There are multiple ways for robots to advance, such as peristalsis, rolling, magnetic drive, and rear push. However, the thrust generated by chemical reactions can also drive objects to move. How to design a robot to generate thrust through chemical reactions to drive it is a challenging problem. This paper proposes a cavity motion robot driven by chemical reactions, multiple reactant delivery mechanisms, a method for controlling the direction of reaction forces, a reaction chamber of the robot, on-off valves, and a device for discharging reaction products. Analyze and derive the relationship between the kinetic energy of gases generated by chemical reactions, robot displacement, and reactant volume. The feasibility of the basic principle of the robot's movement was tested from an experimental perspective. This driving method provides a new type of movement mode for the motion robot in the pipeline. A new field of driving research has been opened up.

Accurate recognition of surgical phases is essential for clinical education and the advancement of robotic surgery. Although significant progress has been made in automated surgical phase recognition for various procedures, a notable research gap remains in the context of video-assisted thoracoscopic pulmonary lobectomy-a highly complex and technically demanding operation. To address this gap, this study is the first to comprehensively investigate automated phase recognition in thoracoscopic pulmonary lobectomy. Our principal contributions include: (1) the creation of the first novel, expert-annotated private video dataset for pulmonary lobectomy, providing a critical resource for this understudied surgical domain; and (2) the optimization and adaptation of the SV-RCNet model, which achieves robust phase recognition by effectively capturing both visual and temporal features. Experimental results demonstrate that our approach achieves a surgical phase recognition accuracy of 92.4%, comparable to the state-of-the-art performance in other surgical phase recognition tasks. This work not only fills a critical research gap, but also lays a solid foundation for the future development of computer-assisted thoracoscopic surgery, offering significant clinical and educational value for surgical training and the advancement of intelligent robotic systems.

The assessment of surgical skill is crucial for indicating a surgeon's proficiency. While motion analysis of surgical tools is widely used in endoscopic surgery, it is not commonly applied to open surgery. Instead, open surgery skill assessment relies on observing the trajectory of surgical tools on tissue. This observation-based method often lacks clear standards, leading to inaccurate assessments. This paper presents a method for evaluating cutting skill in open surgery through scalpel motion analysis. A 3D multiple-facet ArUco code cube is designed, and a dataset of tip coordinate system poses for various scalpels in the ArUco code coordinate system (ACS) is established using the pivot calibration method. The YOLOv8 model and an image dataset of different scalpels are used to identify the scalpel type and select its tip position. The tip position is then transformed from ACS to a binocular camera coordinate system (BCS), representing the incision curve made by the scalpel. Five assessment metrics are proposed to quantify the surgeon's cutting skill: average incision curvature deviation, incision length difference, incision endpoint deviation, average incision deviation, and average cutting jerk. Experiments involving twenty expert and novice surgeons performing four common incisions (straight line, polyline, semicircle, and cross line) demonstrate the metrics' effectiveness. The metrics provide a clear, objective display of individual cutting skills, and a combined ranking reveals comparative skill levels. This study offers a precise method for evaluating surgeons' cutting skills with a scalpel in open surgery.

Wire-driven robots have wide applications. The structure of the driver is simple but bulky, with too many motors and no tensioning mechanism. A driver for a dual-wire coaxial wire-driven robot is proposed. The principle is that the extension and contraction amounts of the mirror-symmetrically distributed driving wires are equal. Based on this principle, a wire-driven hyper-redundant robot and a continuum robot with a dual-wire coaxial driving method are designed, and their kinematic models are established and analyzed. Based on the design method, a hyper-redundant robot and a continuum robot with a dual-wire coaxial driver are fabricated, and their motion performance is tested. The test results show that the wire-driven robot based on the dual-wire coaxial driver conforms to the kinematic analysis, proving that the dual-wire coaxial driving method is correct and efficient.

In existing continuum robots, not every segment within a section is controlled, limiting the full utilization of their flexibility and making navigation through narrow and continuously curved spaces challenging. This paper introduces a single-section controllable continuum robot where each segment can be independently controlled via driving wires. Firstly, we designe the structure and connection method of the continuum robot segments. Secondly, we establish the kinematic model of the single-section controllable continuum robot, enabling independent control of each segment through the driving wires. Finally, we compares and tests the fitting accuracy of different configurations of continuum robots with controllable parts containing 1, 3, 5, and 7 segments in one section, regarding their ability to fit the same curve. The single-section controllable continuum robot can independently control the position of each joint within the continuum, demonstrating superior curve fitting capability and enabling navigation through narrow, continuously curved spaces.

The replacement of surgical tools is one of the basic operations in performing surgery. However, this step is time-consuming in operational processes, and elevates the associated risks. Reason: The revision enhances clarity and employs more formal language suitable for an academic context. At present, medical robots still need manual operation to replace surgical tools. In order to enable the robot to change surgical tools, this study designed a clamping tool holder for changing surgical tools. The clamping tool holder is driven by a 6-degree of freedom robotic arm. In this paper, CAD model of the clamping tool holder is presented, the motion of the clamping tool holder analysis and pick up the tools of simulation operation. Finally, the prototype experiment verified that the clamping tool holder can hold and release surgical tools without manual assistance. The structure can realize the effect of saving manpower and improving operation efficiency.

In neurosurgery, surgeons use single-function surgical tools such as aspirator and electric-coagulation cutters to deal with blood vessel bleeding. Compared with multi-functional surgical tools, the surgical efficiency of using single-function tools is lower. In this paper, a multiple functional integrated medical robotic end-effector which integrates CCD imaging, electric-coagulation and suction is designed. The end-effector can be installed at the end of the serial robotic arm. The CAD model of the end-effector was constructed, and the motion analysis is carried out. The end-effector is proved to be effective by three experiments. The operator can use the design to suction blood, spotting of bleeding spots, and electric-coagulation lesion. In operations, there is no need to change the surgical tools, improve the efficiency of the operation.

Hair direction is an important external feature of hair, and recognising hair direction is a prerequisite for processing hair. In this paper, a new algorithm is proposed and systematically verified experimentally for the problem of recognising hair direction. The main goal of this paper is to develop an algorithm that can identify hair direction in a complex image environment. A curve segment analysis method based on image skeletonisation is adopted, which is based on skeleton extraction, intersection identification, curve segmentation and direction prediction. In addition, this paper combines the technique of non-maximal value suppression and PCA analysis to improve the accuracy and stability of the estimation. In the experimental design, this paper chooses a representative image dataset to verify the effectiveness of this paper's algorithm. The experimental process includes the steps of image preprocessing, skeletonisation processing, intersection detection and merging, and direction prediction. The experimental results show that the method in this paper can accurately and effectively identify the hair direction. The main contribution of this paper is to propose a new hair direction recognition method and experimentally verify its effectiveness and accuracy in complex backgrounds.

Purpose Scalpels are typical tools used for cutting in surgery, and the surgical tray is one of the locations where the scalpel is present during surgery. However, there is no known method for the classification and segmentation of multiple types of scalpels. This paper presents a dataset of multiple types of scalpels and a classification and segmentation method that can be applied as a first step for validating segmentation of scalpels and further applications can include identifying scalpels from other tools in different clinical scenarios.Methods The proposed scalpel dataset contains 6400 images with labeled information of 10 types of scalpels, and a classification and segmentation model for multiple types of scalpels is obtained by training the dataset based on Mask R-CNN. The article concludes with an analysis and evaluation of the network performance, verifying the feasibility of the work.Results A multi-type scalpel dataset was established, and the classification and segmentation models of multi-type scalpel were obtained by training the Mask R-CNN. The average accuracy and average recall reached 94.19% and 96.61%, respec-tively, in the classification task and 93.30% and 95.14%, respectively, in the segmentation task. Conclusion The first scalpel dataset is created covering multiple types of scalpels. And the classification and segmentation of multiple types of scalpels are realized for the first time. This study achieves the classification and segmentation of scalpels in a surgical tray scene, providing a potential solution for scalpel recognition, localization and tracking.

All of oral surgeries are accompanied by bleeding. Manual removal of blood is carried out by a nurse and dozens of repetitive blood suction operations are implemented by the nurse during a single oral surgery. In our previous work, a robot for removal of blood-water mixed liquid is designed. However, the robot requires an initial order to autonomous removal of blood mixed liquid. In this paper, a hand gesture control method for typical operations of the robot is developed. Gesture dataset are developed at first and a deep learning neural network is trained for detection of hand gesture. The gesture control procedures for four typical operations are provided in detail. In a group of experiments, it’s demonstrated that an oral surgeon controls the robot’s motion by presenting prescribed hand gesture. The proposed method could be an alternative manner for blood mixed liquid removal.

Fluid removal and electrotome coagulation are routine procedures in neurosurgery operations, and the doctor’s remote control can reduce physical fatigue. In this paper, an integrated multi-functional surgical tool which can realize electrocoagulation and liquid aspiration is designed. At the same time, the multi-functional integrated surgical device is combined with the remote operation method to realize the remote operation of the instrument. This multifunctional instrument includes CCD-imaging, suction and electrical coagulation. The system can complete the remote operation function of the special design tool, and can isolate the doctor from the operation. The teleoperation of this instrument is integrated with the teleoperation of other functional instruments to realize the teleoperation of the complete surgical process.

Wound treatment is routine work in hospital and bandaging of wound happens numerous even in a hospital scale. However, manual bandaging is accompanied with potential infection and it is tedious and manpower-wasting besides. This paper proposed a customized bandaging mechatronic system for avoiding bacteria from hand contact. The design of the propose bandaging system is developed and the working procedure of the by the system is given step by step. To the best knowledge of the authors, the customized mechatronic system is the first bandaging robotic system for bandaging purpose. After loading a customize bandage package, the proposed system lays a solid foundation for automatous bandaging method without human intervention during bandaging.

Saliva blood mixed liquid (SBML) appears in oral surgery, such as scaling and root planning, and it affects surgical vision and causes discomfort to the patient. However, removing SBML, i.e. frequent aspiration of the mixed liquid, is a routine task involving heavy workload and interruption of oral surgery. Therefore, it is valuable to alternate the manual mode by autonomous robotic technique. The robotic system is designed consisting of an RGB-D camera, a manipulator, a disposable oral aspirator. An algorithm is developed for detection of SBML. Path planning method is also addressed for the distal end of the aspirator. A workflow for removing SBML is presented. 95% of the area of the SBML in the oral cavity was removed after liquid aspiration among a group of ten SBML aspiration experiments. This study provides the first result of the autonomous aspirating robot (AAR) for removing SBML in oral surgery, demonstrating that SBML can be removed by the autonomous robot, freeing stomatology surgeon from tedious work.

In laparoscopic surgery, due to the complexity of the surgical environment and task, it is usually necessary to cooperate with multiple surgical robots to complete the surgical task. In this paper, a cooperative motion method of robots is proposed. Through the method, the slave robot follows the master robot automatically, so that the two robots can cooperate with each other under the condition of only controlling the master robot, thus reducing the work intensity of doctors in surgery. The cooperative motion method uses the forward kinematics of the continuum robot to solve the front pose of the master robot, and establishes the relationship between the front points of the two cooperative robots through the change of spatial coordinates, finally uses the inverse kinematics to solve the change of the ropes length of the slave robot. Several robot pose following experiments with different angles and distances are carried out, in order to verify the feasibility of the proposed method and calculate the average tracking error. The experimental results show that the average tracking error of the robot, which is under the control of cooperative motion method is 1.2%, the cooperative motion method can effectively help doctors to complete the cooperative task in laparoscopic surgery.

Objective: Bleeding impairs observation during neurosurgery, and excessive bleeding endangers the life of a patient. Thus, hemostasis is important during neurosurgery. The detection of bleeding areas is a prerequisite for hemostasis. Methods: To the best of our knowledge, this paper is the first to present results on the detection of neurosurgical craniotomy bleeding scenarios, i.e., scalp incision bleeding, skull incision bleeding, and dura matter-incision bleeding. This is realized via a workflow that combines craniotomy image data preparation and a Mask R-CNN framework. Bleeding images on a porcine skin tissue with a simulated blood injected by a syringe are taken by a visible light camera, and the video frames of the scalp incision, skull incision, and dura matter-incision bleeding are extracted from neurosurgical videos. Results: The precision of bleeding areas detection for the simulated bleeding scenario and the three craniotomy phase scenarios were 94.40%, 84.44%, 89.48%, and 90.46%. Conclusion: The contours of the neurosurgical craniotomy bleeding regions can be detected along with the bleeding areas. Significance: It is beneficial for neurosurgeons to identify the bleeding areas by sending prioritized alerts for bleeding events. Furthermore, it is valuable for a task-level medical robot designed for a neurosurgical pro-cedure, such as craniotomy, or a high-level robot designed for an entire neurosurgery procedure.

Neurosurgery are implemented by using a group of tools, including aspirator, monopolar et al., but the tools used in surgery often have only a single function. The alternation of these tools costs extra time. And compared with using tools with image guidance and several functions, the surgery using the single-functional tools will be with lower efficiency. In this paper, a multi-functional all-in-one neurosurgical tool with CCD-imaging, suction and electrical coagulation is designed. When a bleeding spot emerges, the proposed tool firstly detects it by CCD camera, then aspirator is used to suck off the dynamic bleeding, and finally the monopolar is extended to bleeding spot to coagulate the bleeding spot. The feasibility of the tool is demonstrated by experiments with a prototype.

Objective: The very first step for an autonomous lower gastrointestinal (GI) tract robot to carry out a diagnostic or a therapeutic task is to enter the lower GI. The natural entry point of the lower GI tract is the anus. Thus, to find the anus center is the very first step before entering the lower GI tract. However, to the authors’ knowledge, there doesn’t exist any results of detection of anus center. Methods: An image processing pipeline is proposed by combing several classical image methods including Otsu’s method, the multiscale method and the threshold method and two deep neural networks, including Mask R-CNN and Inception-V3. Also, as a complementary result, the classification of healthy and diseased anus is determined by another Inception-V3, for healthy anus. Results: The positional error of the center detection by the proposed workflow is 2.15% averagely compared to the diagonal length, which is at the same level to that ten experienced proctologists. The proposed anal center detection method is applicable for both hairy anus and hairless anus. Also, the approach is valid for the anus in several common perianal diseases including perianal eczema, the mixed hemorrhoids, the anal fistula, the thrombotic external hemorrhoids, the internal hemorrhoids, and the external hemorrhoid. Conclusion: Anus center is detected by the proposed method with a similar accuracy to human doctors. Significance: This study provides the first solution for the anus center detection, enabling autonomous lower gastrointestinal tracts robot to enter anus without human guidance.

Purpose The detection of pylorus, a natural gastrointestinal (GI) anatomical structure, is one of the fundamental techniques that enables a high-autonomy digestive tract robot to move from the stomach to the duodenum. The pyloric center is the optimal position for passing pylorus from the soft-tissue protection standpoint. Thus, detection of the pylorus center should be investigated further in view of its indispensability for a high-autonomy GI robot. However, to the best of our knowledge, no result of pylorus center detection has been published thus far. Methods In this paper, we have developed a pylorus center detection method using CNN classification, Sobel and Laplace operators. The proposed algorithm’s effectiveness is demonstrated by the precise center detection of six types of healthy pylori and six settings of diseased pylori. Results The average detection accuracy of the pylorus center is 22.33 pixels, which corresponds to a relative error of 2.33% when compared to 960 pixels, which corresponds to the diagonal length of an endoscopic image. A single image takes an average of 26.51 ms to process. Conclusion The clinical feasibility of the algorithm for real-time pylorus center tracking is established. The developed algorithm enables GI robots to autonomously locate and pass the pylorus.

Surgical robot is one of the most important application fields of human-robot collaboration. Since the operating objects are often fragile human tissues, how to improve the safety of the surgical process is the key for practical application of surgical robots. General human-robot interaction methods for safety such as collision detection and obstacle avoidance are relatively passive and robot-based, having difficulty in dealing with complex surgical scenarios. Virtual Fixture(VF), an active motion constraint method, is more suitable for shared control in surgical scenarios. This paper introduces a combination of VF method with admittance control for avoiding damage to robot or tissues while complying with doctor’s intention. Firstly, in order to obtain the human-robot interaction force, a statistical method for six-dimensional force sensor calibration is proposed. Then, two velocity-based admittance control models are compared by simulation, and the first order inertial model is selected. On the basis of these, a variety of hard VFs with visual information are established. Finally, the effectiveness of the proposed method and the causes of violation are analyzed by experiments on a serial robot manipulator.

Surgical incision is a pervasive procedure in medical environments. Stapling is an incision closure method that is comparable to stitching. While incision closure is performed immediately after a surgery, staples are removed several weeks after the closure. The workload of surgeons and the probability of infections can be highly reduced if both stapling and staple removal procedures are accomplished by autonomous medical robots. This study proposes a medical robot system that can autonomously perform both the stapling of surgical incisions and the removal of staples after the stapled incision is healed. Individual module designs for stapling and removing staples are presented in detail. The modules were attached to an end-effector that was connected to the end joint of the manipulator. Based on binocular vision, a control method consisting of incision detection, staple position planning, and staple detection was developed to guide the robot to press the staples into a simulated piece of wounded skin and remove the staples from the same phantom. The experiments demonstrated that the prototypical robot effectively performed both stapling and staple removal tasks without human intervention.