The inherent flexibility and real-time deformation of breast tissue pose significant challenges for achieving full coverage and accurate lesion localization in autonomous breast ultrasound scanning. This paper introduces a robust finite state machine-based framework that mimics the decision-making process of an experienced physician, dynamically transitioning between the global breast scan and the fine lesion scan. An autonomous radial and anti-radial global scan pattern ensures comprehensive breast coverage. To avoid lesion misidentification caused by soft tissue movement, a real-time lesion fine scan method is proposed for lesion detection and localization. Experimental results demonstrate that the system in full coverage tests achieves 7 identified lesions out of 7 existing lesions and maintains a robust localization accuracy of$\mathbf{3. 2 3 ~ m m}$across phantoms with varying stiffnesses.

Deformable object manipulation (DOM) which is a common subtask in various surgical procedures represents an inevitable challenge in robot-assisted surgery (RAS) due to complex nonlinear deformation. This paper proposes a deep reinforcement learning guided adaptive control (RLAC) modelfree framework, which combines learning-based and Jacobianbased methods. To complement each other for optimized performance, we harness the sampling of deep reinforcement learning (DRL) policy explored in simulations to solve a reasonable estimation of the initial deformation Jacobian. In early control iterations, the actions suggested by the DRL agent are adopted until the estimated real-time Jacobian approximates the actual deformation model. Subsequently, the independent Jacobianbased adaptive control (AC) with sufficient initial deformation awareness begins execution to achieve precise internal feature manipulation on deformable objects. Experimental results demonstrate that our method enables more efficient positioning and exhibits near-optimal positioning paths. RLAC with robust sim-to-real performance provides a feasible approach for the complex autonomous DOM in the real world.

Circular electrical connectors (CECs) have a wide range of applications in scenarios that require reliable connections. However, sockets are often located in narrow scenes with random spatial orientations, complex lighting conditions, and obstructions from cables, making it difficult to accurately locate them through cameras. Besides, due to the complex geometric structure of CECs and the presence of electrode protection slots, the existing research on the assembly of cylindrical or polygonal pegs and holes may not be applicable to the assembly of such components. To this end, this article proposes a novel robotic assembly strategy for CECs with small relative initial deviations, whose core is to design a search trajectory and heuristic force strategy to perceive force/pose (F/P) discontinuity characteristics under different geometric constraints. This assembly strategy is independent of the CEC's size and is not affected by the socket's spatial orientation. The experiments with two different sizes of CECs on a robot equipped with a 6-dimensional force/torque ($\mathbf{F} / \mathbf{T}$) sensor are conducted, and the effectiveness and robustness of the proposed assembly strategy for CECs are demonstrated.

Surgical procedures are inherently complex and dynamic, with intricate dependencies and various execution paths. Accurate identification of the intentions behind critical actions, referred to as Primary Intentions (PIs), is crucial to understanding and planning the procedure. This paper presents a novel framework that advances PI recognition in instructional videos by combining top-down grammatical structure with bottom-up visual cues. The grammatical structure is based on a rich corpus of surgical procedures, offering a hierarchical perspective on surgical activities. A grammar parser, utilizing the surgical activity grammar, processes visual data obtained from laparoscopic images through surgical action detectors, ensuring a more precise interpretation of the visual information. Experimental results on the benchmark dataset demonstrate that our method outperforms existing surgical activity detectors that rely solely on visual features. Our research provides a promising foundation for developing advanced robotic surgical systems with enhanced planning and automation capabilities.

This paper presents a robust monocular visual SLAM system that simultaneously utilizes point, line, and vanishing point features for accurate camera pose estimation and mapping. To address the critical challenge of achieving reliable localization in low-texture environments, where traditional point-based systems often fail due to insufficient visual features, we introduce a novel approach leveraging Global Primitives structural information to improve the system’s robustness and accuracy performance. Our key innovation lies in constructing vanishing points from line features and proposing a weighted fusion strategy to build Global Primitives in the world coordinate system. This strategy associates multiple frames with non-overlapping regions and formulates a multi-frame reprojection error optimization, significantly improving tracking accuracy in texture-scarce scenarios. Evaluations on various datasets show that our system outperforms state-of-the-art methods in trajectory precision, particularly in challenging environments.

Dual-arm robots, with their high flexibility and broad operational range, are widely used in industrial and household transportation tasks. However, their high degrees of motion freedom and the closed-chain constraints formed during transportation pose challenges for path planning and force control. This study proposes a dual-agent control framework based on the Soft-Actor-Critic (SAC) algorithm of Deep Reinforcement Learning (DRL), where one agent is responsible for path planning and the other handles force control. This framework enables dual-arm robots to achieve dynamic obstacle avoidance, avoid unsolvable configurations and singularities, and generate efficient and smooth paths, while also fulfilling internal force tracking requirements based on task demands. Additionally, it addresses the gap in transferring the force control agent from simulation to real-world applications through a state mapping network, and the force control agent does not require retraining for different objects. Finally, the effectiveness of the proposed framework is validated through two scenes, including multiple bookshelves stacking and dynamic obstacle avoidance during the box transportation. Validation was also carried out with objects of different geometries and weights.

Previous current-based dynamic identification methods suffer from coupled motor-side uncertainties, imprecise joint drive gains, and current noises. The combined identification methods integrate the bedplate wrench and motor current to derive the decoupled link-side and motor-side dynamic parameters. However, the in-situ calibration demand for the bedplate sensor and drive gain complicates the identification process. Further, asynchronous calibration and identification accumulate and amplify errors. Therefore, this article proposes a unified framework of synchronous calibration and identification for industrial robots. First, the bedplate sensor calibration and dynamic identification are formulated into a unified optimization model and solved. Next, the drive gains and joint dynamic parameters are extracted under the same optimization structure. Then, a unified framework is founded to seamlessly integrate the in-situ calibration, synchronous identification, physical feasibility constraints, composite sensing fusion, and nonlinear friction estimation. Finally, the excitation trajectory for combined identification is delicately designed and executed. The high identification precision is evaluated through cross-validation experiments by the prediction performance of bedplate wrench and joint torque by up to 97.581% and 93.318%, respectively. The suggested framework outperforms other advanced methods and decreases parameter standard deviations.Note to Practitioners—Accurate robot dynamics are crucial for high-performance model-based control techniques. This research aims to address two issues that interfere with combined dynamic identification results: the coupled motor-side uncertainties induced by current signals and the accumulated errors caused by asynchronous sensing calibration and robot dynamic identification. Thus, this article proposes a unified framework of in-situ calibration and synchronous identification for industrial robots using composite sensing, which can bring many benefits to practitioners. Firstly, the proposed framework extracts the robot dynamic and sensing device parameters within one excitation trajectory execution, which significantly refines the results and simplifies the experimental process by the integrated algorithm structure. Secondly, the suggested optimization paradigm of synchronous calibration and identification can be implemented for different types of industrial robots in various scenarios easily. Thirdly, the calibrated device parameters and measurement covariance matrices contribute to the further applications of robot force perception and control.

Wheeled climbing robots have great application prospects in the machining of large components with variable curvature. However, its accurate motion control on variable curvature surfaces faces two fatal challenges. The varied contact states between the robot’s wheels and the variable curvature surfaces make it difficult to establish an accurate kinematics model. Additionally, there exists a different degree of robot slippage when the robot moves in different attitudes due to the dragging effect of gravity. To overcome the above problems, we first present a kinematics modeling method with instantaneous plane constraints on a variable curvature surface. Subsequently, a linear extended state observer (LESO)-based nonlinear model predictive control (NMPC) scheme is designed, in which the NMPC is used to calculate the nominal control inputs and the LESO is used to estimate and compensate for lumped disturbance brought by the robot slippage and surface constraints. Experiments on a real wind turbine blade with variable curvature show that the proposed control scheme can well eliminate the influence of lumped disturbance, and the climbing robots can achieve unbiased (AVG < 0.1 mm) and high-precision (RMSE < 2 mm) trajectory tracking. Note to Practitioners—Wheeled climbing robots, capable of adhering to curved surfaces of workpieces while in motion, provide a new approach for machining large components when equipped with machining actuators. This paper is motivated by the tracking control problem of wheeled climbing robots on curved surfaces with variable curvature, which is crucial for ensuring effective machining. The varying contact states between the wheels and the surfaces during the robot’s motion, along with the robot slippage introduced by the dragging effect of gravity, result in discrepancies between the ideal and actual motion speeds. These discrepancies introduce model uncertainties and thus pose challenges for achieving high-precision trajectory tracking. Additionally, current research on climbing robots primarily focuses on prototype development, with practitioners predominantly utilizing open-loop controller or basic model-free control schemes. This often leads to non-robust trajectory tracking of the climbing robot. To address these problems, this paper offers an accurate kinematics modeling method considering instantaneous plane constraints of the curved surfaces and a LESO-based NMPC scheme which can effectively reduce the impact of external disturbances. This approach provides a solution for automatic and high-precision trajectory tracking for climbing robot with localization system in factories, offering the potential for high-quality machining of large components.

Electrical connectors (ECs) are extensively employed in industrial scenarios, and their assembly quality is crucial. However, these connectors are often located in confined spaces, which poses challenges of complex lighting conditions and visual occlusions during the execution of robotic assembly tasks. Hence, guiding robots to assemble solely through force/torque (F/T) feedback is an alternative way. However, there is currently limited research on achieving assembly tasks solely through F/T information, especially when the spatial pose of the socket is uncertain, and how to achieve the robotic assembly of ECs remains a difficult problem. To this end, this paper proposes a novel strategy for assembling ECs under large initial pose deviations. Specifically, inspired by observing the assembly process of humans without visual assistance, the robotic assembly is first divided into two stages: by arbitrary surface tracking, the relative pose of the current EC is confirmed and adjusted to a dual-point contact state, i.e. the conversion from non-contact to dual-point contact; by setting heuristic F/T, the alignment of the edge, plane and slots of EC is driven, i.e. the alignment of the plug and socket. Next, we analyse the geometric constraint states at these stages and formulate the corresponding desired contact F/T strategies. To our knowledge, this may be the first attempt to achieve the robotic assembly of ECs under large initial deviations solely using F/T information. Finally, experiments on a UR5 robot indicate that the proposed strategy exhibits robustness to large initial pose deviations and can overcome obstacles caused by friction and jamming to ensure the robotic assembly of ECs. Note to Practitioners—The automatic assembly of ECs in confined spaces with visual occlusions remains an unsolved challenge, especially in aerospace scenarios where manual assembly may not be appropriate. The proposed assembly strategy is based on F/T perception without visual assistance, which can effectively address the perception and assembly processes of plugs under large initial pose deviations, demonstrating robust performance. With the requirement of only a F/T sensor and no need for precise dimensions of the assembly object, this strategy exhibits favourable deployment characteristics for automatic assembly platforms. Furthermore, the proposed strategy can be optimized by the integration of learning-based methods into the perception process, thereby further improving assembly efficiency.

In this paper, we propose a prescribed-time synchronization (PTS) algorithm for networked Euler-Lagrange systems subjected to external disturbances. Notably, the system matrix and the state of the leader agent are not accessible to all agents. The algorithm consists of distributed prescribed-time observers and local prescribed-time tracking controllers, dividing the PTS problem into prescribed-time convergence of distributed estimation errors and local tracking errors. Unlike most existing prescribed-time control methods, which achieve prescribed-time convergence by introducing specific time-varying gains and adjusting feedback values, we establish a class of${\mathcal {K}}_{T}$functions and incorporate them into comparison functions to represent time-varying gains. By analyzing the properties of class${\mathcal {K}}_{T}$and comparison functions, we ensure the prescribed-time convergence of distributed estimation errors and local tracking errors, as well as the uniform boundedness of internal signals in the closed-loop systems. External disturbances are handled and dominated by the time-varying gains that tend to infinity as time approaches the prescribed time, while the control signal is still guaranteed to be bounded. Finally, a numerical example and a practical experiment demonstrate the effectiveness and innovation of the algorithm. Note to Practitioners—This paper aims to address the issue of prescribed-time synchronization for networked Euler-Lagrange systems. Existing research on asymptotic and finite-time convergence reveals that the settling time for synchronization is significantly influenced by the system’s initial values and controller parameters, making it challenging to be freely pre-designed. In contrast, our proposed prescribed-time synchronization algorithm ensures that all Euler-Lagrange systems achieve synchronization within a prescribed time. The effectiveness of our algorithm has been validated through numerical simulations and physical experiments. In practical applications, our algorithm can be utilized for cooperative control in robotic manipulators and drones. Compared to traditional PD controllers, our proposed algorithm not only offers the advantage of arbitrary settling time configuration in cooperation but also ensures faster response speeds and higher control accuracy, owing to the incorporation of time-varying gains.

In featureless point clouds, such as cylinder-ruled surfaces or shapes with lots of flattened areas, we observe that some classic iterative closest point variants, including point-to-point, point-to-plane, and symmetric metrics, are trapped in local minima. To explain the above phenomenons, we derive that the upper bounds error of landscapes (UBEL) of point-to-plane and symmetric metrics are nearly zero in the above scenes, resulting in slow convergence or local minima. To address the above challenges, we introduce a constrained registration method, which integrates geometry distance constraints (GDC) and weighted enhanced distance metric. Specifically, WED combines point-to-point and point-to-plane metrics, resulting in a steeper UBEL than point-to-plane and symmetric; GDCs constrain the transformation matrix into a feasible subset to escape local minima. Moreover, we introduce a dynamic slack of constraint algorithm to improve the stability of the linear perturbation in the constrained registration problem. Simulations and experiments are conducted on typical featureless objects, including a turbine blade, a cylinder-ruled surface, an outlet guide vane, and a rotor engine, to verify the effectiveness and efficiency of the presented registration framework.

Accurate and reliable annotation of anatomical landmarks in laparoscopic surgery remains a challenge due to varying degrees of landmark visibility and changing shapes of human tissues during a surgical procedure in videos. In this paper, we propose a knowledge-driven framework that integrates prior surgical expertise with visual data to address this problem. Inspired by visual reasoning knowledge of tool-anatomy interactions, our framework models a spatio-temporal graph to represent the static topology of tool and tissue and dynamic transitions of landmarks' temporal behavior. By assigning explainable features of the surgical scene as node attributes in the graph, the surgical context is incorporated into the knowledge space. An attention-guided message passing mechanism across the graph dynamically adjusts the focus in different scenarios, enabling robust tracking of landmark states throughout the surgical process. Evaluations on the clinical dataset demonstrate the framework's ability to effectively use the inductive bias of explainable features to label landmarks, showing its potential in tackling intricate surgical tasks with improved stability and reliability.

Harmonious human-robot collaboration requires the robot to behave like a human partner, which raises the critical question of what factors make the robot do so. This paper proposes a series of policies based on empathetic and non-empathetic intent inference, proactive and reactive action planning, and ego and non-ego action styles to examine which modules enable robots to exhibit human-like behaviors. Two series of experiments are conducted with human subjects to test the performance of the proposed controllers. In Experiment 1, the participant must identify whether the collaborating partner is a human, similar to a Turing test. The classification results empirically verify that the designed empathetic proactive policies enable the robot to exhibit human-like behaviors. Experiment 2 indicates that the proposed policy can be applied to complex collaborative tasks, and this result is consistent with the findings of Experiment 1. From empirical evidence from the experiments, we believe that empathy and proactive policies are essential elements to enable robots to perform human-like actions.

In minimally invasive endovascular interventional surgery, guidewire navigation is an indispensable process. However, even experienced physicians often encounter difficulties in manually manipulating the guidewire for branch selection, while also facing the risk of radiation exposure. In this study, we investigated robotic autonomous guidewire navigation methods. An electromagnetic system was used to track the real-time position and orientation of the guidewire tip, and a state space representing the guidewire within the vascular environment was constructed to guide the robot in precise guidewire manipulation. Experimental results demonstrated that the proposed trial-and-error and centerline-guided methods successfully completed navigation tasks in a static environment, outperforming human navigation performance in terms of trajectory smoothness, trajectory length, and incorrect branch entry counts. For dynamic environment navigation, dynamic time warping (DTW), a technique for measuring the similarity between two temporal sequences, was integrated into the centerline-guided method. The proposed approaches eliminate the need for visual feedback and thereby minimizing the risk of radiation exposure for both patients and medical staff present in the operating room during the procedure.

Soft growing robots with unique navigation (tip extension by eversion) hold great promise in rescue, medical, and industrial applications. Equipping them with grasping capability would enhance their usefulness in constrained environments for various applications. However, in traditional designs, the tip’s eversion naturally conflicts with grasping, and the addition of grippers at the tip would limit navigation inevitably in constrained environments. To realize grasping in such scenes without extra devices, we propose a nested bi-cavity-based growing soft robot (BIBOT). The new design consists of two coaxially nested cavities, where the inner and outer cavities extend synchronously by inversion and eversion of the film rolls. Such a bi-cavity design enables the BIBOT to navigate and grasp without relative movements between the body and environment, and avoids contact between the object and its surroundings as well. Further, a kinematics model is established and verified to precisely control its lengthening and steering by a feed mechanism. Finally, its capability in a constrained environment is demonstrated by navigating and grasping an object in a curved pipe with a variable internal diameter.

The magnetic foot module is the hardware core of leg-based magnetic climbing robot. To realize versatile mobility on large complex steel structures, the holding force density, energy consumption and sensing capability of magnetic foot are crucial. Here, we present a novel design of a magnetic foot that integrates the electro-permanent magnet (EPM), friction foot pad and flexible pressure sensor. Inspired by the Halbach magnet array, we propose a EPM with Circular Halbach Net structure (CHN-EPM). Due to the utilization of the flux concentration effect, CHN-EPM has a higher holding force density and faster electromagnetic response of magnetic flux switching than the traditional EPM structure. The magnetic holding force can be measured by the flexible pressure sensor between the friction foot pad and the CHN-EPM. The holding force and magnetic switching capacity of the magnetic foot have been verified by theory, FEM and experiment.

This work develops a dynamic compliant force control (DCFC) strategy for the robotic belt grinding system to suppress the vibrations and over-grinding phenomenon. First, the vibration mechanism is investigated, and the corresponding vibration models before and after contact are constructed, both of which can decompose the vibrations into three components: free, accompanying and forced vibrations. Next, the extra compliant hardware is equipped to the grinder to realize the dynamic adjustment of equivalent damping. The DCFC strategy considering mechanical compliance accompanied by the dynamic closed-loop control of the grinder damping is presented based on the empirical wavelet transform and multi-scale permutation entropy. Moreover, the cutting fluctuation ratio index is proposed to evaluate the severity of the over-grinding together with over-grinding time. Experiments demonstrate that compared with the general force control, the DCFC strategy can reduce the vibration amplitude from 3.01 mm/s$^2$to 0.97 mm/s$^2$, and the over-grinding time/cutting fluctuation ratio from 1.05 s/24.71% to 0.54 s/13.18%, consequently enhancing the grinding stability and quality.Note to Practitioners—This work is motivated by the need to maintain the grinding stability and suppress the over-grinding phenomenon for the robotic belt grinding system. The vibrations and over-grinding phenomenon caused by the weak rigidity and poor precision of the robot always result in incomplete grinding of the workpiece that needs partially manual regrinding. The proposed DCFC strategy enables a dynamic compliant contact force between the grinding tool and the robot, thus effectively suppressing the over-grinding phenomenon and enhancing the consistency of grinding quality, which is particularly suitable for considering material removal consistency in cut-in and cut-out areas. This method can be implemented by the user as described or be acquired as a standalone device, but some extra hardware needs to be equipped with the grinding tool to use this method.

State of health (SOH) estimation of lithium-ion batteries (LIBs) is of critical importance for battery management systems (BMSs) of electronic devices. An accurate SOH estimation is still a challenging problem limited by diverse usage conditions between training and testing LIBs. To tackle this problem, this article proposes a transfer learning-based method for personalized SOH estimation of a new battery. More specifically, a convolutional neural network (CNN) combined with an improved domain adaptation method is used to construct an SOH estimation model, where the CNN is used to automatically extract features from raw charging voltage trajectories, while the domain adaptation method named maximum mean discrepancy (MMD) is adopted to reduce the distribution difference between training and testing battery data. This article extends MMD from classification tasks to regression tasks, which can therefore be used for SOH estimation. Three different datasets with different charging policies, discharging policies, and ambient temperatures are used to validate the effectiveness and generalizability of the proposed method. The superiority of the proposed SOH estimation method is demonstrated through the comparison with direct model training using state-of-the-art machine learning methods and several other domain adaptation approaches. The results show that the proposed transfer learning-based method has wide generalizability as well as a positive precision improvement.

Calibrating unknown transformation relationships is an essential task for multirobot cooperative systems. Traditional linear methods are inadequate to decouple and simultaneously solve the unknown matrices due to their intercoupling. This article proposes a novel dual-robot accurate calibration method that uses convex optimization and Lie derivative to solve the dual-robot calibration problem simultaneously. The key idea is that a convex optimization model based on dual-robot transformation chain is established using Lie representation of special Euclidean group in 3 dimensions [SE(3)]. The Jacobian matrix of the established optimization model is explicitly derived using the corresponding Lie derivative ofSE(3). To balance the influence of the magnitudes of the rotational and translational optimization variables, a weight coefficient is defined. Due to the closure and smoothness of Lie group, the optimization model can be solved simultaneously using Newton-like iterative methods without additional orthogonalization processing. The performance of the proposed method is verified through simulation and actual calibration experiments. The results show that the proposed method outperforms the previous calibration methods in terms of accuracy and stability. The actual experiments are used to compare the proposed method with two existing calibration methods, and the mean measurement error of a certified ceramic sphere is reduced from 0.9205 and 0.5363 to 0.4381 mm, respectively.

This article presents a data-driven, Koopman operator-based modeling scheme for analyzing and predicting cross-coupling hysteresis effects of the piezoelectric tube scanners (PTSs) used in atomic force microscopes (AFMs). Such cross-coupling hysteresis effects between different PTS axes significantly reduce the positioning precision of AFMs. In contrast to most of the existing methods for PTS hysteresis, which involve complex nonlinear dynamics identification processes, the present study leverages the Koopman operator theory instead to treat the nonlinear hysteresis as a linear system. Therein, a Hankel extended dynamic mode decomposition (H-EDMD) algorithm is proposed to learn the finite-dimensional descriptions of the Koopman operator and the associated Koopman eigenspectrum. Moreover, the proposed H-EDMD even allows sparse sampling on the PTS systems, which is desirable in real industrial applications. Finally, extensive comparison experiments with a mainstream modified Prandtl-Ishlinskii model are conducted on an NTMDT Prima AFM to substantiate the effectiveness and superiority of the proposed H-EDMD method.

This paper presents a fast global collision detection method for robotic machining of large complex components, aiming to quickly determine whether there is a collision between the robot and the surrounding environment during the whole machining process. Geometric analysis shows that there are always some trajectory points on the motion path of the manipulator that are more likely to collide than the surrounding points during machining. These trajectory points with the highest collision probability within a certain range are defined as the feature points of global collision detection, and are used to replace all trajectory points to perform global collision detection, thus greatly improving the efficiency of related operations while ensuring accuracy. Compare to the traditional discrete collision detection method with computational complexity O($\text{n}^{2}$), the computational complexity of the proposed method is only O(n). Numerical analysis and application experiments verify the effectiveness of the proposed method. Note to Practitioners—Motion planning in robotic machining of large complex components usually needs to perform a lot of global collision detection. Existing methods generally have the problems of large calculation and low efficiency, which seriously affects the efficiency of motion planning. This is mainly because a single global collision detection usually includes no less than$n$times of static collision detection, where$n$is the number of trajectory points. In order to solve this problem, we present a new global collision detection method based on feature-point-set. It does not need to traverse all trajectory points for static collision detection, but only needs to detect a few feature points, that is, the trajectory points most likely to collide within a certain range. On the premise of ensuring the collision detection accuracy, the proposed method greatly reduces the execution times of static collision detection, and significantly improves the computational efficiency of global collision detection. Numerical analysis and experiments show that this method effectively improves the efficiency of motion planning in robotic machining of large complex components.

Localization in a large-scale three-dimensional scene is a key challenge faced by climbing robots on large workpieces. This article proposes a global localization method for climbing robots based on tether displacement sensor, visual-inertial odometry (VIO), and computer-aided design (CAD) model of workpieces. Tether displacement sensor measures the distance between robot and tether anchor with little drift, which enables robot to acquire global pose. Adsorption constraints on robot motion are extracted using CAD model to reduce the drift of VIO. The approach realizes global localization with high accuracy for the robots when climbing on large workpieces without other external locating equipment. The performance is verified with a prototype of climbing robot testing on real large workpieces. In all experiments, our method with adsorption constraints outperforms existing VIO. The largest drift of merged trajectory is as low as 0.51% in global localization on a wind turbine blade with length of 8 m.

Surgical workflow analysis integrates perception, comprehension, and prediction of the surgical workflow, which helps real-time surgical support systems provide proper guidance and assistance for surgeons. This article promotes the idea of critical actions, which refer to the essential surgical actions that progress towards the fulfillment of the operation. Fine-grained workflow analysis involves recognizing current critical actions and previewing the moving tendency of instruments in the early stage of critical actions. Aiming at this, we propose a framework that incorporates operational experience to improve the robustness and interpretability of action recognition in in-vivo situations. High-dimensional images are mapped into an experience-based explainable feature space with low dimensions to achieve critical action recognition through a hierarchical classification structure. To forecast the instrument's motion tendency, we model the motion primitives in the polar coordinate system (PCS) to represent patterns of complex trajectories. Given the laparoscopy variance, the adaptive pattern recognition (APR) method, which adapts to uncertain trajectories by modifying model parameters, is designed to improve prediction accuracy. The in-vivo dataset validations show that our framework fulfilled the surgical awareness tasks with exceptional accuracy and real-time performance.

Imbalanced vibration is an urgent yet challenging problem in active magnetic bearings (AMBs) rotor manufacturing due to the rotor mass imbalance effect. The virtue of active control in AMB systems lies in enabling substantial online mitigation of imbalanced vibrations. However, in practice, due to the lack of speed sensors in most of the existing AMB-rotor systems, efficient rotational speed feedback is still on the way. As a remedy, this article proposes a rotational speed sensor-free synchronous signal processor (SSP) with the double inputs:$x$- and$y$-axes direction displacement measurements of radial AMBs. The proposed SSP is capable of estimating the rotational speed and accordingly generating synchronous signals of the imbalanced vibrations by filtering noise in both directions. Such signals are afterward implemented as a feedforward compensator for eliminating the periodical imbalance effects. With the assistance of the Lyapunov theory, the conditions of the proposed SSP method together with the feedforward imbalance compensator are derived to guarantee the stability of the closed-loop AMB-rotor system. Extensive experimental results substantiate the effectiveness and superiority of the proposed SSP method in terms of imbalanced vibration suppression.

Perception and portability are critical issues for wearable gloves in hand assistive engineering. However, available wearable gloves either lack flexible sensing or are bulky. In this paper, we present a tendon-driven lightweight wearable glove with soft joint sensing, Sen-Glove. Sen-Glove is equipped with 14 soft strain sensors, which enables full bending motion monitoring of 14 joints of five fingers and greatly reduces the weight of the glove. Besides, modular design makes Sen-Glove more compact and weighs 161g in total, reducing the burden on hand. A series of mechanical tests are conducted to evaluate the characteristics of Sen-Glove. Experimental results show that Sen-Glove can withstand 500 bending cycles, assist the subject in grasping 21 multi-scale objects, and recognize 11 gestures. The classification accuracy of 11 different gestures reaches 98.6 %, which verifies the efficacy of the strain sensors.

Robot-Assisted Minimally Invasive Surgery (RAMIS), which introduced robot-actuated invasive tools to increase the dexterity and efficiency of traditional MIS, has become popular. Investigations on how to achieve autonomy in RAMIS have drawn vast intention recently, which urges further insights into the process of the surgical procedures. In this paper, the definition of critical actions, which discriminates the essential stages from regular surgical actions, is proposed to help decompose the complicated surgical processes. A critical intra-operative moment of the surgical workflow, which is called the keyframe, is introduced to indicate the beginning or ending moments of the critical actions. A keyframe detection method is proposed for critical action identification based on a new in-vivo dataset labeled by expert surgeons. Surgeons' criteria for critical actions are captured by the explainable features, which can be extracted from the raw laparoscopic images with a two-stage network. Motivated by the surgeon's decision process of keyframes, a hierarchical structure is designed for keyframe identification by checking the spatial-temporal characteristics of the explainable features. Experimental results show that the reliability of the proposed method for keyframe detection achieves unanimous agreement by expert surgeons.

Due to the robustness to robot modeling and camera calibration errors and avoidance of complete target geometry, image-based visual servoing has always been an important topic in the fields such as robotics, computer vision and so forth. When the image information obtained by the camera is mapped to the robotic task space to design the servoing control law, the resulting interaction matrix, which links the spatial velocity of the camera to the temporal variation of the selected image features, depends on the unknown feature depths. The use of inaccurate feature depths may influence the stability and robustness of the controller, and even cause the failure of the task. In this article, based on the perspective camera model, by employing the principle of reduced order observer, a novel logarithmic observer is presented for on-line recovery of feature depth. Compared with the typical observers now available, the presented observer offers several advantages: global convergence, a faster convergence rate of error structure than exponential error structure, a less restricted observability condition and greater robustness against measurements with noise. The comparison results of numerical simulations indicate the superiority of the presented observer, and real experiments with Kinect v2 sensor further validate the effectiveness of the presented observer in practical situation. Note to Practitioners—This article was motivated by the depth problem in the image-based visual servoing scheme, but it can also be used in other situations where the image depth information is needed, such as 3D reconstruction, robot navigation, etc. The existing depth acquisition methods include TOF sensors, stereo vision, depth observers and so on. However, TOF sensors are sensitive to light conditions, and the mounting space of stereo vision is slightly large, and there is contradiction between observation performance and computational complexity in most existing observers. In this article, a novel structure of logarithmic reduced order observer is described in detail, which can be utilized to estimate the depth information of images easily. The simulations and experiments verify the good performance of the observer. The limitations of the given observer are that the estimation accuracy is not very good under weak excitation, and the camera needs to be calibrated in advance. Future work will focus on overcoming these two limitations.

Industrial robots are characterized by good flexibility and a large working space, and offer a new approach for the machining of large and complex parts with small machining allowances (extra material allowed for subsequent machining). Parts of this type (such as aircraft skin parts, wind turbine blades, etc.) are easily deformed due to their large scale and low stiffness. Therefore, these parts cannot be directly machined according to the designed model. A feasible method is to plan a robotic machining path by using the point clouds of parts after clamping from onsite measurement which contains inherent defects of measurement such as noise points and abrupt points. In this article, a novel method is proposed to plan and optimize a robotic machining path that meets the requirements of smoothness, dexterity, and stiffness based on the point cloud from onsite measurement. The dual nonuniform rational B-spline curves of the machining path points and tool axis points are generated at first. Next, an objective function of smoothness optimization is established to filter out the local mutation of the path by considering the constraints of both the deformation energy and the deviation. Then, the objective function of robot postures optimization is established to optimize dexterity and Cartesian stiffness of a robot during the machining process. To show the feasibility of the proposed method, simulation and experiments are carried out. It is proved that the proposed method can generate a smooth machining trajectory. The stability of joint rotation and the rigidity and dexterity of the robot are improved during the machining process.

A novel radio frequency identification (RFID)-based mobile robot global localization method combining two kinds of RFID signal information, i.e., phase difference and readability, is proposed. Specifically, a phase difference model and a classification logic strategy based on readability are built and integrated into a particle filter localization algorithm. Compared with existing RFID localization methods, the proposed localization method can achieve competitive localization performance in an environment with a relatively sparse reference tag distribution and without the need for offline phase drift calibration. A series of real experimental tests were performed, and the results show that the proposed method can localize a mobile robot with centimeter-level position accuracy and satisfactory attitude angle accuracy when the distance between adjacent reference tags is approximately 60 cm, even if all RFID devices are commercial off-the-shelf (COTS). The proposed method provides a promising option for mobile robot localization applications, such as path tracking of mobile robots.Note to Practitioners—Mobile robot localization is a key technology for its location-based services. Considering that radio frequency identification (RFID) is entirely unaffected by light interference and has a globally unique ID, RFID has been regarded as a localization sensor with broad application prospects. This article proposes an RFID-based mobile robot global localization method combining phase difference and readability, by which the mobile robot can be accurately localized in an environment with a relatively sparse reference tag distribution and without the need for offline phase drift calibration. The experimental results indicate that the proposed method can localize the mobile robot with good performance, including centimeter-level position accuracy and satisfactory attitude angle accuracy. The proposed method can effectively contribute to many practical applications, such as the path tracking of a mobile robot.

This article proposes a standalone radio frequency identification (RFID)-based mobile robot navigation method, in which a mobile robot equipped with reader antennas can be continuously guided to a static object marked with a single passive UHF RFID tag. An observation model based on the RFID phase difference is built and integrated into a particle filter, by which the instantaneous relative position between the mobile robot and the tagged object can be detected in real time. Based on the position information extracted from the RFID system, the mobile robot adjusts its pose to move toward the RFID-tagged object. Compared with the existing RFID-based mobile robot navigation methods, the proposed method requires no external sensors other than the RFID and requires only a single passive tag. Experiments using commercial off-the-shelf (COTS) RFID devices are performed, and the results indicate that the mobile robot can satisfactorily realize navigation task with a distance accuracy of 4.04 cm and a bearing accuracy of 2.23°. The proposed method is well applicable for the navigation scenes in which the absolute position of the tagged target object is not known beforehand.Note to Practitioners—UHF radio frequency identification (RFID) has been widely applied as an asset management ID sensor in many fields. RFID-based mobile robot navigation technology can further increase its application value as a location sensor. This article proposes a standalone RFID-based mobile robot navigation method, in which the reference tag and the external sensors other than RFID are both not required. In the proposed method, only a single passive tag is attached to the static target object. Experimental results indicate that the proposed method can enable navigation task with good performance in situations in which the absolute navigation goal position is not known in advance.

Multirobot systems have shown great potential in dealing with complicated tasks that are impossible for a single robot to achieve. One essential problem encountered in cooperatively working of the multirobot systems is the unknown initial transformation relationships from hand to eye, base to base, and flange to tool. In this article, the problem of multicoordinates calibration for a dual-robot system is formulated to a matrix equation AXB = YCZ. A novel approach for simultaneously solving the unknowns in equation AXB = YCZ is proposed, which is composed of a closed form method based on the Kronecker product and an iterative method which converts the calculation of a nonlinear problem to an optimization problem of a strictly convex function. The closed form method is used to quickly obtain an initial estimation for the iterative method to improve the efficiency and accuracy of iteration. In addition, a series of conditions on the solvability of the problem are proposed to guide the operators to select appropriate robot attitudes during the calibration process. To show the feasibility and superiority of the proposed iterative method, two other calibration methods are chosen to be compared to the proposed method through simulation and practical experiments. The comparison results verify the superiority of the proposed method in accuracy, efficiency, and stability.

In order to improve the robustness and optimize trajectory of projective homography-based uncalibrated visual servoing (PHUVS) proposed in our previous work, an analytical expression of optimal trajectory for camera in projective homography space is proposed in this article, which is totally free of camera parameters and is corresponding to camera's shortest path in the 3-D space with straight path in translation and minimal geodesic in rotation. The projective homography is computed without scale ambiguity in both planning and tracking stages. The PHUVS controller is modified correspondingly to track the planned trajectory in projective homography space while maintaining superior characteristic of PHUVS under uncalibrated scenario. The simulations and experiments' results reveal the effectiveness and necessity of the proposed trajectory optimization method in the existence of large initial errors. Note to Practitioners-The state-of-the-art visual-guided robotic technology in industry usually requires system calibration, which is often costly, vulnerable, and challenging for ordinary workers. In our previous work, we offered an uncalibrated visual servo method based on projective homography named projective homography-based uncalibrated visual servoing (PHUVS), which is suitable for plug and play application for eye-in-hand robot visual servo tasks. However, PHUVS suffers from some defects, including undesirable 3-D space motion and local convergence. In this article, we proposed the trajectory planning method along with a modified PHUVS controller to improve the original one from the disadvantages mentioned earlier. This planning method is also calibration-free. With pure image information, a straight-line path in translational motion along with minimal geodesic in rotary motion can be achieved. This approach is capable of extending the range of applications for uncalibrated visual servo technology in robotic tasks, such as assembling, painting, and robotic machining.

A novel ultrahigh-frequency (UHF) radio frequency identification (RFID) localization method is proposed in this paper, by which the location of a static passive tag can be easily obtained using a mobile RFID antenna. An unwrapped phase-position model with three parameters is built, and the location of the tag can be pinpointed through an ordinary nonlinear least-squares algorithm. The main advantage of this method is that it is cheap in computation cost compared with the existing grid-based methods. The experimental tests confirm that the proposed method can localize the RFID tags with a competitive computational efficiency and accuracy performance, i.e., millisecond-level computing time and centimeter-level location accuracy. The proposed UHF RFID localization method is well suited to the pervasive location-aware applications, searching RFID-tagged item in the intelligent warehouse by the mobile robot with an onboard RFID system, for example.

Free-form surface matching that aligns measured points with a design model is a common problem in manufacturing automation. In this paper, an iterative variance-minimization matching (VMM) method is proposed to address measured points that have measuring defects, such as uneven/open point distributions and measuring noise. The basic idea is that the objective function is defined as the variance of the closest distance from each measured point to the design model, and the measuring defects are considered by incorporating an average distance item into the objective function. Using the defined average distance item, a strategy for analyzing the effect of measuring defects on VMM and existing methods is presented. It is shown that the VMM method does not easily become trapped in a local optimum when measuring defects exist. To consider convergence speed and convergence stability, a new distance based on the first-order point-to-point distance and point-to-tangent distance is developed and used in the objective function. To demonstrate the availability of the proposed method, quadratic convergence and positive definiteness are theoretically analyzed. The proposed method is efficient and insensitive to measuring defects and is useful for shape matching tasks involving free-form surface features. Note to Practitioners-This paper is motivated by the problem of matching measured points with a design model to automate manufacturing processes such as geometric inspection, workpiece localization, and allowance distribution. Measured points are obtained by applying a scanning device where measuring defects usually appear. Existing matching methods suffer from the drawback that the measured points may incline toward dense data and become trapped in a local optimum, due to measuring defects. To address this practical issue, this paper proposes a new method called variance-minimization matching (VMM), in which the objective function is optimized to weaken the effect of measuring defects. By examining the differences between VMM and existing methods, it is found that VMM can achieve quadratic convergence speed. Most importantly, the method is insensitive to uneven/open point distributions. In summary: 1) this method allows us to improve the matching accuracy in the presence of measuring defects; 2) there is no need to obtain a high-quality scan of the entire workpiece, potentially reducing scanning difficulty and improving scanning efficiency; and 3) the requirement of uniform sampling for measured points is reduced.

In the first part of this paper, a free-form surface matching method called variance-minimization matching (VMM) was proposed to address uneven/open point distributions and measuring noise. The convergence property and sensitivity to measuring defects were theoretically studied. In the second part of this paper, a series of experiments are presented to verify the feasibility of the proposed method in free-form surface matching. The experiments are divided into four sets: a measuring defects experiment, a noise experiment, a convergence experiment, and an artificial experiment. In the first set of experiments, the existing methods are prone to becoming trapped in a local optimum affected by uneven/open point distributions, which shows that measured points incline toward dense areas. However, in VMM, there is little inclination regardless of the increase in the number of measuring defects. In the second set of experiments, sensitivity to varying noise is tested. The results show that VMM helps prevent unstable sliding in the presence of Gaussian noise. In the third set of experiments, we compare convergence speed and convergence stability under different initial positions. It is verified that VMM exhibits the quadratic convergence. Finally, a set of artificial experiments is implemented, revealing that the proposed method is appropriate for use in automated manufacturing processes such as geometric inspection and allowance distribution. Note to Practitioners-Measuring defects usually occur when using a scanning device to obtain the measured points of a workpiece. Weakening the effect of measuring defects on matching results is critical to promoting manufacturing automation. This paper proposes a new method called variance-minimization matching (VMM) that considers measuring defects. In the first part of this paper, the modeling and theoretical analysis of VMM were introduced. In the second part of this paper, simulated experiments are performed to verify the feasibility of VMM in addressing uneven/open point distributions, measuring noise, and large initial positions. Next, artificial experiments employing VMM in geometric inspection and allowance distribution are presented. The proposed method also applies to other automated manufacturing processes, such as workpiece localization, deformation analysis, and complex parts repair.

Large skin parts play an important role in the aerospace industry. The wall thickness of the machined pocket in the skin part needs to be strictly controlled to ensure the transport capacity and structural strength. The wall-thickness accuracy is generally decreased by various factors, such as the shaping error of the workpiece blank, fixing error, machine tool error, and deformation caused by cutting force or internal stress. These factors are usually inevitable and stochastic due to the extremely weak rigidity and easy-to-deflect characteristics of the large skin parts. To ensure the wall-thickness accuracy, a fuzzy v-support vector machine (Fv-SVM)-based wall-thickness error decomposition method is proposed. The wall-thickness errors, which are monitored in the cutting process, are decomposed into spatial-related errors and time-related errors. The Fv-SVM-based decomposition method with the principle of spatial statistical analysis is a data-driven approach for intelligent manufacturing. The data-driven method can consider all factors that affect the wall-thickness accuracy, while the model-driven method usually only considers one factor, such as the workpiece deformation or fixing error. After decomposition, the spatial-related wall-thickness error is offline compensated, and the time-related wall-thickness error is compensated by using a real-time strategy. The novel method can be applied to complex tool paths. The cutting experiment of rectangular pockets in a large skin panel was conducted to verify the effectiveness of the proposed method. The wall-thickness accuracy can be improved to 0.05 mm for the workpiece with only 2 mm thickness.

This paper presents a novel eddy current damper design for chatter suppression in robotic milling process. The designed eddy current dampers are installed on a milling spindle to damp the tool tip vibrations. The structural design of the eddy current dampers and the working principle of the proposed vibration attenuation method are explained. Finite element method is used to analyze the magnetic flux density and the magnetic force generated by the designed eddy current. The dynamics of the robotic milling system without and with eddy current dampers are modeled, and the damping performance of the proposed method is verified through simulations in both frequency and time domains. The results show that the peaks of the tool tip frequency response function caused by the spindle and milling tool modes are damped by 3.2 dB and 5.3 dB, respectively, and the chatter stability is improved by about 43% in the high spindle speed zone, compared to the case without eddy current dampers.

Without the 3-D geometry of the target and robust to camera calibration error, image-based visual servoing schemes have gained a lot of attention. However, the depth of the selected feature, which is involved in the interaction matrix relating the time variation of the feature to the velocity twist of the camera, must be estimated correctly to guarantee the stability of the controller. To this end, this paper proposes a new nonlinear reduced-order observer structure to recover the feature depth in real time. Compared with the existing works, the proposed observer has a global asymptotic convergence property and fast convergence rate, and the convergence rate can be easily adjusted only using a single gain parameter. In addition, the proposed observer has a less restrictive observability condition and stronger robustness to noisy measurements. Extensive comparative numerical simulations are carried out to validate the effectiveness of the proposed depth observer.

An uncalibrated visual servo method based on projective homography, denoted as Projective Homography based Uncalibrated Visual Servoing (PHUVS), is proposed in this paper, in which a novel task function based on the element of projective homography is devised to realize visual servo without a prior knowledge of the camera intrinsic parameters and hand-eye relationships. The main advantage of this method is that it is not only suitable for totally uncalibrated scenarios but also cheap in computation costs when compared with classical image-based uncalibrated visual servoing methods. Numerical experiments are performed and the results confirm that the new approach is capable of both static positioning and dynamic tracking tasks, and presents competitive computational efficiency and accuracy performance.

Chatter is a typical kind of unstable dynamics often encountered in machining processes, which often results in overcut and rapid tool wear. Hence, chatter phenomenon worsens the surface quality and reduces productivity in milling systems as well. Recent years have witnessed a surging industrial demand of high quality and high efficiency machining. Specifically, for low radial immersion milling situation, large depth of cuts is inevitably needed so as to increase the machining efficiency. To fulfill such a task, this paper develops a robust active control method to mitigate the chatter dynamics of low radial immersion milling processes. The present approach increases the axial depth of cuts, and the improvement is inversely proportional to the radial immersion ratio. Finally, case studies are conducted to show the substantially enlarged stable region in the stability lobe diagram (SLD) spanned by spindle rotational speed and axial depth of cut. Thus, the method can be expected to improve the efficiency of milling processes.

Piezo-actuated stages have become more and more promising in nanopositioning applications due to the excellent advantages of the fast response time, large mechanical force, and extremely fine resolution. Modeling and control are critical to achieve objectives for high-precision motion. However, piezo-actuated stages themselves suffer from the inherent drawbacks produced by the inherent creep and hysteresis nonlinearities and vibration caused by the lightly damped resonant dynamics, which make modeling and control of such systems challenging. To address these challenges, various techniques have been reported in the literature. This paper surveys and discusses the progresses of different modeling and control approaches for piezo-actuated nanopositioning stages and highlights new opportunities for the extended studies.

This paper presents a trajectory tracking control strategy based on the subspace stabilization approach to accurately manipulate an under-actuated flying robot from a known initial state to the desired terminal state. To facilitate the development of this tracking strategy, the dynamical model of the quadrotor is firstly proposed. Subsequently, an optimal trajectory generation algorithm is adopted to generate dynamically consistent trajectories regarding the initial and terminal state constraints in specific missions. Then, a trajectory tracking control strategy based on the subspace stabilization approach is developed considering the lumped disturbances and time delays. The developed control strategy is applied for ball juggling of a highly under-actuated quadrotor, which is a popular flying robot in recent years. Real-time experimental results show that the quadrotor can be accurately manipulated from a known initial state to the desired terminal state within a given time horizon. In the consecutive juggling tasks, the quadrotor with a racket of radius 0.065 m can consecutively juggle the ball for averagely 4 hits in each rally, and a longest rally achieved by the developed control strategy is 14 hits. The feasibility of the developed control strategy is also preliminarily verified through the cooperative juggling between two quadrotors. All of these results demonstrate the effectiveness of the developed control strategy.

Fixtures are used in almost all modern manufacturing operations. In practice, there is a wide range of specifications on the manufacturing precision and thus different requirements on workpiece locating accuracy during each production process. In view of that, this paper developed a unified signed distance function framework. Under this framework, three systems of sensitivity equations, which link the locator source errors to the resulting workpiece localization error, were derived. Accordingly, unique linear, one-sided quadratic, and two-sided quadratic models have been developed. These three models, distinguished by whether or not taking into account workpiece and/or locator curvature effects, provide a range of locating precision analysis which is illustrated and verified by several examples. The developed modeling technique can handle general fixture locating rather than being limited to certain locating schemes. The proposed models are of practical relevance and have great potential to be applied towards locating scheme evaluation, fixture design, fault diagnosis, and tolerance analysis.

The aim of this paper is to develop an integral equation based spectral method for prediction of chatter stability in low radial immersion milling. First, the delay-differential equation with time-periodic coefficients governing the dynamic milling process is transformed into the integral equation. Then, the duration of one tooth period is divided into the free vibration and the forced vibration processes. While the former one has an analytical solution, the discretization technique is explored to approximate the solution of the latter one. After the forced vibration duration being equally discretized, the Gauss-Legendre formula is used to discretize the definite integral, in the meantime the Lagrange interpolation is adopted for approximating the state item and the time-delay item by using the corresponding discretized state points and time-delay state points. The approximate Floquet transition matrix is thereafter constructed to predict the milling stability based on the Floquet theory. The benchmark examples are utilized to verify the proposed method. Compared with previous time domain methods, the proposed method enables higher rate of convergence. The results also demonstrate that the proposed method is high-effective.

Acoustic transducers with large radiation surface are commonly used in non-contact levitation and transportation systems. Traditional modeling can not predict its dynamic performance precisely. There is not enough documented information on design and optimization. A coupled 3D model has been built in this work. Modal and harmonic analysis has been performed to investigate mechanical and electrical behaviors. Identical experimental conditions were simulated by finite element method modeling to investigate size effect and optimize the transducer. Experiment has been set up to validate the model. The good agreement between the simulated and experimental results shows that the model in design procedure provides an optimal tool to construct an acoustic transducer used in Near Field Acoustic Levitation.

The nine The ten papers in this special issue focus on three areas: equipment automation, operations automation, and modeling.

Mura is a typical vision defect of LCD panel, appearing as local lightness variation with low contrast and blurry contour. This letter presents a new machine vision inspection way for Mura defect based on the level set method. First, a set of real Gabor filters are applied to eliminating the global textured backgrounds. Then, the level set method is employed for image segmentation with a new region-based active contours model, which is an improvement of the Chan-Vese's model so that it more suitable to the segmentation of Mura. Using some results from the level set based segmentation, the defects are quantified based on the SEMU method. Experiments show that the proposed method has better performance for Mura detection and quantification.

Fixtures are devices used in manufacturing systems to locate, immobilize, and hold workpieces. There are many different numerical measures proposed in the literature to quantify the locating accuracy of a fixture, and its capability in firmly holding the workpiece in the presence of external loads. These numerical measures are useful in fixture layout design and the related area of grasp synthesis. As a number of numerical measures are available, a user may face the problem that how to choose an appropriate one as the optimality criterion in practice for fixture design. In this paper, we present a comparative study on several widely used optimality criteria, based on both theoretical analysis and simulation studies. First, we establish a set of inequalities, which describe the relations between several widely used numerical measures. Second, comparisons between optimality criteria are demonstrated by numerical examples.

In mechanism and machine design, the notion of serial kinematics machine (SKM), parallel kinematics machine (PKM) and hybrid kinematics machine (HKM) is well understood. In this paper, we introduce a fourth type of kinematics machine, known as quotient kinematics machine(QKM). A QKM generating a subgroup motion G consists of two mechanisms (or motion modules) acting in unison, one synthesizing a subgroup H of G, and another that of a complement of G/H. Apparently, the two motion modules of a QKM have simpler kinematic structures than that of a SKM, PKM or HKM with the same motion type G, and thus is expected to have performance advantages in terms of stiffness (speed and accuracy), modularity and etc, over its SKM/PKM/HKM counterparts. The formulation of the QKM concept and its analysis and synthesis are considered in this paper.

This paper presents an efficient algorithm for grasp synthesis and fixture layout design in discrete domain. Given N candidate contact points on the surface of a 3-D object, the algorithm determines a minimal subset from the candidate points so that they construct a grasp or a fixture with the form-closure property. The proposed algorithm is implemented by solving a single linear program. It has been shown that a minimal subset of the contact points that achieves form-closure can be directly identified from the optimal basic solution vector of the linear program, provided that the solution vector is nondegenerate. The proposed algorithm has a straightforward geometric interpretation, which has also been described in the paper. The primary advantage of the proposed algorithm is its computational simplicity, of which the overall complexity is O(N). Numerical examples have been presented to verify the effectiveness and efficiency of the algorithm, which show that the computational time cost of the algorithm is much less than the existing techniques described in the literature

Type synthesis of low (3-5) degree of freedom (Dof) spatial parallel manipulators is well documented in literature. Recent approaches such as proposed in J.M. Herve and F. Sparacino (1991) - Z. Huang and Q.C. Li (2003) showed some systematic design capability, but did not develop an equally effective means to check for prescribed finite motion. In this paper, we studied the finite motion set of parallel manipulators from a general input-affine nonlinear system viewpoint. Differential geometry tools for controllability (reachability) analysis of nonlinear system on a differential manifold are utilized together with lie group theory. Our techniques are shown to be effective by applying to a systematic type synthesis method proposed in M. Jian, et al. (2005) and W. Yuanqing, et al. (2005)

Computation of grasps with form/force closure is one of the fundamental problems in the study of multifingered grasping and dextrous manipulation. Based on the geometric condition of the closure property, this paper presents a numerical test to quantify how far a grasp is from losing form/force closure. With the polyhedral approximation of the friction cone, the proposed numerical test can be formulated as a single linear program. An iterative algorithm for computing optimal force-closure grasps, which is implemented by minimizing the proposed numerical test in the grasp configuration space, is also developed. The algorithm is computationally efficient and generally applicable. It can be used for computing form/force-closure grasps on 3-D objects with curved surfaces, and with any number of contact points. Simulation examples are given to show the effectiveness and computational efficiency of the proposed algorithm.

A new control method based on a combination of robust control and internal model control has been proposed. This control system includes internal model controller for velocity loop, robust controller for position loop, and a feedforward controller. The internal model controller is designed to suppress disturbance. Stability robustness of the closed loop is provided by the robust controller. The zero phase error tracking controller is adopted to act as a feedforward controller to further improve the tracking performance. The theoretical analysis shows the validity of the proposed control scheme. Furthermore, simulations and experimental results are presented to demonstrate performance improvement of the proposed control structure.

Various parallel kinematic manipulator (PKM) type design papers enumerate eligible links as the combination of revolute and prismatic joints and synthesize using local screw theory, but analysis and comparison on real capacity of different types has not been developed yet. This paper applies differential Lie group tools to developing a spectrum of so called regular link spatial translation (T(3)) PKM, which maximized workspace from a topological point of view.

Nonlinear friction and external disturbances affect the positioning accuracy of high-speed motion systems, especially impelled by linear motor. Thus, how to eliminate theses disturbance should be considered when designing a robust controller. This paper presents a controller, which includes three parts: a proportional-plus-derivative (PD) feedback controller, a friction compensator, and a disturbance observer. The friction compensator is based on LuGre model and it compensates for nonlinear friction. The disturbance observer is used to eliminate the friction compensation error and other external disturbances. Experimental results show that the controller gives high positioning accuracy and more robust performance in the presence of disturbances.

Computation of grasps with form/force-closure is one of the fundamental problems in the study of multifingered grasping and dexterous manipulation. Based on the geometric condition of the closure property, this paper presents a numerical test to quantify how far a grasp is from losing form/force-closure. With the polyhedral approximation of the friction cone, the proposed numerical test can be formulated as a single linear program. An iterative algorithm for computing optimal force-closure grasps, which is implemented by minimizing the proposed numerical test in the grasp configuration space, is also developed. The algorithm is computationally efficient and generally applicable. It can be used for computing form/force-closure grasps on 3D objects with curved surfaces, and with any number of contact points. Several simulation examples are given to show the effectiveness and computational efficiency of the proposed algorithm.

This paper addresses the problem of measurement point planning for 3-D workpiece localization in the presence of part surface errors and measurement errors. A number of frame-invariant norms of the infinitesimal rigid body displacement are defined to quantify the localization accuracy required by manufacturing processes. Then, two kinds of frame-invariant indices are derived to characterize the sensitivities of the accuracy measures to the sampling errors at the measurement points. With a dense set of discrete points on the workpiece datum surfaces pre-defined as candidates for measurement, planning of probing points for accurate recovery of part location is modeled as a combinatorial problem focusing on minimizing the accuracy sensitivity index. A heuristic floating forward search algorithm is presented to efficiently find a near-optimal solution. An example confirms the validity of the presented criteria and algorithm.

By using the concept of gauge function, a pseudodistance function is defined for quantifying the clearance or the penetration depth of two convex point sets, depending on whether they separate or intersect. The linear programming formulations (for convex polyhedra) and the nonlinear constrained optimization formulations (for general convex objects) are presented for its calculation. For a pair of convex polyhedra, the pseudodistance function is differentiable almost everywhere with respect to the coordinate vectors of their vertices. Sufficient conditions for the differentiability and the characterization of its derivative are presented. By applying the pseudodistance function to the wrench space, a numerical measure of multifingered grasps is defined, which can be used for qualitative test and quantitative analysis of the force-closure property. On this basis, two algorithms for planning optimal force-closure grasps on general three-dimensional objects are developed. In addition, the application of the pseudodistance function in robot path planning is also demonstrated.

This paper presents a numerical test for the closure properties (force closure and form closure) of multifingered grasps. For three-dimensional (3-D) grasps with frictional point contacts or soft contacts, the numerical test is formulated as a convex constrained optimization problem without linearization of the friction cone. For 3-D frictionless grasps, it can be calculated by solving a single linear program. The proposed numerical test (along with the rank of the grasp matrix) provides an efficient tool for the analysis of the force-closure property and the relative force-closure property.

The advanced sensor networks play an increasingly important role to promote maintenance strategy shifting from traditional periodic maintenance to proactive e-maintenance in modern industry. IP sensor, a smart device that is Internet visible and capable, have been implemented in the paper, which has compliant STIM data structure described by IEEE 1451.2 standard, while an IP module is presented and adopted herein to instead NCAP to enable sensors entering into Internet in an easy and cost-effective way. It provides a powerful tool to gather information at plant level of the distributed application to enable remote maintenance engineering transparently monitoring and operating the field devices over Internet in e-maintenance system. Finally, a full working prototype of the system is realized, and two experiments have been conducted to experimentally verify the IP sensor proposed herein are feasible and suitable for distributed applications under LAN environment.

In this paper, we present a quantitative measure of multifingered grasps. The measure quantifies the capability of a grasp in firmly holding an object while resisting external loads and/or disturbances. It can also be used for qualitative test of closure properties (form closure and force closure). For planar grasps and frictionless three-dimensional (3-D) grasps, the quantitative measure can be computed efficiently by solving a set of linear programs, while for frictional 3-D grasps, it can be computed by solving nonlinear programs without linearization of the friction cone. By using the proposed quantitative measure, an algorithm for grasp synthesis on polygonal objects is developed. Rather than producing a single grasp configuration, the algorithm computes all grasps on a polygon that satisfy quantitative constraints, i.e., the value of the quantitative measure is greater than a predetermined positive constant. The approach has potential application in grasp planning with multiple optimality criteria.

The general algorithm for manufacturability analysis of 5-axis sculptured surface machining is presented. The visibility cone which represents the aggregate of all visible directions is constructed to describe geometric constraints. Combined with the convex hull computation algorithm, a detailed procedure for computing the visibility cone has been developed. Through the geometric constraint analysis, the manufacturability for sculptured surface machining is evaluated so that geometric defects and inefficiencies can be inspected and corrected at the earlier design stages. Hence reworks in product development can be reduced or even avoided and the concurrence of sculptured surface design and manufacturing can be enhanced.

This paper presents two neural network approaches to minimum infinity-norm solution of the velocity inverse kinematics problem for redundant robots. Three recurrent neural networks are applied for determining a joint velocity vector with its maximum absolute value component being minimal among all possible joint velocity vectors corresponding to the desired end-effector velocity. In each proposed neural network approach, two cooperating recurrent neural networks are used. The first approach employs two Tank-Hopfield networks for linear programming. The second approach employs two two-layer recurrent neural networks for quadratic programming and linear programming, respectively. Both the minimal 2-norm and infinity-norm of joint velocity vector can be obtained from the output of the recurrent neural networks. Simulation results demonstrate that the proposed approaches are effective with the second approach being better in terms of accuracy and optimality.

This paper presents a neural-network based computational scheme for redundancy resolution of manipulators. The Tank-Hopfield (TH) network is adopted for pseudoinverse and inverse kinematics calculations and it can provide joint velocity and joint acceleration solutions within a time frame of the order of hundred nanoseconds. The proposed TH network consists of a left-hand network and a right-hand network. The connection weights of the network can be directly obtained from the known matrix J (Jacobian matrix) and vector X/spl dot/ (end-effector velocity) or vector X/spl uml/ (end-effector acceleration). Incorporating the TH network into the redundancy resolution scheme allows planning algorithms to be implemented in real time. Simulation results for a 3-link planar manipulator are presented to demonstrate that the proposed approach is efficient and practical.