Because of the limited space, the suture of the oral and maxillofacial surgery is a challenging task, which requires oral surgeons to master excellent techniques. This letter presents a single-arm stapling robot for oral and maxillofacial surgery using magnesium alloy staples, as well the stapling strategy of the robot, which can complete the stapling operation. The robot has six degrees of freedom to adjust position and orientation, and a novel stapler with one degree of freedom at the end of the robot. A special stapling strategy is proposed to reapproximate the wound, which is featured using the staple with two different length legs. This letter introduces the mechanism of the robot. Moreover, the kinematics control of the robot is presented. The stapling experiments have been conducted on an oral and maxillofacial model with the wounds of three different widths and different directions. The results demonstrate that the robot can staple the wounds with reasonable accuracy.

In robot-assisted oral surgery, the surgical tool needs to be fed into the target position to perform surgery. However, unmodeled extraoral and complex intraoral environments bring difficulties to motion planning. Meanwhile, the motion is operated manually by the surgeon, causing relatively limited accuracy as well as the risk of misoperation. Moreover, the random movements of the patient’s head bring additional disturbance to the task. To achieve the task, a motion strategy based on a new conical virtual fixture (VF) was proposed. First, by preoperatively specifying a conical guiding cone as the VF, virtual repulsive forces were applied on the out-of-range end effector. Then, based on the two-point adjustment model and velocity conversion, the effect of VF was established to prevent the end-effector from exceeding the constraint region. Finally, a vision system corrects the guiding cone to compensate for the random movement of the patient’s head to feed to a dynamic target. As an auxiliary framework for surgical operation, the proposed strategy has the advantages of safety, accuracy, and dynamic adaptability. Both simulations and experiments are conducted, verifying the feasibility of the proposed strategy.

For robot-assisted dental implant surgery, it is necessary to feed the instrument into a specified position to perform surgery. To improve safety and efficiency, a preoperative planning framework, including a finite-parameter surrogate model (FPSM) and an automatic instrument-placement method, is proposed in this paper. This framework is implemented via two-stage optimization. In the first stage, a group of closed curves in polar coordinates is used to represent the oral cavity. By optimizing a finite number of parameters for these curves, the oral structure is simplified to form the FPSM. In the second stage, the FPSM serves as a fast safety estimator with which the target position/orientation of the instrument for the feeding motion is automatically determined through particle swarm optimization (PSO). The optimized feeding target can be used to generate a virtual fixture (VF) to avoid undesired operations and to lower the risk of collision. This proposed framework has the advantages of being safe, fast, and accurate, overcoming the computational burden and insufficient real-time performance of complex 3D models. The framework has been developed and tested, preliminarily verifying its feasibility, efficiency, and effectiveness.

Fuzzy control algorithm has a good performance in robustness, but it is difficult to achieve higher control precision. Iterative learning control can achieve higher precision with bad robustness. In this paper, a new algorithm is presented combining fuzzy control with iterative learning control. The new algorithm is a P-type iterative learning control in nature, with error gain self-tuning by fuzzy control. The fuzzy control does not use the error rate in order to improve system robustness and avoid differential disturbance. Simulations illustrate the effectiveness and convergence of the new algorithm. The new algorithm has a good performance in robustness with impulse disturbance or persistent random disturbance

Line-Of-Sight (LOS) check plays a crucial role in collision avoidance and time comsuming, particularly in scenarios involving large-scale maps with sparse obstacles, as it necessitates a grid-by-grid state check. Specifically, LOS check consumes more than half of the computational time in any-angle path planning algorithms, such as Theta*, Visibility Graph, and RRT. To address this issue, we propose an efficient LOS checker for maps of arbitrary dimensions with sparse obstacles. Our approach involves a two-step process. Firstly, we partition the passable space into blocks until there is no vacancy for a minimum-sized block. When the adapted Bresenham algorithm reaches a surface of a block, it bypasses grid-by-grid traversal within the block and directly jumps to the opposing surface. This method significantly reduces the number of grids examined, resulting in higher efficiency compared to traditional LOS checks. We refer to our approach as Jump Over Block (JOB). To demonstrate the advantages of JOB, we compare its performance against traditional LOS checks using a widely recognized public dataset. The results indicate that JOB incurs only 1/6 to 1/5 of the computational cost associated with raw LOS checks, making it a valuable tool for both researchers and practitioners in the field. In order to facilitate further research within the community, we have made the source code of the proposed algorithm publicly available. We anticipate that this framework will contribute to the development of more efficient path planning algorithms and expedite various aspects of robotics that involve collision checks.