Magnetic localization, owing to its immunity to line-of-sight occlusion and non-contact nature, is considered a promising technology for medical applications. While it is intuitive that localization performance degrades as the target moves farther from the sensor array, uncertainty analysis has long been overlooked, which is essential for quantifying localization quality. In this work, we present a pose estimation and uncertainty analysis framework on SE(3) for magnetic source localization using sensor arrays, which enables concise formulation of the problem and quantitative assessment of the results. The volume of the uncertainty ellipsoid is used to characterize localization confidence, while the surface shape in Cartesian space is used for visualization. This also provides insight into the effective workspace of the magnetic localization system prior to deployment. Guided by this analysis, we designed a movable magnetic sensor array to expand the limited sensing volume and mitigate localization uncertainty, thereby enhancing overall localization performance. Simulations and pose tracking experiments validate the effectiveness of this framework. By dynamically moving the sensor array to minimize the volume of the uncertainty ellipsoid, localization errors are reduced compared with static and projection-based strategies by 67.04%, 16.51% in position and 43.87%, 20.73% in orientation, respectively. Furthermore, phantom experiments on distal locking screw alignment and magnetic capsule endoscope tracking demonstrate the system's capability in improving localization accuracy (reducing alignment errors by 71.88%) and expanding the effective workspace by several folds.

Nowadays, laparoscopic surgery procedures face a trade-off between expensive, complex robotic systems and manual instruments with limited functionality. Fully robotic solutions offer precision but lack portability and intuitive control, while manual tools rely solely on the surgeon’s dexterity, limiting maneuverability and depth perception in confined spaces. To bridge this, we propose a Human-Guided Robotic-Assistance Handheld Continuum Medical Robot System (HRHC). This system simulates intuitive manual operation with robotic precision, extending the surgeon’s capabilities while maintaining portability. Additionally, a stereo vision system enhances real-time depth perception, improving spatial awareness in minimally invasive procedures.

Continuum robots, owing to their inherent compliance, have become essential in endoscopic surgical procedures, such as mucosal ablation. However, the prevalent design of endoscopic manipulators, which typically features only a single active bending segment, often results in limited dexterity and accessibility. Additionally, the incorporation of variable stiffness in these robots has attracted significant interest, with the aim to improve manipulation capabilities in confined spaces. In the paper, we propose a novel variable-length continuum robot with variable stiffness for endoscopic surgery. The robot’s stiffness can be altered either by modifying the catheter’s length or solid-liquid transition of low-melting-point alloy (LMPA). The design and fabrication methods of the robot are meticulously detailed. Additionally, a quasi-static stiffness model along with a learning-based stiffness compensation approach for accurate stiffness estimation are proposed. Leveraging this model, a contact force controller is designed for ablation procedure. The experimental results show that our robot possesses good flexibility and accessibility, making it highly adept at manipulating in confined spaces. Its variable stiffness feature significantly enhances its ability to counteract external disturbance and prevent tip deformation (with a average position change of 1.1mm). Finally, through force control experiments and a surgical demonstration in a gastrointestinal model, we have further validated the robot’s applicability in surgical contexts. Note to Practitioners—This paper proposed a variable-length continuum robot with variable stiffness for endoscopic surgery. The robot can achieve axial elongation and omnidirectional bending motion, having better dexterity and accessibility than traditional medical continuum robots with one active bending segment. The robot’s stiffness can be adjusted by the length changes or solid-liquid transition of low-melting-point alloy (LMPA). Besides, an accurate stiffness model and a contact force controller are proposed for endoscopic ablation surgery. By experimental results, the robot shows high flexibility and accessibility, allowing access to confined spaces for manipulation, and good control accuracy and variable stiffness capability for endoscopic surgery.

Magnetic continuum robots are subject to external magnetic fields and deformed remotely, simplifying the robot’s transmission mechanism and providing it with significant potential for miniaturization and operational flexibility. However, modeling magnetic field distribution generated by permanent magnets is complex and requires time-consuming pre-calibrations. Moreover, it is highly susceptible to environments with ferromagnetic materials, posing significant challenges for the control of magnetic continuum robots. In response, we propose an approach that does not overly focus on the magnetic field distribution but instead directly learns the inverse kinematics of magnetic continuum robots end-to-end. Binding the robot’s configuration to the pose of external magnets, precise control of continuum robots is facilitated. Additionally, we leverage teleoperation techniques to broaden the applicability of this method. By mounting magnets on a robotic arm and directly utilizing the target pose of the external magnet predicted by a multi-layer perceptron (MLP), we achieve the operation and navigation of magnetic continuum robots in complex environments. Experiments demonstrate that the mean control accuracy along the robot using our learning-based inverse kinematics is about half of the robot’s diameter.

Small-scale continuum robot-assisted minimally invasive surgery has received crucial attention due to its smaller incisions and high dexterity. In medical scenarios such as radiofrequency ablation and nasal/throat swab sampling, monitoring and controlling the forces applied to human tissue can help improve the safety and comfort level of the procedure. However, the tip-sensor-based force detection method can barely be deployed due to the miniature size of the continuum robot; meanwhile, the mechanical modeling-based high-precision force estimation cannot be realized on account of the continuum robots’ complex structure with high nonlinear properties. To address the high-precision force estimation challenge for further compliant control during minimally invasive interventions, a learning-based high-precision force estimation method via long short-term memory (LSTM) is proposed in this paper. On this basis, compliance control and high-precision force tracking can be further realized for small-scale continuum robot. The compliance control ensures a smooth and stable transition during the interaction between the robot and the environment, and force tracking can be utilized for maintaining or precisely controlling the force applied to the human tissue. Finally, the contact force sensing and control experiments are carried out on a small-scale continuum robot system prototype, and a demonstration using a human nasal cavity model is conducted. The results validate that the proposed LSTM neural network fits the mechanical model of the continuum robot well with the root mean square error of 3.44mN, and the control method can significantly compensate for the instantaneous impact during contact with an attenuation of 59.3$\%$and rapidly respond to keep the force accurately at the expected value with the mean absolute error of 2.41mNNote to Practitioners—This research is motivated by the increasing number of applications for small-scale continuum robot-assisted minimally invasive interventional surgery, such as radiofrequency ablation, biopsy, and endoscopic submucosal dissection. These surgeries require high-precision contact force sensing and control. However, due to the small size and complex structure of the continuum robot, traditional methods such as installing force sensors and mechanical modeling are not effective. Therefore, this paper utilizes a neural network to infer contact force through more accessible information about the robot, such as the tension of the actuators. This method allows for compliant force control during the dynamic contact between the small-scale continuum robot and human tissue. Experiments conducted on a human nasal cavity model demonstrate that the proposed method can improve the safety and reliability of small-scale continuum robot-assisted minimally invasive surgery.

With the increasing computing power, using data-driven approaches to co-design a robot's morphology and controller has become a promising way. However, most existing data-driven methods require training the controller for each morphology to calculate fitness, which is time-consuming. In contrast, the dual-network framework utilizes data collected by individual networks under a specific morphology to train a population network that provides a surrogate function for morphology optimization. This approach replaces the traditional evaluation of a diverse set of candidates, thereby speeding up the training. Despite considerable results, the online training of both networks impedes their performance. To address this issue, we propose a concurrent network framework that combines online and offline reinforcement learning (RL) methods. By leveraging the behavior cloning term in a flexible manner, we achieve an effective combination of both networks. We conducted multiple sets of comparative experiments in the simulator and found that the proposed method effectively addresses issues present in the dual-network framework, leading to overall algorithmic performance improvement. Furthermore, we validated the algorithm on a real robot, demonstrating its feasibility in a practical application.