Efficient and high-fidelity reconstruction of deformable surgical scenes is a critical yet challenging task. Building on recent advancements in 3D Gaussian splatting, current methods have seen significant improvements in both reconstruction quality and rendering speed. However, two major limitations remain: (1) difficulty in handling irreversible dynamic changes, such as tissue shearing, which are common in surgical scenes; and (2) the lack of hierarchical modeling for surgical scene deformation, which reduces rendering speed. To address these challenges, we introduce EH-SurGS, an efficient and high-fidelity reconstruction algorithm for deformable surgical scenes. We propose a deformation modeling approach that incorporates the life cycle of 3D Gaussians, effectively capturing both regular and irreversible deformations, thus enhancing reconstruction quality. Additionally, we present an adaptive motion hierarchy strategy that distinguishes between static and deformable regions within the surgical scene. This strategy reduces the number of 3D Gaussians passing through the deformation field, thereby improving rendering speed. Extensive experiments on public datasets captured with static endoscopes demonstrate that our method surpasses existing state-of-the-art approaches in both reconstruction quality and rendering speed. Ablation studies further validate the effectiveness and necessity of our proposed components. We will open-source our code upon acceptance of the paper.

Existing sensor-based global localization methods limit the miniaturization potential of magnetically-actuated endoscopes (MAE) while localization based on external medical imaging demands accurate registration and imposes a variety of modality-specific challenges during continuous image acquisition. This work proposes a novel self-sufficient method for discrete (one-time) global localization of an MAE based solely on inherent endoscopic images without any prior MAE pose information. More specifically, it adopts a model-free control approach to determine five different external magnet (EM) poses (corresponding to five independent nonlinear equations) that can align the MAE image center with the lumen center while the MAE maintains the same pose. The five degree-of-freedom (DoF) global pose of the MAE can then be estimated by minimizing the root mean square of MAE's torque balance residuals under these EM poses. Our proposed method achieves similar accuracy as other sensor-based methods for permanent magnet-driven MAE with$\mathbf{6.7} \pm \mathbf{2.1}$mm position error and$\mathbf{9.5} \pm \mathbf{2.9}^{\circ}$orientation error in the experiments. Compared to existing methods, our approach does not require physical sensor integration, enabling a more compact endoscope design for exploration in narrower respiratory tracts. It also offers a critical step toward achieving sensorless and continuous global localization of the permanent magnet-driven MAE during its autonomous navigation.

Flexible endoscope motion tracking and analysis in mechanical simulators have proven useful for endoscopy training. Common motion tracking methods based on electromagnetic tracker are however limited by their high cost and material susceptibility. In this work, the motion-guided dual-camera vision tracker is proposed to provide robust and accurate tracking of the endoscope tip's 3D position. The tracker addresses several unique challenges of tracking flexible endoscope tip inside a dynamic, life-sized mechanical simulator. To address the appearance variation and keep dualcamera tracking consistency, the cross-camera mutual template strategy (CMT) is proposed by introducing dynamic transient mutual templates. To alleviate large occlusion and light-induced distortion, the Mamba-based motion-guided prediction head (MMH) is presented to aggregate historical motion with visual tracking. The proposed tracker achieves superior performance against state-of-the-art vision trackers, achieving 42% and 72% improvements against the second-best method in average error and maximum error. Further motion analysis involving novice and expert endoscopists also shows that the tip 3D motion provided by the proposed tracker enables more reliable motion analysis and more substantial differentiation between different expertise levels, compared with other trackers. Project page: https://github.com/PieceZhang/MotionDCTrack

As one of the effective closed-loop control methods, visual servoing control methods are widely applied to continuum robots. However, existing visual servoing control methods mostly focus on accurate control of the robot’s end-effector, with less consideration given to the robot’s shape. In this work, a spatial position-based visual servoing obstacle-avoidable shape control framework for an 11-degree-of-freedom (DOF) hybrid continuum robot is proposed. In the control framework, a set of markers representing the shape of the continuum robot are measured and two spatial arcs are used to fit the shape. When controlling the redundant DOFs of the robot, position-based visual servoing shape control combined with obstacle avoidance is formulated as a quadratic programming problem, yielding the optimal solution at each sample time for the joint velocity vector of the 11-DOF hybrid continuum robot. Several experiments are conducted to validate the proposed control framework, which indicates the accuracy of the shape control achieves 0.88 mm.

Needle tip tracking under ultrasound (US) imaging is critical for accurate lesion targeting in US-guided percutaneous procedures. While most state-of-the-art trackers have relied on complex network architecture for enhanced performance, the compromised computational efficiency prevents their real-time implementation. Pure visual trackers are also limited in addressing the drift errors caused by temporary needle tip disappearance. In this paper, a compact, task-oriented visual tracker, consisting of an appearance adaptation module and a distractor suppression module, is first designed before it is integrated with a motion filter, namely TransKalman, that leverages the Transformer network for Kalman filter gain estimation. The ablation study shows that the mean tracking success rate (i.e. error <3mm in 95% video frames) of the visual tracker increases by 25% compared with its baseline model. The complete tracking system, integrating the visual tracker and TransKalman, outperforms other existing trackers by at least 5.1% in success rate and 47% in tracking speed during manual needle manipulation experiments in ex-vivo tissue. The proposed real-time tracking system will potentially be integrated in both manual and robotic procedures to reduce operator dependence and improve targeting accuracy during needle-based diagnostic and therapeutic procedures.

Defocus blur is a persistent problem in microscope imaging that poses harm to pathology interpretation and medical intervention in cell microscopy and microscope surgery. To address this problem, a unified framework including the multi-pyramid transformer (MPT) and extended frequency contrastive regularization (EFCR) is proposed to tackle two outstanding challenges in microscopy deblur: longer attention span and data deficiency. The MPT employs an explicit pyramid structure at each net-work stage that integrates the cross-scale window attention (CSWA), the intra-scale channel attention (ISCA), and the feature-enhancing feed-forward network (FEFN) to capture long-range cross-scale spatial interaction and global channel context. The EFCR addresses the data de-ficiency problem by exploring latent deblur signals from different frequency bands. It also enables deblur knowl-edge transfer to learn cross-domain information from ex-tra data, improving deblur performance for labeled and unlabeled data. Extensive experiments and downstream task validation show the framework achieves state-of-the-art performance across multiple datasets. Project page: https:llgithub.comIPieceZhangIMPT-CataBlur.

Recently, fiber optic sensors such as fiber Bragg gratings (FBGs) have been widely investigated for shape reconstruction and force estimation of flexible surgical robots. However, most existing approaches need precise model parameters of FBGs inside the fiber and their alignments with the flexible robots for accurate sensing results. Another challenge lies in online acquiring external forces at arbitrary locations along the flexible robots, which is highly required when with large deflections in robotic surgery. In this paper, we propose a novel data-driven paradigm for simultaneous estimation of shape and force along highly deformable flexible robots by using sparse strain measurement from a single-core FBG fiber. A thin-walled soft sensing tube helically embedded with FBG sensors is designed for a robotic-assisted flexible ureteroscope with large deflection up to 270° and a bend radius under 10 mm. We introduce and study three learning models by incorporating spatial strain encoders, and compare their performances in both free space without interactions as well as constrained environments with contact forces at different locations. The experimental results in terms of dynamic shape-force sensing accuracy demonstrate the effectiveness and superiority of the proposed methods.

In this article, we investigate the optimal control problem for semicar active suspension systems (SCASSs). First, we model the SCASSs by Newtonian dynamics as well as considering the uncertainties and nonlinear dynamics of the actuator. Second, in order to solve the complexity brought by uncertainties, we apply the Takagi–Sugeno (T-S) fuzzy approach to transform the SCASSs as multilinear systems, as well as solving the optimal control problem as a zero-sum problem to find the solution of Nash-equilibrium. Third, we construct a novel self-learning method based on the reinforcement learning framework, and propose two algorithms to solve the fuzzy game algebraic Riccati equation. Especially, in the second algorithm, without using any model information of the SCASSs, we only use the state and input information in control design by a self-learning manner removing the traditional dependence problem, which is more preferable for practical applications. Finally, we give a simulation result of the SCASSs to demonstrate the effectiveness and practicability for the designed self-learning algorithms.

Recent advancements in Magnetic Resonance Imaging (MRI) reconstruction techniques aim to accelerate the imaging process. However, these methods still face two key limitations. Firstly, although the same location consistently provides anatomical information across different modalities, such as organs, tissues, or lesions, previous studies have predominantly relied on single-modality information, overlooking the potential advantages of incorporating complementary data from other modalities. Secondly, while adjacent MRI slices often capture the same location or organ with similar anatomical structures, only a few methods consider the information from neighboring slices during the reconstruction process. To address these challenges, we propose aMulti-modalMulti-slice cooperativeDual-domain cascaded de-alising network for MR imagingReconstruction (MMDR). Specifically, we design a multi-slice and multi-modal feature fusion network based on 3D convolution and swin transformer that efficiently extracts multi-modal features from MRI. Then, a dual domain cascaded recurrent network through dense-blocks with large receptive fields for fast MRI reconstruction is explored. Extensive experiments on the IXI datasets were carried out to evaluate the proposed method's robustness across varying network structures, under-sampling rates, and sampling patterns. MMDR demonstrates promising performance across both qualitative and quantitative metrics, particularly with a competitive PSNE of 42.15 and an SSIM of 0.984 for T2 reconstruction using 30% T2WI and PDWI, as well as achieving a PSNE of 39.92 and an SSIM of 0.966 for PDWI reconstruction with 30% PDWI and T2WI.

Ultrasound (US) is widely used in image-guided needle procedures. Correctly tracking the needle tip position in US images during the procedure plays an important role in improving the needle targeting accuracy and patient safety. This paper presents a leaning-based visual tracking network with a Siamese architecture, which makes full use of the attention mechanism to explore the potential of global features and takes advantage of an online target model prediction module to robustly track the needle tip in US images. Several self- and cross-attention modules are applied to learn global features from the whole US image. A discriminative target model is also learned as a complementary part to improve the discriminability of the proposed tracker. The template used during the tracking is updated frequently according to the tracking results to ensure that the tracker can always capture the latest characteristics of the appearance of the needle tip. Experimental results in both phantom and tissue showed that the proposed tracking network was more robust than other state-of-the-art visual trackers. The mean success rates of the proposed tracker are 7.1% and 9.2% higher than the second best performing visual tacker when the needle was inserted by motors and human hands in the tissue experiments.

Magnetic resonance imaging (MRI) is increasingly used for robotic needle-based clinical diagnosis and therapy due to its high resolution and high soft tissue contrast. A needle driver therefore becomes an essential part of these MRI -guided robotic systems to perform in-bore needle placement under continuous MR imaging. However, existing actuator designs for needle drivers are constrained by the high magnetic field and the bore size of the MR scanner, and most lack high- performance capability, especially in terms of large output force and long stroke. In this work, we introduce an MR-safe concentric bellows linear actuator (CBLA) with improved performance over the existing designs. This soft actuator achieves 45N output force, 77 % stroke-length ratio for the bellows, and around 250% stroke-diameter ratio. A mathematical model was built to characterize the behavior of the actuator to provide guidance for the actuator design. The fabrication and experimental evaluation of the actuator are also presented. The results demonstrate the effectiveness of the CBLA design and the strong potential of its application in MR-guided surgical procedures.

Pericardiocentesis is an important surgical intervention to treat a medical condition called pericardial effusion, during which excessive fluid accumulates around the heart, potentially leading to life-threatening situation. It involves the insertion of a needle and catheter towards the heart into the pericardial space to drain the excessive fluid under ultrasound (US) guidance. The risky procedure requires surgeons to acquire sufficient training to ensure safe execution of the procedure. However, existing heart simulators lack dynamic features, do not offer realistic images under US imaging, and are not reusable. This work presents a dynamic heart simulator (DHS) with pericardial effusion to mimic the beating motion of the human heart and the realistic US imaging results. The beating heart motion is realized using a hydraulic actuation system connected to a double-layer balloon set. The clear and realistic US imaging results are obtained through a unique formula proposed for the chest tissue and the cardiac muscle. A characterization method was also developed to allow customization of important anatomical parameters in the DHS. The experimental results show that the DHS allowed highly realistic simulation of the beating heart, cardiac muscle, and pericardium under US imaging and has been demonstrated to enable successful US-guided pericardiocentesis.

LiDAR sensors are almost indispensable for autonomous robots to perceive the surrounding environment. However, the transmission of large-scale LiDAR point clouds is highly bandwidth-intensive, which can easily lead to transmission problems, especially for unstable communication networks. Meanwhile, existing LiDAR data compression is mainly based on rate-distortion optimization, which ignores the semantic information of ordered point clouds and the task requirements of autonomous robots. To address these challenges, this article presents a task-driven Scene-Aware LiDAR Point Clouds Coding (SA-LPCC) framework for autonomous vehicles. Specifically, a semantic segmentation model is developed based on multidimension information, in which both 2-D texture and 3-D topology information are fully utilized to segment movable objects. Furthermore, a prediction-based deep network is explored to remove the spatial–temporal redundancy. The experimental results on the benchmark semantic KITTI dataset validate that our SA-LPCC achieves state-of-the-art performance in terms of the reconstruction quality and storage space for downstream tasks. We believe that SA-LPCC jointly considers the scene-aware characteristics of movable objects and removes the spatial–temporal redundancy from an end-to-end learning mechanism, which will boost the related applications from algorithm optimization to industrial products.

Due to the huge volume of point cloud data, storing and transmitting it is currently difficult and expensive in autonomous driving. Learning from the high-efficiency video coding (HEVC) framework, we propose a novel compression scheme for large-scale point cloud sequences, in which several techniques have been developed to remove the spatial and temporal redundancy. The proposed strategy consists mainly of three parts: intracoding, intercoding, and residual data coding. For intracoding, inspired by the depth modeling modes (DMMs), in 3-D HEVC (3-D-HEVC), a cluster-based prediction method is proposed to remove the spatial redundancy. For intercoding, a point cloud registration algorithm is utilized to transform two adjacent point clouds into the same coordinate system. By calculating the residual map of their corresponding depth image, the temporal redundancy can be removed. Finally, the residual data are compressed either by lossless or lossy methods. Our approach can deal with multiple types of point cloud data, from simple to more complex. The lossless method can compress the point cloud data to 3.63% of its original size by intracoding and 2.99% by intercoding without distance distortion. Experiments on the KITTI dataset also demonstrate that our method yields better performance compared with recent well-known methods.Note to Practitioners—This article deals with the problem of efficient compression of point cloud sequences that come from light detection and ranging (LiDARs) mounted on autonomous mobile robots. The vast amount of point cloud data could be an important bottleneck for transmission and storage. Inspired by the HEVC algorithm, we develop a novel coding architecture for the point cloud sequence. The scans are divided into intraframe and interframe, which are encoded separately using different techniques. Our method can be used for the compression of LiDAR point cloud sequences or dense LiDAR point cloud map and will significantly reduce the transmission bandwidth and storage spaces. We have to admit that although our method is less effective for real-time solutions, it can be highly efficient for off-line applications. Future studies will concentrate on further optimizing the coding algorithm to reduce the computational complexity and trying to find a balance between them.

This article explores a novel adaptive optimal control strategy for a class of sophisticated discrete-time nonlinear Markov jump systems (DTNMJSs) via Takagi–Sugeno fuzzy models and reinforcement learning (RL) techniques. First, the original nonlinear system model is represented by fuzzy approximation, while the relevant optimal control problem is equivalent to designing fuzzy controllers for linear fuzzy systems with Markov jumping parameters. Subsequently, we derive the fuzzy coupled algebraic Riccati equations for the fuzzy-based discrete-time linear Markov jump systems by using Hamiltonian–Bellman methods. Following this, an online fuzzy optimization algorithm for DTNMJSs as well as the associated equivalence proof is given. Then, a fully model-free off-policy fuzzy RL algorithm is derived with proved convergence for the DTNMJSs without using the information of system dynamics and transition probability. Finally, two simulation examples, respectively, related to the single-link robotic arm and the half-car active suspension are given to verify the effectiveness and good performance of the proposed approach.

Most existing surgical robots employ straight rigid instruments and are not compatible with imaging modalities, especially magnetic resonance imaging (MRI) that presents restrictive constraints on the robot and actuator materials. Employing continuum distal end effector and fulfilling multi-imager compatibility will potentially lead to wide adoption of surgical robots in intraoperative image-guided minimally invasive surgery (MIS). This paper introduces a 3-dimensional (3D) printed polyamide continuum robot with 2-degree of freedom, driven by modular shape memory alloy (SMA) spring actuators that enable real-time distal manipulation in 3D workspace. The multi-imager compatibility is conditionally satisfied by utilizing no ferromagnetic materials and MRI-conditional actuators in the robotic system. The use of modular SMA allows repeatable configuration setting, easy integration with active cooling strategies, and facilitates the creation of a sterile barrier between the end effector module and the actuation module. Detailed design of the robot, kinematics modeling and actuator modeling are discussed in the paper. We experimentally verified the robot kinematics and evaluated the dynamic performance of the continuum robot. It features an operating bandwidth of 0.12 Hz at -3 dB and a root-mean-square error of 0.98 mm under model predictive control when tracking sinusoidal signals, both with ±10 mm amplitude (distal bending angle of ±80◦). As a demonstration, the robot was used as a flexible endoscope manipulator and steered under inputs from a joystick to show its real-time performance.

Multi-segment continuum robots, that offer inherent compliance and distal dexterity, are suitable for deployment in minimally invasive surgical procedures. Cable-driven mechanism is commonly used in continuum surgical robots but could lead to inter-segment motion coupling in a multi-segment robot. In this paper, we present a coupled mechanics model for a two-segment notched continuum robot to analyze the coupled deflection in the proximal segment due to the distal cable force. The model has been developed for two different conditions in which the proximal segment is initially bent (general condition) and initially straight (special condition). It allows us to introduce a decoupled design methodology that systematically determines a stiffness parameter in each of the segments, based on the desired coupled bending angle and other design requirements. Using the method, we fabricated a decoupled notched continuum robot and evaluated the model accuracy compared with experimental data with mean errors of 0.57° and 0.61°, respectively for general and special conditions throughout the 90° distal segment bending angle. It was also shown in a demonstration in a maxillary sinus phantom that the distal segment was capable of independently perform omnidirectional steering without the proximal segment getting in contact with its surrounding nasal wall.

The combination of a dexterous continuum robot and magnetic resonance imaging can potentially improve surgical precision and minimize brain manipulation in a minimally invasive neurosurgical procedure. In this work, a seven degree-of-freedom (DoF) continuum neurosurgical robot was developed. The main innovation lies in the design of a safe and robust switching mechanism and gear-based quick-connect mechanism that, respectively, allow selective actuation of the 6-DoF end effector using only three motors and highly efficient end effector exchange. Its performance has been validated in experiments involving multi-segment dexterous motion. We also evaluated the robotic system on a human cadaver head in a clinical 3-Tesla MRI. The entire workflow of robotic system set-up was implemented, confirming its clinical feasibility. The signal-to-noise ratio (SNR) drop was consistently less than 6% throughout various stages of end effector motion.

Robotic hands with anthropomorphism considerations are of prominent popularity in human-centered environment. Existing anthropomorphic robotic hands achieving part or most of human hand comparable dexterity have been applied as various robotic end-effectors and prosthetics. However, two deficiencies are evident that the design for a dexterous anthropomorphic hand is largely based on the intuition of designers and the dexterity of robotic hand is hard to evaluate. To tackle these two challenges, this paper summarizes 50 hand dexterity benchmarks (HD-marks) to evaluate hand dexterity comprehensively from three perspectives. Secondly, a novel 22-DOFs soft robotic hand (S-22) replicates human hand kinematics is used to demonstrate all the 50 HD-marks. Thirdly, 7 critical joint-based kinematic motions (K-motions) and their correlation with the 50 HD-marks are established. Therefore, a clear robotic hand design guideline is built by mapping the hand functional dexterity to the required joint kinematics.

Due to the huge volume of point cloud data, storing or transmitting it is currently difficult and expensive in autonomous driving. Learning from the high efficiency video coding (HEVC) coding framework, we propose an advanced coding scheme for large-scale LiDAR point cloud sequences, in which several techniques have been developed to remove the spatial and temporal redundancy. The proposed strategy consists mainly of intra-coding and inter-coding. For intra-coding, we utilize a cluster-based prediction method to remove the spatial redundancy. For inter-coding, a predictive recurrent network is designed, which is capable of generating future frames according to the previously encoded frames. By calculating the residual error between the predicted and real point cloud data, the temporal redundancy can be removed. Finally, the residual data is quantized and encoded by lossless coding schemes. Experiments are conducted on the KITTI data set with four different scenes to verify the effectiveness and efficiency of the proposed method. Our approach can deal with multiple types of point cloud data from the simple to more complex, and yields better performance in terms of compression ratio compared with octree, Google Draco, MPEG TMC13 and other recently proposed methods.

Deep intracranial tumor removal can be achieved if the neurosurgical robot has sufficient flexibility and stability. Towards achieving this goal, we have developed a spring-based continuum robot, namely a Minimally Invasive Neurosurgical Intracranial Robot (MINIR-II) with novel tendon routing and tunable stiffness for use in a magnetic resonance imaging (MRI) environment. The robot consists of a pair of springs in parallel, i.e., an inner inter-connected spring that promotes flexibility with decoupled segment motion and an outer spring that maintains its smooth curved shape during its interaction with the tissue. We propose a shape memory alloy (SMA) spring backbone that provides local stiffness control and a tendon routing configuration that enables independent segment locking. In this work, we also present a detailed local stiffness analysis of the SMA backbone and model the relationship between the resistive force at the robot tip and the tension in the tendon. We also demonstrate through experiments, the validity of our local stiffness model of the SMA backbone and the correlation between the tendon tension and the resistive force. We also performed MRI compatibility studies of the 3-segment MINIR-II robot by attaching it to a robotic platform that consists of SMA spring actuators with integrated water cooling modules.

Minimally invasive neurosurgical robotic procedure performed under real-time MRI can be achieved with a robot that is head-mounted and fits in a standard MRI bore. The design of a remotely-actuated cable-driven robotic system is presented in this work. A head mounted insertion module has been developed to advance the robot module towards the target brain tumor. The robot module is formed by snapping together a reusable part that leads to the remotely-placed actuators and a disposable part which includes the previously developed spring-based MINIR-II robot. Gear transmission mechanism was implemented in the robot module to transmit the force from the actuators to the end effectors of the robot. The robot module and the insertion module are lightweight and compact with a total weight of 250 g and a total height of 222 mm. This allows the entire setup to be placed vertically on a patient's head in a closed MRI bore. The compact design of the robot module has been achieved, significantly due to the innovative modifications made to the structure of standard spur gears and smart routing of the cables around the gears. The completely MRI-compatible Bowden cable module has been implemented to allow transmission of significant force of around 10 N required for the actuation of the robot. A static friction model has been used to estimate the friction coefficient for the Bowden cable module as a preliminary step towards the complete modeling of the entire robotic system. The relationships between the input displacement and force of the cable at the actuator end and those at the middle and end robot segments were determined through experiments and discussed. A functional robotic system has been presented with the robot being inserted into the phantom tissue and only the end segment actuated back and forth.

The paper presents the use of shape memory alloy (SMA) spring actuators with real-time cooling to control the motion of the MINIR-II robot. A new actuation mechanism involving the passage of water as the cooling medium and air as the medium to drive out the water has been developed to facilitate real-time control of the springs. Control parameters, such as current, water flow rates, SMA pre-displacement, and gauge pressure of the compressed air, are identified from the SMA thermal model and from the actuation mechanism. In depth modeling and characterization have been performed regarding these parameters to optimize the robot motion speed. Forced water cooling has also been compared with forced air cooling and proved to be the superior method to achieve higher robot speed. An improved robot design and an MRI-compatible experimental platform have been developed for the implementation of the actuation mechanism.

Brain tumor, be it primary or metastatic, is usually life threatening for a person of any age. Primary surgical resection which is one of the most effective ways of treating brain tumors can have tremendously increased success rate if the appropriate imaging modality is used for complete tumor resection. Magnetic resonance imaging (MRI) is the imaging modality of choice for brain tumor imaging because of its excellent soft-tissue contrast. MRI combined with continuum soft robotics has immense potential to be the next major technological breakthrough in the field of brain cancer diagnosis and therapy. In this work, we present the design, kinematic, and force analysis of a flexible spring-based minimally invasive neurosurgical intracranial robot (MINIR-II). It is comprised of an inter-connected inner spring and an outer spring and is connected to actively cooled shape memory alloy spring actuators via tendon driven mechanism. Our robot has three serially connected 2-DoF segments which can be independently controlled due to the central tendon routing configuration. The kinematic and force analysis of the robot and the independent segment control were verified by experiments. Robot motion under forced cooling of SMA springs was evaluated as well as the MRI compatibility of the robot and its motion capability in brainlike gelatin environment.

Robotic surgery, especially in the field of neurosurgery, can be tremendously improved with the integration of an excellent imaging modality during the procedure. Shape memory alloy (SMA), a high power density and inexpensive MRI-compatible actuator, is therefore being considered as an appropriate actuator for the robot. However, the low control bandwidth of SMA due to the long cooling time makes it undesirable for commercial use. An efficient and low-cost cooling method using water as a coolant that passes through a flexible tube coiled around the SMA spring is proposed to increase the cooling rate of SMA, thereby improving its actuation frequency. SMA constitutive model and heat transfer model have been developed to simulate theoretical behavior of SMA springs in antagonistic configuration. The maximum bandwidth we achieved was 0.333 Hz for tracking of a sinusoidal trajectory of 3 mm peak-to-peak magnitude. We also demonstrated the capability of our cooling system to control the motion of an SMA spring actuated one-DOF MINIR-II robot prototype.