Natural orifice transluminal endoscopic surgery (NOTES) has received significant attentions due to its minimal incision trauma compared with traditional multi-port robot assisted surgery. Continuum robot can be used in NOTES due to its high flexibility which can adapt to circuitous paths. However, the modeling of continuum robot is complex due to its nonlinear dynamics, especially with uncertain external disturbance. In this letter a hybrid adaptive control framework is proposed, combining offline trained robot inverse kinematics with neural network and online adaptive adjustment of PID controller parameters with another neural network, providing additional driving amount to the robot system to compensate the positioning error caused by external disturbance. Path tracking and point tracking experiments with external load are conducted and the results validate that the proposed hybrid adaptive control framework can compensate uncertain factors such as friction, driving tendon relaxation and external load during robot movement, and obtain relatively high positioning accuracy (maximum absolute error less than 2 mm) with quickresponse.

In robot-assisted endoscopic nasal surgery, due to the narrow and complex nasal cavity and sinus anatomical structure, the nasal endoscope of robot end-effector is prone to injure surrounding tissues during surgical approaches. In order to improve the safety of endoscope movement in robot-assisted endoscopic nasal surgery, this paper proposes a path planning method for endoscopic surgical approaches based on medical image pixel map searching, and safe surgical paths from the nasal entrance point to the operating areas are obtained. Firstly, through the trajectory analysis of nasal endoscope tip during surgery, the path planning requirements of endoscope tip are analyzed and obtained. Then, considering the narrow and complex anatomy of the nasal cavity, a binarized three-dimensional grid map containing the spatial anatomy of the nasal cavity is constructed based on the patient's CT medical image sequence. The endoscopic surgical approaches are searched and optimized by introducing the A-star algorithm, and safe surgical paths from the nasal entrance point to the operating areas are obtained. Finally, a virtual nasal endoscopy system is developed and tested on a head model containing nasal tissue, and the effectiveness of the planned surgical paths is verified by automatic virtual nasal endoscopy browsing experiment.

In endoscopic nasal surgery, robots, rather than surgical assistants, can be introduced to hold endoscopes and act as the surgeon's third hand, which helps to reduce their operation burden. To address the problem of robot-assisted surgical field of view (FOV) acquisition in endoscopic nasal surgery, this paper proposes an automatic surgical FOV control method based on tracking motion for the entire surgical process to achieve efficient and safe surgical FOV acquisition. First, through the analysis of the spatial pose relationship between the nasal endoscope and the surgical instrument, the desired endoscope pose corresponding to the optimal surgical FOV during surgery is obtained. Then, an automatic surgical FOV control method based on tracking motion is proposed. For automatic FOV acquisition outside the nasal cavity, an unconstrained tracking motion control strategy is proposed. For automatic FOV acquisition inside the nasal cavity, a remote center of motion (RCM) constrained control strategy is proposed. Combined with the entire surgical process, the overall control framework of the surgical FOV during nasal surgery is established. After that, a virtual fixtures (VFs) model based on the CT data of the nasal cavity and a safety contact force threshold control method are proposed to improve the safety of robot motion. Finally, the robot-assisted maxillary sinus surgery experiment is carried out. The results show that the proposed automatic FOV control method can realize clear and safe automatic FOV acquisition during the entire surgical process so that the surgeon can realize the “one surgeon three hands” operation, which reduces the surgeon's operation burden.

In endoscopic sinus surgery, the robot assists the surgeon in holding the endoscope and acts as the surgeon’s third hand, which helps to reduce the surgeon’s operating burden and improve the quality of the operation. This paper proposes a human–robot cooperative control method based on virtual fixture to realize accurate and safe human–robot interaction in endoscopic sinus surgery. Firstly, through endoscopic trajectory analysis, the endoscopic motion constraint requirements of different surgical stages are obtained, and three typical virtual fixtures suitable for endoscopic sinus surgery are designed and implemented. Based on the typical virtual fixtures, a composite virtual fixture is constructed, and then the overall robot motion constraint model is obtained. Secondly, based on the obtained robot motion constraint model, a human–robot cooperative control method based on virtual fixture is proposed. The method adopts admittance control to realize efficient human–robot interaction between the surgeon and robot during the surgery; the virtual fixture is used to restrain and guide the motion of the robot, thereby ensuring motion safety of the robot. Finally, the proposed method is evaluated through a robot-assisted nasal endoscopy experiment, and the result shows that the proposed method can improve the accuracy and safety of operation during endoscopic sinus surgery.

Background and Aims: Lower extremity fracture reduction surgery is a key step in the treatment of lower extremity fractures. How to ensure high precision of fracture reduction while reducing secondary trauma during reduction is a difficult problem in current surgery.Methods: First, segmentation and three‐dimensional reconstruction are performed based on fracture computed tomography images. A cross‐sectional point cloud extraction algorithm based on the normal filtering of the long axis of the bone is designed to obtain the cross‐sectional point clouds of the distal bone and the proximal bone, and the optimal reset target pose of the broken bone is obtained by using the iterative closest point algorithm. Then, the optimal reset sequence of reset parameters was determined, combined with the broken bone collision detection algorithm, a surgical planning algorithm for lower limb fracture reset was proposed, which can effectively reduce the reset force while ensuring the accuracy of the reset process without collision.Results: The average error of the reduction of the model bone was within 1.0 mm. The reduction operation using the planning and navigation system of lower extremity fracture reduction surgery can effectively reduce the reduction force. At the same time, it can better ensure the smooth change of the reduction force.Conclusion: Planning and navigation system of lower extremity fracture reduction surgery is feasible and effective.