BACKGROUND: Since the advent of laparoscopic hysterectomy, several studies have described artefacts, such as vascular pseudoinvasion, constituting potential pitfalls in the histological evaluation of these specimens. The use of an intrauterine manipulator is often suggested as the factor creating these artefacts.
OBJECTIVE: To describe possible artefacts, such as vascular pseudoinvasion, myometrial clefts, and tumor cells in the lumen of the cervix, on the serosa, and in the tubal lumen, and to correlate them with clinical and pathological characteristics.
METHODS: This is a retrospective monocentric study of 60 patients having been treated for benign (n = 27, 45%) or malignant (n = 33, 55%) uterine pathologies.
RESULTS: Vascular pseudoinvasion was found in 13 (22%) adenocarcinomas and in one (2%) benign uterine pathology. Clefts within the myometrium were observed in 16 (27%) uteri. Cells in the tubal lumen were observed in six (10%) hysterectomies. True vascular emboli were not correlated with the use of an intrauterine manipulator (p = 0.47) or the type of surgery (p = 0.21). Vascular pseudoinvasion was correlated with the presence of tumor cells in the lumen of the cervix (p = 0.013) and the presence of clefts in the myometrium (p < 0.001), but not with the other factors studied.
CONCLUSIONS: Overall, in our series, we did not observe any statistical association between the use of an intrauterine manipulator and the presence of true emboli or vascular pseudoinvasion during hysterectomy in women with malignant or benign uterine pathologies. Vascular pseudoinvasion was also associated with the presence of other artefacts.

In this paper, a practical discrete-time control method with adaptive image feature prediction for the image-based visual servoing (IBVS) scheme is presented. In the discrete-time IBVS inner-loop/outer-loop control architecture, the time delay caused by image capture and computation is noticed. Considering the dynamic characteristics of a 6-DOF manipulator velocity input system, we propose a linear dynamic model to describe the motion of a robot end effector. Furthermore, for better estimation of image features and smoothing of the robot\'s velocity input, we propose an adaptive image feature prediction method that employs past image feature data and real robot velocity data to adopt the prediction parameters. The experimental results on a 6-DOF robotic arm demonstrate that the proposed method can ensure system stability and accelerate system convergence.

It is worthwhile to calculate the execution cost of a manipulator for selecting a planning algorithm to generate trajectories, especially for an agricultural robot. Although there are various off-the-shelf trajectory planning methods, such as pursuing the shortest stroke or the smallest time cost, they often do not consider factors synthetically. This paper uses the state-of-the-art Python version of the Robotics Toolbox for manipulator trajectory planning instead of the traditional D-H method. We propose a cost function with mass, iteration, and residual to assess the effort of a manipulator. We realized three inverse kinematics methods (NR, GN, and LM with variants) and verified our cost function\'s feasibility and effectiveness. Furthermore, we compared it with state-of-the-art methods such as Double A* and MoveIt. Results show that our method is valid and stable. Moreover, we applied LM (Chan λ = 0.1) in mobile operation on our agricultural robot platform.

Origami structures have made significant contributions to the field of robotics, offering various advantages. One such advantage is their ability to conserve space by transforming the structure into a compact form. Additionally, many origami structures can be fabricated in a flat state to simplify manufacturing, giving them the potential for large-scale and cost-effective production. Rotational joints play a crucial role in the construction of robotic systems, yet origami rotational joints can suffer from a limited range of motion. We previously theoretically proposed the Self-Lock Joint to address this issue, but it is only partially flat-foldable. This paper presents a novel approach to the 3D printing of modular origami joints, such as the Self-Lock Joint, using 3D-printed plates joined with a fabric layer. The compliance of the fabric can improve the joint\'s semi flat-foldability or even enable it to achieve complete flat-foldability. Furthermore, the rotational motion of the joint is enhanced, allowing for close to 360 degrees of rotational movement. We assess the physical properties of the joint under both loaded and unloaded conditions in order to identify design trade-offs in the physical properties of the joints. Moreover, as a proof of concept, we construct and demonstrate manipulators utilizing these joints. The increase in rotational movement enabled by this fabrication method, coupled with the compliant joint\'s flat-foldability and modular nature, make it a promising candidate for use in a wide range of applications.

Motion error and wear growth are two crucial factors that determine the performance of the manipulator system. The two factors are distinct from the sensitivity to time and respectively indicate its control performance and lifespan level. In this paper, a double-time-scale non-probabilistic reliability (DTSNPR)-based optimization method, which considers time-sensitive factor motion error as time-dependent reliability (TDR) and time-insensitive factor clearance wear growth as time-independent reliability (TIR), is proposed to comprehensively evaluate and optimize the controller of the manipulator system in consideration of these multi-scale factors. Meanwhile, for a highly nonlinear response problem of a manipulator system, the adaptive subinterval collocation method (ASICM) which transfers the highly nonlinear uncertainty propagation problem into a sequence of small subintervals, is adopted to obtain the response range more precisely. The proposed DTSNPR-based optimization method is applied to three numerical manipulator systems and ensures each of them under a predefined level of reliability upon both motion error and wear growth. The results also indicate that the proposed ASICM owns an advantage over computational cost and precision, with only 0.4% error and 1% computational cost compared with the Monte-Carlo methods.

This paper is concerned with the issue of path optimization for manipulators in multi-obstacle environments. Aimed at overcoming the deficiencies of the sampling-based path planning algorithm with high path curvature and low safety margin, a path optimization method, named NA-OR, is proposed for manipulators, where the NA (node attraction) and OR (obstacle repulsion) functions are developed to refine the path by iterations. In the iterations of path optimization, the node attraction function is designed to pull the path nodes toward the center of their neighbor nodes, thereby reducing the path curvature and improving the smoothness. Also, the obstacle repulsion function is developed to push the path nodes out of the potentially unsafe region by generating a repulsive torque on the path nodes, thus improving the safety margin of the motion. By introducing the effect of NA-OR, the optimized path has a significant improvement in path curvature and safety margin compared with the initial path planned by Bi-RRT, which meaningfully enhances the operation ability of manipulators for the applications that give a strong emphasis on security. Experimental results on a 6-DOF manipulator in 4 scenarios demonstrate the effectiveness and superiority of the proposed method in terms of the path cost, safety margin, and path smoothness.

Soft actuators that execute diverse motions have recently been proposed to improve the usability of soft robots. Nature-inspired actuators, in particular, are emerging as a means of accomplishing efficient motions based on the flexibility of natural creatures. In this research, we present an actuator capable of executing multi-degree-of-freedom motions that mimics the movement of an elephant\'s trunk. Shape memory alloys (SMAs) that actively react to external stimuli were integrated into actuators constructed of soft polymers to imitate the flexible body and muscles of an elephant\'s trunk. The amount of electrical current provided to each SMA was adjusted for each channel to achieve the curving motion of the elephant\'s trunk, and the deformation characteristics were observed by varying the quantity of current supplied to each SMA. It was feasible to stably lift and lower a cup filled with water by using the operation of wrapping and lifting objects, as well as effectively performing the lifting task of surrounding household items of varying weights and forms. The designed actuator is a soft gripper that incorporates a flexible polymer and an SMA to imitate the flexible and efficient gripping action of an elephant trunk, and its fundamental technology is expected to be used as a safety-enhancing gripper that requires environmental adaptation.

This paper describes the design, construction and experimental testing of a single-joint manipulator arm actuated by pneumatic artificial muscles (PAMs) for the tasks of transporting and sorting work pieces. An antagonistic muscle pair is used in a rotational sense to produce a required torque on a pulley. The concept, operating principle and elementary properties of pneumatic muscle actuators are explained. Different conceptions of the system realizations are analyzed using the morphological-matrix conceptual design framework and top-rated solution was practically realized. A simplified, control-oriented mathematical model of the manipulator arm driven by PAMs and controlled with a proportional control valve is derived. The model is then used for a controller design process. Fluidic muscles have great potential for industrial applications and assembly automation to actuate new types of robots and manipulators. Their characteristics, such as compactness, high strength, high power-to-weight ratio, inherent safety and simplicity, are worthy features for advanced manipulation systems. The experiments were carried out on a practically realized manipulator actuated by a pair of muscle actuators set into an antagonistic configuration. The setup also includes an original solution for the subsystem to add work pieces in the working space of the manipulator.

Controlling the manipulator is a big challenge due to its hysteresis, deadzone, saturation, and the disturbances of actuators. This study proposes a hybrid state/disturbance observer-based multiple-constraint control mechanism to address this difficulty. It first proposes a hybrid state/disturbance observer to simultaneously estimate the unmeasurable states and external disturbances. Based on this, a barrier Lyapunov function is proposed and implemented to handle output saturation constraints, and a back-stepping control method is developed to provide sufficient control performance under multiple constraints. Furthermore, the stability of the proposed controller is analyzed and proved. Finally, simulations and experiments are carried out on a 2-DOF and 6-DOF robot, respectively. The results show that the proposed control method can effectively achieve the desired control performance. Compared with several commonly used control methods and intelligent control methods, the proposed method shows superiority. Experiments on a 6-DOF robot verify that the proposed method has good tracking performance for all joints and does not violate constraints.

Robot hands play an important role in the interaction between robots and the environment, and the precision and complexity of their tasks in work production are becoming higher and higher. However, because the traditional manipulator has too many driving components, complex control, and a lack of versatility, it is difficult to solve the contradiction between the degrees of freedom, weight, flexibility, and grasping ability. The existing manipulator has difficulty meeting the diversified requirements of a simple structure, a large grasping force, and the ability to automatically adapt to shape when grasping an object. To solve this problem, we designed a kind of underactuated manipulator with a simple structure and strong generality based on the metamorphic mechanism principle. First, the mechanism of the manipulator was designed on the basis of the metamorphic mechanism principle, and a kinematics analysis was carried out. Then, the genetic algorithm was used to optimize the size parameters of the manipulator finger structure. Finally, for different shapes of objects, the design of the control circuit binding force feedback control was carried out with a grasping experiment. The experimental results show that the manipulator has simple control and can grasp objects of different sizes, positions, and shapes.