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In this paper, a robotic system based on intraoperative 2D fluoroscopy navigation in which fluoroscopy images are obtained by C-arm popularized in the clinic, for spine surgery is proposed. The procedure of robot-assisted spine surgery is designed and described. An improved special 6-dof hybrid robot considering both the stiffness and workspace is proposed. And the kinematic model and jacobian matrix of robot are established. To realize fluroscopy images acquisition and avoid interfering with the patient during the process of positioning, human-robot cooperation based on fuzzy variable admittance control considering arm stiffness which would influence the stability of robot, is applied in robotic system. In addition, navigation techniques containing a novel bi-planar design, a robust circular control point detector and closed-loop positioning of tool, based on fluoroscopy images are proposed. In experiments, kinematic model of hybrid robot is test based on laser tracker and the distance error is 0.17 mm which is much smaller than the motion range. The effect of tool weight compensation is measured and the max mean force and torque errors are 0.7477N and 0.0617 N.M. The experimental results could meet requirements in human-robot cooperation. Furthermore, we test the the robotic system proposed in this paper on spine model bone and cadaver with the help of surgeons. The experimental results are validated by 2D image c-arm-based and 3D image CT-based and considered clinically acceptable.

For robotic-assisted surgery, the preoperative preparation procedure within which the surgical arms need to be adjusted manually to their expected configuration can pose interlocking problems in terms of accuracy, system robustness, and human-robot interaction experience. Previous work about variable impedance/admittance control methods did improve the smoothness of this procedure while individual characteristics of different operating personnel haven't yet been adequately considered. To further improve this process on the basis of existing methods, a novel strategy based on fuzzy Sarsa(λ)-learning algorithm is both proposed and incorporated into the virtual parameter adjustment strategy to achieve physical human-robot interaction (pHRI) so that the robot can acclimatize various handling characteristics of different operators through enough online learning. To also shorten the online training cycle, reduce the undesirable subjective factors and improve the overall training performance, a particle-swarm-optimization-based (PSO-based) algorithm is as well employed for optimizing the partition of state variable space and the distribution of discrete actions. Several groups of experiments have demonstrated the validity of this scheme and the effectiveness of the PSO optimization element.

Serve as the human-robot interface of laparoscopic surgical robot, the master device always plays a crucial role in terms of master-slave manipulation. Growing demands of robot-assisted surgery system also warrant more rational design and optimization for the master manipulator mechanism, which turn out to be important for improving the performance of surgical operations. In this paper, a novel 9 degrees of freedom (DOFs) haptic master manipulator applied to laparoscopic surgical robots is proposed. First of all, the mechanical configuration of the serial master manipulator is presented along with its corresponding kinematic analysis. The proposed strategy can decouple the posture and position completely and to a certain extent simplify the kinematics calculations. Then a mechanism optimization index which synthesizes the global kinematic performances, the global positioning accuracy and the structure length utilization of the manipulator mechanism is introduced. Finally, an improved particle swarm optimization algorithm is proposed to find the optimal mechanism design parameters. The optimization index and algorithm are verified by comparing the optimized parameters with the initial settings. Theoretical analysis and optimization results have demonstrated that the master manipulator can achieve better kinematic performances while maintaining 6 dimensions force feedback.

This paper presents a novel Gaussian Processes - Peak Suppression Particle Filter (GP-PSPF) method with adaptive weighting corrections, so as to identify sources in the multi-modal radiation field under some tough conditions, e.g. spatially sparse measurements and sources with large strength differences. As the radiation cumulative effect and ambiguous source number, most existing methods fail to localize the hotspots clustered in narrow regions, and PSPF scheme overcomes these difficulties through multi-layer structure and peak-suppressed correction. In contrast to our earlier work, the proposed algorithm mainly focuses on more severe and practicable conditions, as well as accuracy and robustness improvement. Firstly measurement biases are adopted as the correction feedback through Gaussian Processes technique, and then strength deviation for each particle can be inferred and utilized in two dynamic modules. The dynamic peak-suppressed correction is implemented to achieve more accurate estimations, while the location correction focuses on the solution of location dilemmas, consisting of redundant source identification and less swarm clustering. In addition, scaling adaptation policy and sequential swarm reordering are specially conceived and developed for more stable and accurate optimization. Finally, extensive simulations and physical experiment are conducted under above-mentioned intractable situations, validating the accuracy improvement and practical effectiveness of the algorithm.

The continuum manipulators, which have gradually become a research hotspot of minimally invasive surgery have the characteristics of small size, lightweight, and better environmental adaptability. In this paper, a two degrees of freedom continuum manipulator with notches is proposed. The kinematic and mechanical models of the notch continuum manipulator are coupled. The output driving force of the motor is needed to control the manipulator. Due to the drift of the current of the motor, the accuracy of the control is low. In order to solve the above problem, the constant curvature assumption is realized by changing the structural parameters of the notches. The continuum manipulator mechanics model is established through the analysis of the continuum manipulator structure. The calculation of the deformation angle of a single notch of the manipulator is carried out. The overall deformation of the manipulator is derived through iterative algorithms. The stiffness of the continuum manipulator, which can affect the deformation of the continuum manipulator is modified by changing the structural parameters. In this way, the problem of the non-constant curvature deformation under the uniform notch of the continuum manipulator is improved. And the effectiveness of the variable stiffness model is verified by simulation experiments.

In this paper, we propose a robust circular control point detector for bi-planar spine surgery navigation system. First, this detector divides the regions into three cases, including real control points, possible control points, and false control points, based on support vector machine in which quantity of edge pixels, closed-loop attribute, and distance criterion are selected as the features. Next, the valid control points are extracted from possible control points based on improved HVCD (Horizontal and vertical search for circle detection) method, which has improved minor-radius circles detection in two ways: one is parameter adjustment for search principle and the other is the promotion of the edge segment detector. Finally, the effective control points could be obtained and classified into two classifications by using the distance & density clustering algorithm. Experimental results demonstrate that both detectors proposed in this paper and EDCircles could obtain effective control points on images of spine model bone while the detector proposed in this paper is obviously superior to EDCircles on images of swine bone and swine which have strong background interference. In addition, the parameters used in this paper are fixed for all the images.

In this paper, a class of singular phenomenon is first found for the existing closed-form solutions of the hand-eye calibration problem with the form AiX = XBi when the angles corresponding to rotational parts of Ai and Bi are near or equal to π radian. A universal observability index is put forward to detect when this singularity would undoubtedly occur. For avoiding this singularity, a novel analytical solution based on a new cost function is proposed to estimate the hand-eye matrix in the presence of measurement errors. Simulation and experimental results can illustrate the feasibility and benefits of the proposed observability index and the singular-free closed-form solution. In addition, the other kind of singular phenomenon is also discovered for the existing closed-form solution, where the orientation of the unknown hand-eye matrix is parameterized by modified Rodrigues parameters. Therefore, in order to obtain the non-singular analytical solution based on modified Rodrigues parameters, a novel additional rotation theory is introduced and verified by the hand-eye calibration of a novel surgical robot.

In the grinding process of friction stir weld seams, excessive grinding will cause damage to the base metal and bring significant economic losses. In this paper, the authors design a robotic system for grinding the weld seam and present a monitoring method of excessive grinding. The designed system consists of an industrial robot, a line scanner for measuring the weld seam and a force-controlled grinding tool. Since the result of the measurement of the weld seam is a point cloud, the extraction method of the weld seam point cloud based on graph-cut is proposed in this paper. The extracted features are used as prior knowledge of the monitoring algorithm. On the other hand, by combining the features from the point cloud and force-position information during the processing, a monitoring method for excessive grinding based on PSO-SVM is proposed and verified by experiments. The experiments demonstrate that the proposed method can identify excessive grinding, and the accuracy of recognition is 91.5%.

Methods of point cloud registration based on ICP algorithm are always limited by convergence rate, which is related to initial guess. A good initial alignment transformation can sharply reduce convergence time and raise efficiency. In this paper, we propose a global registration method to estimate the initial alignment transformation based on HEALPix (Hierarchical Equal Area isoLatitude Pixelation of a sphere), an algorithm for spherical projections. We adopt EGI (Extended Gaussian Image) method to map the normals of the point cloud and estimate the transformation with optimized point correspondence. Cross-correlation method is used to search the best alignment results in consideration of the accuracy and robustness of the algorithm. The efficiency and accuracy of the proposed algorithm were verified with created model and real data from various sensors in comparison with similar methods.

This paper addresses a detection problem where sparse measurements are utilized to estimate the source parameters in a mixed multi-modal radiation field. As the limitation of dimensional scalability and the unimodal characteristic, most existing algorithms fail to detect the multi-point sources gathered in narrow regions, especially with no prior knowledge about intensity and source number. The proposed Peak Suppressed Particle Filter (PSPF) method utilizes a hybrid scheme of multi-layer particle filter, mean-shift clustering technique and peak suppression correction to solve the major challenges faced by current existing algorithms. Firstly, the algorithm realizes sequential estimation of multi-point sources in a cross-mixed radiation field by using particle filtering and suppressing intensity peak value, while existing algorithms could just identify single point or spatially separated point sources. Secondly, the number of radioactive sources could be determined in a non-parametric manner as the fact that invalid particle swarms would disperse automatically. In contrast, existing algorithms either require prior information or rely on expensive statistic estimation and comparison. Additionally, to improve the prediction stability and convergent performance, distance correction module and configuration maintenance machine are developed to sustain the multimodal prediction stability. Finally, simulations and physical experiments are carried out in aspects such as different noise level, non-parametric property, processing time and large-scale estimation, to validate the effectiveness and robustness of the PSPF algorithm.

Due to the narrow space and a harsh chemical environment in the sterilization processes for the end-effector of surgical robots, it is difficult to install and integrate suitable sensors for the purpose of effective and precise force control. This paper presents an innovative tension sensor for estimation of grasping force in our laparoscope surgical robot. The proposed sensor measures the tension of cable using fiber gratings (FBGs) which are pasted in the grooves on the inclined cantilevers of the sensor. By exploiting the stain measurement characteristics of FBGs, the small deformation of the inclined cantilevers caused by the cable tension can be measured. The working principle and the sensor model are analyzed. Based on the sensor model, the dimensions of the sensor are designed and optimized. A dedicated experimental setup is established to calibrate and test the sensor. The results of experiments for estimation the grasping force validate the sensor.

As one of the major methods for the diagnosis and treatment of cancers in their early stages, the percutaneous puncture technique has bright prospect in biopsy, ablation, proximity radiotherapy, and drug delivery. Recent years, researchers found the flexible needle cannot realize feedback control during the puncture surgeries only by path planning. To solve this problem, the flexible needle is tried to achieve real-time detection in this paper. Compared with previous methods, the strain gauges glued on the needle surface rather than the medical imaging techniques is used to collect the information to reconstruct the needle curve, which is benefit to integrate the whole system and obtain a more simple and accurate closed-loop control. This paper presented the math model of curve fitting and analyzed the causes of curve fitting errors. To verify the feasibility of this method, an experiment setup was built. Results from the experiments validated the solution in this paper to be effective.

A friction–inertial-based rotary motor driven by shear piezoelectric actuators (SPAs) is proposed in this paper, which possesses many superior features, including high resolution, compact size, large load-capacity, and low cost. In order to eliminate the step loss and increase the step size when an external load is applied, the power-function-shape driving signal was used to actuate the rotary motor. According to the step characteristics under this driving signal, two motion modes were observed and defined, namely the stick-shoot motion mode and the stick-slip-shoot motion mode. The former motion mode can realize a large step size while the later one cannot due to the slipping during the rising phase. After analyzing the results from the numerical simulation and the experiment study, it was found that the motion performance of the motor is closely related to the preload and the base number of the driving signal rather than the size of SPAs, which means the motor can be further downsized according to its actual requirements.

To achieve ideal force control of a functional autonomous exoskeleton, sensitivity amplification control is widely used in human strength augmentation applications. The original sensitivity amplification control aims to increase the closed-loop control system sensitivity based on positive feedback without any sensors between the pilot and the exoskeleton. Thus, the measurement system can be greatly simplified. Nevertheless, the controller lacks the ability to reject disturbance and has little robustness to the variation of the parameters. Consequently, a relatively precise dynamic model of the exoskeleton system is desired. Moreover, the human-robot interaction (HRI) cannot be interpreted merely as a particular part of the driven torque quantitatively. Therefore, a novel control methodology, so-called probabilistic sensitivity amplification control, is presented in this paper. The innovation of the proposed control algorithm is two-fold: distributed hidden-state identification based on sensor observations and evolving learning of sensitivity factors for the purpose of dealing with the variational HRI. Compared to the other state-of-the-art algorithms, we verify the feasibility of the probabilistic sensitivity amplification control with several experiments, i.e., distributed identification model learning and walking with a human subject. The experimental result shows potential application feasibility.

To achieve strength augmentation, endurance enhancement, and human assistance in a functional autonomous exoskeleton, control precision, back drivability, low output impedance, and mechanical compactness are desired. In our previous work, two elastic modules were designed for human–robot interaction sensing and compliant control, respectively. According to the intrinsic sensing properties of the elastic module, in this paper, only one compact elastic module is applied to realize both purposes. Thus, the corresponding control strategy is required and evolving internal model control is proposed to address this issue. Moreover, the input signal to the controller is derived from the deflection of the compact elastic module. The human–robot interaction is considered as the disturbance which is approximated by the output error between the exoskeleton control plant and evolving forward learning model. Finally, to verify our proposed control scheme, several experiments are conducted with our robotic exoskeleton system. The experiment shows a satisfying result and promising application feasibility.

This paper presents a new approach to generate the workspace of planar redundant manipulators based on the workspace density of a single link. The workspace density of planar serial redundant manipulators can be computed efficiently by using the concept of the Fourier transform for the group of rigid-body motions of the plane and the convolution theorem. For manipulators using discrete actuation, it is important to study not only the shape of the workspace, but also the density of reachable poses in the workspace. In this paper, we focus on using the workspace density to calculate collision-free paths in complex environments with multiple obstacles in the workspace of the manipulator. This paper presents the algorithm and demonstrates it effectiveness using simulation results.

Based on the proposed end-effector structure of a laparoscopic minimally invasive surgical manipulator, a dimensional optimization method is investigated to enlarge the motion range of the mechanical arm in the specific target area and reduce the collision among the mechanical arms simultaneously. Both the length of the kinematics links and the overall size of the integrated system are considered in the optimization process. The NSGA-II algorithm oriented to the multi-objective optimization is utilized to calculate the Pareto solution set of the objective function. Finally, the dependence of the evaluation indexes is analysed to filter the non-inferior set, which guarantees the selection of the optimization solution.

The tendon driven mechanism using a cable and pulley to transmit power is adopted by many surgical robots. However, backlash hysteresis objectively exists in cable-pulley mechanisms, and this nonlinear problem is a great challenge in precise position control during the surgical procedure. Previous studies mainly focused on the transmission characteristics of the cable-driven system and constructed transmission models under particular assumptions to solve nonlinear problems. However, these approaches are limited because the modeling process is complex and the transmission models lack general applicability. This paper presents a novel position compensation control scheme to reduce the impact of backlash hysteresis on the positioning accuracy of surgical robots’ end-effectors. In this paper, a position compensation scheme using a support vector machine based on feedforward control is presented to reduce the position tracking error. To validate the proposed approach, experimental validations are conducted on our cable-pulley system and comparative experiments are carried out. The results show remarkable improvements in the performance of reducing the positioning error for the use of the proposed scheme.

Due to the urgent need for high precision surgical equipment for minimally invasive spinal surgery, a novel robot-assistant system was developed for the accurate placement of pedicle screws in lumbar spinal surgeries. The structure of the robot was based on a macro-micro mechanism, which includes a serial mechanism (macro part) and a bi-planar 5R parallel mechanism (micro part). The macro part was used to achieve a large workspace, while the micro part was used to obtain high stiffness and accuracy. Based on the transfer function of dimension errors, the factors affecting the accuracy of the end effectors were analyzed. Then the manufacturing errors and joint angle error on the position-stance of the end effectors were investigated. Eventually, the mechanism of the strain energy produced by the deformation of linkage via forced assembly and displacements of the output point were calculated. The amount of the transfer errors was quantitatively analyzed by the simulation. Experimental tests show that the error of the bi-planar 5R mechanism can be controlled no more than 1 mm for translation and 1° for rotation, which satisfies the required absolute position accuracy of the robot.