Leader-follower robotic guidewire delivery system has been a research focus in terms of vascular interventional surgery (VIS) for a few years. However, due to the inconvenient operations and lack of intuition in leader robot manipulation, such systems are not widely used. To get rid of cumbersome operations and promote the safety of guidewire delivery in a VIS, the first autonomous guidewire delivery scheme for VIS which incorporates both human skill and robotic autonomous manipulation is proposed in this article. The delivery criterion of a surgeon remotely manipulating the guidewire is formulated by an impedance model. Then, an autonomous delivery controller based on this model steers the guidewire distal position, not the proximal position which is commonly used in relevant research, towards the desired location. We reduce the effect of uncertainty in flexible guidewire deformation by a real-time estimator. The proposed method exhibits distinct superiorities in both human skill and robotic fast response. Experimental results confirmed the advantages of the proposed autonomous delivery scheme in reducing cumbersome operations and smoothening contact force in a VIS.Note to Practitioners—Many researchers have developed a variety of leader-follower robotic systems for the VIS to reduce harm to surgeons from radiation, because of the occupational use of X-rays in interventional surgeries. However, the insertion manipulation, as a common movement in a VIS, needs a large workspace which is in contradiction to the workspaces of commonly used leader robots. For the sake of less cumbersome movements and smooth operational force, an autonomous guidewire delivery scheme is proposed. This scheme is composed of two phases. In the first phase, the surgeon manipulates the leader robot to remotely complete just one insertion movement. From this movement, manual manipulation skill is extracted by an impedance model, which reveals the relationship between the guidewire tip position and the contact force. Then, in the second phase, an autonomous delivery controller based on the impedance model is devised and adopted to complete the insertion task. By taking the guidewire tip position, not the follower robot position, as a fundamental component of the impedance model, this scheme conforms to sophisticated manual manipulation skill. Such an autonomous scheme takes advantage of both human skills and robotic stability and can be applied in autopilot, assembling, etc.

Percutaneous puncture surgery is commonly used for effective diagnosis and treatment. However, the popular manual manipulation brings in positional unsteadiness which increases the suffering of patients. In this study, a collaborative method is proposed by taking into the surgeon’s skills and the robotic stable operation. In this scheme, a modified compliance controller with a biomechanics-based activation condition is designed. This controller excludes positional undulations resulting from force disturbances. In addition, a breakthrough detector and regulator based on the human force reaction process are advised to repress the positional overshooting deriving from force jump at the moment of tissue breakthrough. This method enhances the manipulating smoothness and suppresses puncture needle overshooting. The overshooting is compensated accordingly by the proposed regulator and surgical risk is lowered. A collaborative puncture robot was constructed. Then, model and animal experiments on this robot verified the effectiveness and feasibility of the proposed method.

The operating skills of vascular interventionists have an important impact on the effect of surgery. However, current research on behavior recognition and skills learning of interventionists' operating skills is limited. In this study, an innovative deep learning-based multimodal information fusion architecture is proposed for recognizing and analyzing eight common operating behaviors of interventionists. An experimental platform integrating four modal sensors is used to collect multimodal data from interventionists. The ANOVA and Manner-Whitney tests is used for relevance analysis of the data. The analysis results demonstrate that there is almost no significant difference ( p <0.001) between the actions related to the unimodal data, which cannot be used for accurate behavior recognition. Therefore, a study of the fusion architecture based on the existing machine learning classifier and the proposed deep learning fusion architecture is carried out. The research findings indicate that the proposed deep learning-based fusion architecture achieves an impressive overall accuracy of 98.5%, surpassing both the machine learning classifier (93.51%) and the unimodal data (90.05%). The deep learning-based multimodal information fusion architecture proves the feasibility of behavior recognition and skills learning of interventionist's operating skills. Furthermore, the application of deep learning-based multimodal fusion technology of surgeon's operating skills will help to improve the autonomy and intelligence of surgical robotic systems.

Snake-like robots for endoscopic surgery make it possible to reach deep-seated lesions. With the use of small flexible tendon-driven instruments, it is possible to perform bimanual micro-surgical tasks that are challenging for standard endoscopic surgeries. Existing devices, however, lack articulated wrists and rolling motion of the end-effector. This paper presents a new instrument design with a distal-roll gripper for snake-like robots. The developed 5 DoFs miniaturized instruments with a diameter of 3 mm enable the deployment into narrow endoluminal channels. Issues related to actuation coupling, tendon slack, and backlash are addressed. Experimental results show that the distal-roll gripper can rotate 106°, and the actuated joints can achieve good repeatability and accuracy with the proposed compensation control scheme.

Visual localization is a crucial problem in mobile robotics and autonomous driving. One solution is to retrieve images with known pose from a database for the localization of query images. However, in environments with drastically varying conditions (e.g. illumination changes, seasons, occlusion, dynamic objects), retrieval-based localization is severely hampered and becomes a challenging problem. In this paper, a novel domain-invariant feature learning method (DIFL) is proposed based on ComboGAN, a multi-domain image translation network architecture. By introducing a feature consistency loss (FCL) between the encoded features of the original image and translated image in another domain, we are able to train the encoders to generate domain-invariant features in a self-supervised manner. To retrieve a target image from the database, the query image is first encoded using the encoder belonging to the query domain to obtain a domain-invariant feature vector. We then preform retrieval by selecting the database image with the most similar domain-invariant feature vector. We validate the proposed approach on the CMU-Seasons dataset, where we outperform state-of-the-art learning-based descriptors in retrieval-based localization for high and medium precision scenarios.

Cardiovascular disease remains a primary cause of morbidity globally. Percutaneous coronary intervention plays a crucial role in the treatment. The radiation exposure of surgeons during the cardiovascular intervention can be avoided by master-slave surgical robots. This paper introduces a master- slave guidewire and catheter robotic system to protect the surgeons from X ray radiation to the most extent. And the jitters of master manipulators are mitigated by Kalman filtering algorithm. With two master manipulators, it helps to retain the surgeon's traditional operating habits. Also, a vascular model trial was conducted to validate that this interventional robotic system could complete the alternate progress and rotation of interventional guidewire and catheter.