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 order to achieve effective physical human–robot interaction, human dynamic characteristics needs to be considered in admittance control. This paper proposes a variable admittance control method for physical human–robot interaction based on trajectory prediction of human hand motion. By predicting the moving direction of the robot end tool under human guidance, the admittance control parameters are adjusted to reduce the interaction force. The end tool trajectory of the robot under human guidance is used for offline training of long and short-term memory neural network to generate trajectory predictors. Then the trajectory predictors are used in variable admittance control to predict the trajectory and movement direction of the robot end tool in real time. The variable admittance controller adjusts the damping matrix to reduce the damping value in the moving direction. Experiment results show that, using the constant admittance method as a benchmark, the interaction force of the proposed method is reduced by 23%, the trajectory error is reduced by 51%, and the operating jerk is reduced by at least 21%, which proves that the proposed method improves the accuracy and compliance of the operation.

For a mobile robot, navigation skills that are safe, efficient, and socially compliant in crowded, dynamic environments are essential. This is a particularly challenging problem as it requires the robot to accurately predict pedestrians’ movements, analyse developing traffic situations, and plan its own path or trajectory accordingly. Previous approaches still exhibit low accuracy for pedestrian trajectory prediction, and they are prone to generate infeasible trajectories under complex crowded conditions. In this paper, we develop an improved socially conscious model to learn and predict a pedestrian’s future trajectory. To generate more efficient and safer trajectories in a changing crowed space, an online path planning algorithm considering pedestrians’ predicted movements and the feasibility of the candidate trajectories is proposed. Then, multiple traffic states are defined to guide the robot finding the optimal navigation strategies under changing traffic situations in a crowded area. We have demonstrated the performance of our approach outperforms state-of-the-art approaches with public datasets, in low-density and simulated medium-density crowded scenarios.

Objective: Computational model of bone healing can replace animal experiments to study the parameters affecting the bone healing process, thus reducing the damage to experimental animals and saving a lot of time. We propose a computational model for continuous simulation of four phases of bone healing to study the effects of mechanical environmental and biological factors, including initial conditions at the fracture site, mechanical stimulus loading, and vascular growth rate. Methods: A finite element model of mechanobiological fracture healing containing several pre-determined variables was developed for bone healing after fracture in sheep, which included many relevant parameters and biological effects during fracture healing, such as the effects of mechanical environment, blood supply level in the local fracture area, cell migration and diffusion, and resorption effects of fracture healing. The effects of several parameters on indices such as Young's modulus of the callus during bone healing were obtained by simulation. Results: The initial geometry of the healing tissue and mechanical loading had the greatest effect on fracture healing, and different preset values were likely to cause delayed or non-healing fractures. Changed initial tissue properties of the healing tissue showed a nonlinear effect on fracture healing rather than a linear delay or advancement. Parameters related to angiogenesis had a greater effect on fracture healing compared to those related to cell migration. Conclusion: This paper quantified the effect of fracture healing pre-determined variables on fracture healing to better understand the application of mechanobiology in fracture healing simulation models and optimization of treatment strategies. Significance: The importance of initial conditions and loads on fracture healing has been shown to help physicians treat bone nonunion or delayed bone healing after a fracture.