a temperature of 1450℃ has been found to yield superior results compared to both 1350℃ and 1400℃, and it is advantageous to conduct a quantitative analysis of the slag when it is in a “water-like” state with low viscosity.

DGConv: A Novel Convolutional Neural Network Approach for Weld Seam Depth Image Detection

We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations.Initially,to enhance the capability of deep neural networks in extracting geometric attributes from depth images,we developed a novel deep geometric convolution operator(DGConv).DGConv is utilized to construct a deep local geometric feature extraction module,facilitating a more comprehensive exploration of the intrinsic geometric information within depth images.Secondly,we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network(FCN8)to establish a high-performance deep neural network algorithm tailored for depth image segmentation.Concurrently,we enhance the FCN8 detection head by separating the segmentation and classification processes.This enhancement significantly boosts the network’s overall detection capability.Thirdly,for a comprehensive assessment of our proposed algorithm and its applicability in real-world industrial settings,we curated a line-scan image dataset featuring weld seams.This dataset,named the Standardized Linear Depth Profile(SLDP)dataset,was collected from actual industrial sites where autonomous robots are in operation.Ultimately,we conducted experiments utilizing the SLDP dataset,achieving an average accuracy of 92.7%.Our proposed approach exhibited a remarkable performance improvement over the prior method on the identical dataset.Moreover,we have successfully deployed the proposed algorithm in genuine industrial environments,fulfilling the prerequisites of unmanned robot operations.

PAPS: Progressive Attention-Based Pan-sharpening

Pan-sharpening aims to seek high-resolution multispectral(HRMS) images from paired multispectral images of low resolution(LRMS) and panchromatic(PAN) images, the key to which is how to maximally integrate spatial and spectral information from PAN and LRMS images. Following the principle of gradual advance, this paper designs a novel network that contains two main logical functions, i.e., detail enhancement and progressive fusion, to solve the problem. More specifically, the detail enhancement module attempts to produce enhanced MS results with the same spatial sizes as corresponding PAN images, which are of higher quality than directly up-sampling LRMS images.Having a better MS base(enhanced MS) and its PAN, we progressively extract information from the PAN and enhanced MS images, expecting to capture pivotal and complementary information of the two modalities for the purpose of constructing the desired HRMS. Extensive experiments together with ablation studies on widely-used datasets are provided to verify the efficacy of our design, and demonstrate its superiority over other state-of-the-art methods both quantitatively and qualitatively. Our code has been released at https://github.com/JiaYN1/PAPS.

Generalized polynomial chaos expansion by reanalysis using static condensation based on substructuring

This paper presents a new computational method for forward uncertainty quantification(UQ)analyses on large-scale structural systems in the presence of arbitrary and dependent random inputs.The method consists of a generalized polynomial chaos expansion(GPCE)for statistical moment and reliability analyses associated with the stochastic output and a static reanalysis method to generate the input-output data set.In the reanalysis,we employ substructuring for a structure to isolate its local regions that vary due to random inputs.This allows for avoiding repeated computations of invariant substructures while generating the input-output data set.Combining substructuring with static condensation further improves the computational efficiency of the reanalysis without losing accuracy.Consequently,the GPCE with the static reanalysis method can achieve significant computational saving,thus mitigating the curse of dimensionality to some degree for UQ under high-dimensional inputs.The numerical results obtained from a simple structure indicate that the proposed method for UQ produces accurate solutions more efficiently than the GPCE using full finite element analyses(FEAs).We also demonstrate the efficiency and scalability of the proposed method by executing UQ for a large-scale wing-box structure under ten-dimensional(all-dependent)random inputs.