During the continuous curvilinear capsulorhexis (CCC) procedure in cataract surgery, an ideally circular and centered capsulorhexis is contributed to the capsular edge perfectly overlapping the intraocular lens (IOL). The anterior lens capsule, as a typical soft material, controlling the propagation of its crack is a critical step in surgery. In this study, to investigate the motion trajectory of the capsulorhexis under a well-centered opening, a mathematical model is proposed to predict the two-dimensional (2D) motion trajectory of the clamping point of the forceps. To analyze the crack propagation, the crack length ratio is defined, and these ratios are obtained for both porcine and human capsules under uniaxial tension and CCC conditions. The mean values for the porcine and human capsules are 1.18 and 1.13 under uniaxial tension, and 1.20 and 1.03 under CCC, respectively. To further refine the theoretical model, we conducted simulated capsule tearing experiments using our ophthalmic robotic platform based on the ex vivo data from the porcine lens capsule and made necessary adjustments. The modified model is then utilized for capsulotomy, and results reveal a diameter error of 8% and a centrality of 0.9 for the torn lens anterior capsule, which validates the feasibility of the proposed trajectory model. Overall, our study provides valuable insights into robot-assisted automated capsulorhexis for cataract surgery.

Robot-assisted membrane peeling provides several benefits including improvement of manipulation precision and safety. A forceps which is capable of sensing peeling force, scleral force, and insertion depth is the basis of developing robot control method. But such forceps still lacks. To relieve these challenges, a multi-function sensing forceps based on fiber bragg gratings (FBG) is proposed. Six FBG sensors are divided into three groups and attached along the grooves of a 23G forceps. The first FBG group aims to measure the peeling force, while the other two FBG groups measure the scleral force and insertion depth. The calculation algorithm is built. Both linear calibration method and radial basis function (RBF) neural network calibration method are proposed to improve the sensing accuracy. Experimental results show that the precision of the RBF neural network is approximately 76.28% higher than that of linear calibration method. Verification experiments results show that the average root mean square error of the proposed forceps are 0.28 mN (peeling force), 0.37 mN (scleral force), and 0.29 mm (insertion depth), respectively. The tested sensing range are 30 mN (peeling force), 100 mN (scleral force), 10 mm (insertion depth), respectively.

Retinal vein cannulation involves puncturing an occluded vessel on the micron scale. Even single millinewton force can cause permanent damage. An ophthalmic robot with a piezo-driven injector is precise enough to perform this delicate procedure, but the uncertain viscoelastic characteristics of the vessel make it difficult to achieve the desired contact force without harming the retina. The paper utilizes a viscoelastic contact model to explain the mechanical characteristics of retinal blood vessels to address this issue. The uncertainty in the viscoelastic properties is considered an internal disturbance of the contact model, and an active disturbance rejection controller is then proposed to precisely control the contact force. The experimental results show that this method can precisely adjust the contact force at the millinewton level even when the viscoelastic parameters vary significantly (up to 403.8%). The root mean square (RMS) and maximum value of steady-state error are 0.32 mN and 0.41 mN. The response time is below 2.51 s with no obvious overshoot.

Continuous curvilinear capsulorhexis (CCC) is a delicate operation that may benefit from robot technology. This paper introduces a new hybrid cataract surgery robot that consists of four parallel prismatic pairs connected in series to a "Rotation-Prismatic" unit. Additionally, the paper proposes a master-slave control strategy and a virtual remote center of motion (RCM) algorithm. The virtual RCM is based on the kinematic model of the robot and enables the setting of an arbitrary point on the forceps as the RCM point. Finally, the effectiveness of the proposed robot is verified through experimental evaluations on ex-vivo pig eyeballs.

The loose connection of bolts on high-voltage transmission towers can adversely affect the regular operation and safety of the power system. Existing methods for bolt looseness detection suffered from inefficiency and missed detection. To address these issues and achieve high-precision real-time bolt looseness detection, this paper proposes a method based on binocular vision and neural networks. After capturing images of the bolts using a binocular camera, a Fast Region-based Convolutional Neural Network (RCNN) is utilized to locate the bolt connections. Then, adaptive threshold segmentation and Hough transform are employed to obtain the contours and corners of the hexagonal nuts. The obtained corners are used for stereo matching and 3D reconstruction, enabling the calculation of the distance between the nuts and the bolts ends to determine the looseness. The proposed method achieves a distance measurement error of 1.1%, an accuracy rate of 98.5% for looseness detection, and an average processing time of only 0.41s, thus realizing high-precision and fast detection.

Purpose Robot assistance in membrane peeling may improve precision and dexterity or prevent complications by task automation. To design robotic devices, surgical instruments’ velocity, acceptable position/pose error, and load ability need to be precisely quantified. Methods A fiber Bragg grating and inertial sensors are attached to forceps. Data collected from forceps and microscope images are used to quantify a surgeon's hand motion (tremor, velocity, posture perturbation) and operation force (voluntary and involuntary) in inner limiting membrane peeling. All peeling attempts are performed on rabbit eyes in vivo by expert surgeons. Results The root mean square (RMS) of the tremor amplitude is 20.14 µm (transverse, X), 23.99 µm (transverse, Y), and 11.68 µm (axial, Z). The RMS posture perturbation is 0.43° (around X), 0.74° (around Y), and 0.46° (around Z). The RMS angular velocities are 1.74°/s (around X), 1.66°/s (around Y), and 1.46°/s (around Z), whereas the RMS velocities are 1.05 mm/s (transverse) and 1.44 mm/s (axial). The RMS force is 7.39 mN (voluntary force), 7.41 mN (operation force), and 0.5 mN (involuntary force). Conclusions Hand motion and operation force are measured in membrane peeling. These parameters provide a potential baseline for determining a surgical robot's accuracy, velocity, and load capacity. Translational Relevance Baseline data are obtained that can be used to guide ophthalmic robot design/evaluation.

Continuous curvilinear capsulorhexis (CCC) requires surgeons to manipulate fragile eye tissue at the microscale. The limited perceptual accuracy of surgeons makes it difficult to precisely position the forceps. Robot technology provides a feasible way to improve the performance of CCC.

针对直角坐标机器人在动态分拣过程中顺序规划算法效率低下的问题,提出了一种适用于机器人连续分拣作业的改进贪心策略规划算法.建立直角坐标机器人的运动学模型,确保物体被准确拾取.设计时间窗口,对传送带上的连续运动物体进行区域划分,并应用贪心策略对同一时间窗口内的物体规划分拣顺序.考虑物体存在分拣遗漏的风险,设计评价函数对贪心策略进行改进,增强了所提算法的实用性.设计模拟程序对所提算法进行仿真,并利用搭建的机器人平台开展分拣实验,验证了算法的可行性和有效性.实验表明:所提算法可在机器人实际分拣作业中规划出有效的分拣路径,平均分拣距离和分拣时间均小于顺序规划算法,提高了机器人对平面随机分布的连续运动物体的分拣效率,实时性好,实用性强,对机器人动态分拣场景下的分拣路径优化研究具有一定的指导意义.

：To realize RCM (remote center of motion) motion mode required for robot-assisted intraocular surgeries, a novel six-degree-of-freedom series-parallel hybrid mechanism (RP+PPRRP/PPRR) is proposed.Firstly, inspired by the structure of U pair (Universal pair), the parallel configuration (PPRRP/PPRR) is reconstructed by two sets of planar five-bar mechanism (RPPRP) units.After calculating the DOFs (degrees of freedom) and analyzing their characteristics based on screw theory, the capability of RCM motion mode for this mechanism is confirmed.Subsequently, the forward kinematics model and the inverse kinematics model of this mechanism containing RCM parameters are established through coordinate transformation method.Then, according to the configuration of the mechanism, a physical prototype is designed and established.Finally, based on master-slave control method, a motion experiment is performed to verify the correctness of the kinematic model and the adaptability of this mechanism to intraocular surgeries.

为解决传统的基于三点法的质心测量系统无法应用于带翼展飞行器的问题,提出了一种基于三点方式的任意旋转角质心测量法.为了提高系统测量精度,采用响应面法分析多种随机误差对系统测量精度的综合影响.首先,构建了带翼展飞行器的质量质心测量系统,然后利用随机误差传递公式得到各个随机误差与系统测量精度之间的关系式,并使用响应面法和拉丁超立方抽样法得到多种随机误差与系统测量精度之间的二次项关系式模型,进而依据二次项关系式模型和系统精度指标得到各元器件的精度要求,并分析了满足系统测量误差的旋转角度范围.最后对200 kg、400 kg、800 kg三种质量级别的待测带翼展飞行器在不同旋转角度下进行了多次测量,并将响应面计算结果与理论值进行了对比.对比结果表明,质心测量精度满足系统精度要求,从而验证了任意旋转角度下该测量方法的有效性,以及随机误差与系统测量精度之间的二次项关系式模型的正确性.

Continuous curvilinear capsulorhexis (CCC) is a delicate ophthalmic procedure that may benefit from robot technology. In CCC, surgeons make an incision in the cornea, insert forceps into the anterior segment through the incision, and peel the anterior lens capsule (ALC) from the lens. The absence of perception of axial peeling force applied to ALC is challenging in developing robot-assisted CCC. A potential way to deal with this challenge is measuring axial force with a mercantile force sensor, which has the advantage of sub-millinewton precision, high axial stiffness, high stability, and ease of use. However, the measured results of the mercantile force sensor contain the axial peeling force and forceps-cornea friction. Therefore, we seek to estimate the friction at the incision with the distributed LuGre friction model. The normal force and friction at incision are measured by fiber bragg grating (FBG) sensors and a mercantile force sensor, respectively. Then, the parameters of the LuGre model are identified by applying a low-frequency sinusoidal movement on the forceps. An identification experiment is performed on ex-vivo pig cornea samples. Finally, friction during inserting forceps into the anterior segment of ex-vivo pig eyes is estimated. The root mean square error of friction identification and estimation are 0.59 mN and 1.59 mN, respectively.

Capsulotomy in cataract surgery is delicate and susceptible to surgeon fatigue, limited perception accuracy, and physiological hand tremors. To aid cataract surgery, we propose a novel vision-guided hybrid robot system. The robot, consisting of a PPRR/PPRRP parallel unit and an RP serial unit (R: rotation pair, P: prismatic pair), adopts a parallel configuration as the base. Capsulotomy has high requirements for position accuracy, thus, a robot control algorithm with the RCM constraint is proposed. This algorithm reduces the position error of the robot by limiting the position error within tolerance, thereby increasing the motion accuracy and smoothing the trajectory. Instead of obtaining position information based on the visual perception of the surgeons, we propose an edge detection method based on the U-Net to provide visual feedback to the robot.Experimental validations on a standard circle and ex-vivo pig eyeballs are performed to verify the effectiveness of the proposed robot control algorithm with the RCM constraint and the accuracy of the proposed robot system. When a circular trajectory of 5 mm is tracked, the maximum position error of the actuator end point is 0.084 mm and that of the RCM point is 0.292 mm. When the edge of the anterior capsule is tracked, the mean tracking error of the robot is 0.129 mm and that of the doctor is 0.764 mm.

基于广义Maxwell黏弹性模型建立手术器械与眼组织之间的黏弹性接触模型,并通过力松弛实验对接触模型的参数进行辨识.进而在此基础上提出一种导纳控制方法,通过控制手术器械的位移实现对接触力的控制.该方法将传统的导纳控制器替换为比例-积分(PI)控制器,从而将黏弹性接触模型中的理想微分环节替换为1阶微分环节.替换后,在低频段,尤其是频率趋近于0时的幅频响应可得到提升并保持稳定,从而避免幅频响应过低导致的接触力衰减.通过对离体猪眼球进行力控制实验验证了该控制方法的有效性.实验结果表明,阶跃力平均误差约为4.6％,响应时间约为2.5 s,无明显超调,并可实现一定频率的正弦力输出.阶跃力输出精度可以满足机器人辅助眼科手术的要求.

A new material handling robot applied in production line must have collision-free motion planning and good kinematics performance. A motion planning method that can satisfy obstacle avoidance and kinematic requirements simultaneously is proposed to solve the problem. This method is conducted during trajectory parameterization, collision detection, fitness building, and particle swarm optimization (PSO). First, a finite number of intermediate nodes are used to construct a piecewise polynomial function as the parameterized trajectory in the joint space. Next, the collision detection between the obstacles and the gripper is executed through bounding box simplification and intersection test of geometric objects. A fitness function combining collision and kinematics performance is then defined by using weighted coefficient and penalty function methods. Finally, the motion planning problem is solved with PSO algorithm. This method has better obstacle avoidance flexibility, smoothness, small impact, and minimal time consumption during the motion process, and its effectiveness is verified through virtual prototype simulation.

将机器视觉应用于传送带分拣系统,使得分拣系统具有更高的适应性,能够适应各种任务下的分拣需求.对机器视觉在分拣平台应用上存在的标定问题进行了研究,完成了基于机器视觉的传送带分拣系统搭建.首先分析了视觉系统的标定方法,然后提出了一种传送带与机器人的位姿关系的标定方法,最后在摄像机标定和传送带标定的基础上实现了机器人与摄像机的位姿标定.实验证明:工件的定位识别精度能够满足识别分拣的要求,能够完成机器人的分类抓取任务.