This Review explores the challenges and the opportunities for robotics to tackle infectious diseases such as COVID-19.

During cardiac surgery, the excessive or insufficient contact force between the tip of catheter and heart tissue will lead to the cardiac penetration or noneffective ablative lesion. Usually, the surgeon can only estimate the force at the distal end roughly from their experience by operating the catheter's proximal handle. In this paper, we design a fiber Bragg grating-based triaxial force sensor with parallel flexure hinges to sense the contact force. The proposed mechanism integrated with parallel flexure hinges is capable of achieving an excellent lateral and axial stiffness balance to guarantee the resolution at each direction with the same order of magnitude. The mathematical model is built to design a suitable stiffness configuration and validate the design reasonability. The model-based and model-free methods are adopted to decouple lateral and axial force components. Experimental results demonstrate that the developed 8 French size force sensor can successfully achieve the triaxial force prediction with <;1 g resolution using both decoupling methods. And the model-free method can accomplish a higher accuracy with RMS error <;1% of full scale of [-100 g, 100 g] along the lateral direction and [0 g, 100 g] along the axial compression direction.

Dexterous continuum manipulators (DCMs) have been widely adopted for minimally- and less-invasive surgery. During the operation, these DCMs interact with surrounding anatomy actively or passively. The interaction force will inevitably affect the tip position and shape of DCMs, leading to potentially inaccurate control near critical anatomy. In this paper, we demonstrated a two-dimensional mechanical model for a tendon actuated, notched DCM with compliant joints. The model predicted deformation of the DCM accurately in the presence of tendon force, friction force, and external force. A partition approach was proposed to describe the DCM as a series of interconnected rigid and flexible links. Beam mechanics, taking into consideration tendon interaction and external force on the tip and the body, was applied to obtain the deformation of each flexible link of the DCM. The model results were compared with experiments for free bending as well as bending in the presence of external forces acting at either the tip or body of the DCM. The overall mean error of tip position between model predictions and all of the experimental results was 0.62 ± 0.41 mm. The results suggest that the proposed model can effectively predict the shape of the DCM.

Cable-driven continuum robots are widely used for endoluminal intervention because of their dexterity and shape conforming steerability. However, body contact between the continuum robot and its surrounding anatomy is unavoidable, which imposes a potential safety risk, including vessel wall damage or even perforation. This article presents an approach for body contact estimation of continuum robots with tension-profile sensing of actuation fibers. First, tension-sensing optical fibers with multiple inscribed fiber Bragg grating sensors are used for both actuation and in-situ sensing of the continuum robot. Second, a beam theory-based mechanical model considering segmental differences, multiple fiber interactions, and external force interactions is established, followed by robust estimation of contact positions and forces. Finally, detailed simulations are conducted to validate the accuracy and effectiveness of the proposed method. Experiments on a notched continuum robot are carried out, and the results show that the proposed approach can effectively recover in-situ segmental actuation forces without the need of explicit modeling of the friction between the fibers and guiding channels. The method enables the estimation of the number of contact points, as well as contact positions and contact forces along the body of the continuum robot.

The assessment of microsurgical skills for Robot-Assisted Microsurgery (RAMS) still relies primarily on subjective observations and expert opinions. A general and automated evaluation method is desirable. Deep neural networks can be used for skill assessment through raw kinematic data, which has the advantages of being objective and efficient. However, one of the major issues of deep learning for the analysis of surgical skills is that it requires a large database to train the desired model, and the training process can be time-consuming. This paper presents a transfer learning scheme for training a model with limited RAMS datasets for microsurgical skill assessment. An in-house Microsurgical Robot Research Platform Database (MRRPD) is built with data collected from a microsurgical robot research platform (MRRP). It is used to verify the proposed cross-domain transfer learning for RAMS skill level assessment. The model is fine-tuned after training with the data obtained from the MRRP. Moreover, microsurgical tool tracking is developed to provide visual feedback while task-specific metrics and the other general evaluation metrics are provided to the operator as a reference. The method proposed has shown to offer the potential to guide the operator to achieve a higher level of skills for microsurgical operation.