Recently, Multimodal Large Language Models (MLLMs) have demonstrated their immense potential in computer-aided diagnosis and decision-making. In the context of robotic-assisted surgery, MLLMs can serve as effective tools for surgical training and guidance. However, there is still a deficiency of MLLMs specialized for surgical scene understanding in endoscopic procedures. To this end, we present EndoChat, an MLLM tailored to address various dialogue paradigms and subtasks in understanding endoscopic procedures. To train our EndoChat, we construct the Surg-396K dataset through a novel pipeline that systematically extracts surgical information and generates structured annotations based on large-scale endoscopic surgery datasets. Furthermore, we introduce a multi-scale visual token interaction mechanism and a visual contrast-based reasoning mechanism to enhance the model's representation learning and reasoning capabilities. Our model achieves state-of-the-art performance across five dialogue paradigms and seven surgical scene understanding tasks. Additionally, we conduct evaluations with professional surgeons, who provide positive feedback on the majority of conversation cases generated by EndoChat. Overall, these results demonstrate that EndoChat has the potential to advance training and automation in robotic-assisted surgery. Our dataset and model are publicly available at https://github.com/gkw0010/EndoChat.

Recent advances in surgical robotics and computer vision have greatly improved intelligent systems' autonomy and perception in the operating room (OR), especially in endoscopic and minimally invasive surgeries. However, for open surgery, which is still the predominant form of surgical intervention worldwide, there has been relatively limited exploration due to its inherent complexity and the lack of large-scale, diverse datasets. To close this gap, we present OpenSurgery, by far the largest video-text pretraining and evaluation dataset for open surgery understanding. OpenSurgery consists of two subsets: OpenSurgery-Pretrain and OpenSurgery-EVAL. OpenSurgery-Pretrain consists of 843 publicly available open surgery videos for pretraining, spanning 102 hours and encompassing over 20 distinct surgical types. OpenSurgery-EVAL is a benchmark dataset for evaluating model performance in open surgery understanding, comprising 280 training and 120 test videos, totaling 49 hours. Each video in OpenSurgery is meticulously annotated by expert surgeons at three hierarchical levels of video, operation, and frame to ensure both high quality and strong clinical applicability. Next, we propose the Hierarchical Surgical Knowledge Pretraining (HierSKP) framework to facilitate large-scale multimodal representation learning for open surgery understanding. HierSKP leverages a granularity-aware contrastive learning strategy and enhances procedural comprehension by constructing hard negative samples and incorporating a Dynamic Time Warping (DTW)-based loss to capture fine-grained temporal alignment of visual semantics. Extensive experiments show that HierSKP achieves state-of-the-art performance on OpenSurgegy-EVAL across multiple tasks, including operation recognition, temporal action localization, and zero-shot cross-modal retrieval. This demonstrates its strong generalizability for further advances in open surgery understanding.

Test-time adaptation with pre-trained vision-language models, such as CLIP, aims to adapt the model to new, potentially out-of-distribution test data. Existing methods calculate the similarity between visual embedding and learnable class embeddings, which are initialized by text embeddings, for zero-shot image classification. In this work, we first analyze this process based on Bayes theorem, and observe that the core factors influencing the final prediction are the likelihood and the prior. However, existing methods essentially focus on adapting class embeddings to adapt likelihood, but they often ignore the importance of prior. To address this gap, we propose a novel approach, Bayesian Class Adaptation (BCA), which in addition to continuously updating class embeddings to adapt likelihood, also uses the posterior of incoming samples to continuously update the prior for each class embedding. This dual updating mechanism allows the model to better adapt to distribution shifts and achieve higher prediction accuracy. Our method not only surpasses existing approaches in terms of performance metrics but also maintains superior inference rates and memory usage, making it highly efficient and practical for real-world applications.

Continuum robots offer high flexibility and multiple degrees of freedom, making them ideal for navigating narrow lumens. However, accurately modeling their behavior under large deformations and frequent environmental contacts remains challenging. Current methods for solving the deformation of these robots, such as the Model Order Reduction and Gauss-Seidel (GS) methods, suffer from significant drawbacks. They experience reduced computational speed as the number of contact points increases and struggle to balance speed with model accuracy. To overcome these limitations, we introduce a novel finite element method (FEM) named Acc-FEM. Acc-FEM employs a large deformation quasi-static finite element model and integrates an accelerated solver scheme to handle multi-contact simulations efficiently. Additionally, it utilizes parallel computing with Graphics Processing Units (GPU) for real-time updates of the finite element models and collision detection. Extensive numerical experiments demonstrate that Acc-Fem significantly improves computational efficiency in modeling continuum robots with multiple contacts while achieving satisfactory accuracy, addressing the deficiencies of existing methods.

Surgical phase recognition is critical for assisting surgeons in understanding surgical videos. Existing studies focused more on online surgical phase recognition, by leveraging preceding frames to predict the current frame. Despite great progress, they formulated the task as a series of frame-wise classification, which resulted in a lack of global context of the entire procedure and incoherent predictions. Moreover, besides online analysis, accurate offline surgical phase recognition is also in significant clinical need for retrospective analysis, and existing online algorithms do not fully analyze the entire video, thereby limiting accuracy in offline analysis. To over-come these challenges and enhance both online and offline inference capabilities, we propose a universal Surgical Phase LocalizAtion Network, named SurgPLAN++, with the principle of temporal detection. To ensure a global understanding of the surgical procedure, we devise a phase localization strategy for SurgPLAN ++ to predict phase segments across the entire video through phase proposals. For online analysis, to generate high-quality phase proposals, SurgPLAN++ incorporates a data augmentation strategy to extend the streaming video into a pseudo-complete video through mirroring, center-duplication, and down-sampling. For offline analysis, SurgPLAN++ capi-talizes on its global phase prediction framework to continu-ously refine preceding predictions during each online inference step, thereby significantly improving the accuracy of phase recognition. We perform extensive experiments to validate the effectiveness, and our SurgPLAN++ achieves remarkable performance in both online and offline modes, which outper-forms state-of-the-art methods. The source code is available at https://github.com/franciszchenlSurgPLAN-Plus.

Simultaneous Localization and Mapping (SLAM) is essential for precise surgical interventions and robotic tasks in minimally invasive procedures. While recent advancements in 3D Gaussian Splatting (3DGS) have improved SLAM with high-quality novel view synthesis and fast rendering, these systems struggle with accurate depth and surface reconstruction due to multi-view inconsistencies. Simply incorporating SLAM and 3DGS leads to mismatches between the reconstructed frames. In this work, we present Endo-2DTAM, a real-time endoscopic SLAM system with 2D Gaussian Splatting (2DGS) to address these challenges. Endo-2DTAM incorporates a surface normal-aware pipeline, which consists of tracking, mapping, and bundle adjustment modules for geometrically accurate reconstruction. Our robust tracking module combines point-topoint and point-to-plane distance metrics, while the mapping module utilizes normal consistency and depth distortion to enhance surface reconstruction quality. We also introduce a pose-consistent strategy for efficient and geometrically coherent keyframe sampling. Extensive experiments on public endoscopic datasets demonstrate that Endo-2DTAM achieves an RMSE of$1.87 \pm 0.63 \mathbf{m m}$for depth reconstruction of surgical scenes while maintaining computationally efficient tracking, high-quality visual appearance, and real-time rendering. Our code will be released at github.com/lastbasket/Endo-2DTAM.

Haptic feedback enhances user interaction with systems by adding the sense of touch, thereby improving immersion and realism in applications like virtual reality (VR), augmented reality (AR), video games, education, and robotic surgery. To address the challenges in mechanically actuated haptic feedback devices such as limited mobility, mechanical wear, and complex mechanical structures, several research sought to develop electromagnetic haptic feedback systems. However, they also suffer from the rapid decay of magnetic force with distance, thus restricting their workspace size and application potential. In this paper, we propose a novel electromagnetic haptic feedback device that is actuated by magnetic torque instead of magnetic force. By controlling the magnetic torque, which decays with distance only at a thirdorder rate, our device achieves a large workspace—a 200-mm-diameter hemisphere—while still delivering perceptible realtime haptic feedback within the hemisphere. While using the device, the user wears a lightweight haptic thimble housing a permanent magnet on their finger, which enables 2 degree-offreedom (DoF) haptic feedback. A 13-coil electromagnet array serves as the source of the magnetic field. A mathematical model is proposed to determine the currents in the electromagnet array to generate the desired amount of haptic feedback torque. We conducted two experiments to prove the viability of the device. A haptic feedback accuracy experiment was conducted and validated the device's ability to generate sufficient torque within a large workspace. A user evaluation experiment showed that the device achieved an overall accuracy of 77.86% in a virtual enclosure exploration task, indicating its effectiveness and usability in haptic feedback applications.

Localizing the bronchoscope in real time is essential for ensuring intervention quality. However, most existing vision-based methods struggle to balance between speed and generalization. To address these challenges, we present BronchoTrack, an innovative real-time framework for accurate branch-level localization, encompassing lumen detection, tracking, and airway association. To achieve real-time performance, we employ benchmark light weight detector for efficient lumen detection. We firstly introduce multi-object tracking to bronchoscopic localization, mitigating temporal confusion in lumen identification caused by rapid bronchoscope movement and complex airway structures. To ensure generalization across patient cases, we propose a training-free detection-airway association method based on a semantic airway graph that encodes the hierarchy of bronchial tree structures. Experiments on 11 patient datasets demonstrate BronchoTrack's localization accuracy of 81.72%, while accessing up to the 6th generation of airways. Furthermore, we tested BronchoTrack in an in-vivo animal study using a porcine model, where it localized the bronchoscope into the 8th generation airway successfully. Experimental evaluation underscores BronchoTrack's real-time performance in both satisfying accuracy and generalization, demonstrating its potential for clinical applications.

The surgical intervention is crucial to patient healthcare, and many studies have developed advanced algorithms to provide understanding and decision-making assistance for surgeons. Despite great progress, these algorithms are developed for a single specific task and scenario, and in practice require the manual combination of different functions, thus limiting the applicability. Thus, an intelligent surgical assistant is expected to accurately understand the surgeon’s intentions and accordingly conduct the specific tasks to support the surgical process. In this work, by improving advanced multimodal large language models (MLLMs), we propose a multimodal Surgical Function Calling (SurgFC) framework that can accurately understand the surgeon’s intention and complete a series of surgical understanding tasks, e.g., surgical scene analysis, surgical instrument detection, and segmentation on demand. Specifically, to achieve superior surgical multimodal understanding, we devise a mixture-of-projectors (MOP) module to align the surgical MLLM in SurgFC to balance the natural and surgical knowledge. Moreover, we devise a surgical Function-Calling Tuning strategy to enable the SurgFC to understand surgical intentions, and thus make a series of surgical function calls on demand to meet the needs of the surgeons. Extensive experiments on neurosurgery data confirm that our SurgFC can understand the surgeon’s intention more accurately than the existing MLLM, resulting in overwhelming performance in textual analysis and visual tasks. The source code is available at https://github.com/franciszchen/SurgFC.

AI-assisted operating room scene understanding is essential for the next generation of surgical interventions. Surgical instrument localization plays an important role in this context. However, existing instrument localization methods primarily focus on surgical instrument localization in endoscopy images and struggle with occlusions in broader operating room scenarios. In this work, we propose a weakly supervised instance segmentation framework, Pixel-driven Weakly-supervised Instance Segmentation (PWISeg), to solve the occluded instrument localization with low-cost annotations. Specifically, We utilize the projection relationship between the bounding box and the surgical instrument mask as a supervision signal to train PWISeg to predict coarse masks of surgical instruments. Then, we use the annotation of a few pixel points to train PWISeg to predict accurate masks of surgical instruments. To extensively validate the effectiveness, we collect and release a high-quality dataset, Surg-Inst that covers real-world hard cases of overlapping, dense placement, and various levels of instrument occlusion. Experiments demonstrate that our PWISeg achieves a remarkable performance advantage over state-of-the-art methods on both Surg-Inst and public HOSPI-Tools datasets.

Traditional rigid endoscopes have challenges in flexibly treating tumors located deep in the brain, and low operability and fixed viewing angles limit its development. This study introduces a novel dual-segment flexible robotic endoscope MicroNeuro, designed to perform biopsies with dexterous surgical manipulation deep in the brain. Taking into account the uncertainty of the control model, an image-based visual servoing with online robot Jacobian estimation has been implemented to enhance motion accuracy. Furthermore, the application of model predictive control with constraints significantly bolsters the flexible robot’s ability to adaptively track mobile objects and resist external interference. Experimental results underscore that the proposed control system enhances motion stability and precision. Phantom testing substantiates its considerable potential for deployment in neurosurgery.

Transorbital Neuro Surgery (TNS) offers a novel treatment towards the lesion inside skull pursuing minimal invasiveness. Most conventional TNS tools are rigid and straight, limiting the dexterity and accessibility in passing a small port. Bendable and steerable surgical tools provides an alternative for this issue. In this work, we proposed a dual-segment slender surgical robot arm for TNS, which is a Co-Axial Slender Tubular robot (CAST), and modelled it using novel approaches. Another contribution is tendon-mortise shaped slits along the axial direction, enhancing the overall stiffness. The bending of CAST is actuated by pushing/pulling distance, and the maximum diameter is only 1.7mm with high dexterity after mounting on a rigid robot arm. Experiments demonstrates that the proposed the slit design doubles the stiffness properties compared to traditional rectangle slit designs. The path-following task shows that the position error was maximally 3mm in open-looped control. Test on a skull model demonstrates that the whole system could successfully perform electrocoagulation procedure inside the depth of skull in a robotized manner effectively.

Severe chronic pulmonary endarterectomy needs a dissector to delicately remove proliferative intima located in the depth of the pulmonary artery. This work proposed a novel endoscopic robotized steerable dissector for this surgery, enabling easier access to curved deep artery branches. The handheld surgical dissector also provides suction and visualization for surgeons to enhance effectiveness. The steerable section is a cable-driven hinged structure, and through an antagonistic mechanism regulating the cable tension, the overall stiffness is adjusted to adapt various surroundings. The mapping between actuation space and shape configuration and tip force estimation model are respectively established for further closed-loop control scheme, achieving adaptive positioning and safe surgery. Experiments first demonstrate the feasibility of the proposed models and ex vitro trials validated the usage and effectiveness of the robotized dissector.

Surgical instrument segmentation is crucial in surgical scene understanding, thereby facilitating surgical safety. Existing algorithms directly detected all instruments of predefined categories in the input image, lacking the capability to segment specific instruments according to the surgeon’s intention. During different stages of surgery, surgeons exhibit varying preferences and focus toward different surgical instruments. Therefore, an instrument segmentation algorithm that adheres to the surgeon’s intention can minimize distractions from irrelevant instruments and assist surgeons to a great extent. The recent Segment Anything Model (SAM) reveals the capability to segment objects following prompts, but the manual annotations for prompts are impractical during the surgery. To address these limitations in operating rooms, we propose an audio-driven surgical instrument segmentation framework, named ASI-Seg, to accurately segment the required surgical instruments by parsing the audio commands of surgeons. Specifically, we propose an intention-oriented multimodal fusion to interpret the segmentation intention from audio commands and retrieve relevant instrument details to facilitate segmentation. Moreover, to guide our ASI-Seg segment of the required surgical instruments, we devise a contrastive learning prompt encoder to effectively distinguish the required instruments from the irrelevant ones. Therefore, our ASI-Seg promotes the workflow in the operating rooms, thereby providing targeted support and reducing the cognitive load on surgeons. Extensive experiments are performed to validate the ASI-Seg framework, which reveals remarkable advantages over classical state-of-the-art and medical SAMs in both semantic segmentation and intention-oriented segmentation. The source code is available at https://github.com/Zonmgin-Zhang/ASI-Seg.

Real-time 6 DOF localization of bronchoscopes is crucial for enhancing intervention quality. However, current vision-based technologies struggle to balance between generalization to unseen data and computational speed. In this study, we propose a Depth-based Dual-Loop framework for real-time Visually Navigated Bronchoscopy (DD-VNB) that can generalize across patient cases without the need of re-training. The DD-VNB framework integrates two key modules: depth estimation and dual-loop localization. To address the domain gap among patients, we propose a knowledge-embedded depth estimation network that maps endoscope frames to depth, ensuring generalization by eliminating patient-specific textures. The network embeds view synthesis knowledge into a cycle adversarial architecture for scale-constrained monocular depth estimation. For real-time performance, our localization module embeds a fast ego-motion estimation network into the loop of depth registration. The ego-motion inference network estimates the pose change of the bronchoscope in high frequency while depth registration against the pre-operative 3D model provides absolute pose periodically. Specifically, the relative pose changes are fed into the registration process as the initial guess to boost its accuracy and speed. Experiments on phantom and in-vivo data from patients demonstrate the effectiveness of our framework: 1) monocular depth estimation outperforms SOTA, 2) localization achieves an accuracy of Absolute Tracking Error (ATE) of 4.7 ± 3.17 mm in phantom and 6.49 ± 3.88 mm in patient data, 3) with a frame-rate approaching video capture speed, 4) without the necessity of case-wise network retraining. The framework’s superior speed and accuracy demonstrate its promising clinical potential for real-time bronchoscopic navigation.

Bronchoscopy plays a significant role in the early diagnosis and treatment of lung diseases. This process demands physicians to maneuver the flexible endoscope for reaching distal lesions, particularly requiring substantial expertise when examining the airways of the upper lung lobe. With the development of artificial intelligence and robotics, reinforcement learning (RL) method has been applied to the manipulation of interventional surgical robots. However, unlike human physicians who utilize multimodal information, most of the current RL methods rely on a single modality, limiting their performance. In this paper, we propose BronchoCopilot, a multimodal RL agent designed to acquire manipulation skills for autonomous bronchoscopy. BronchoCopilot specifically integrates images from the bronchoscope camera and estimated robot poses, aiming for a higher success rate within challenging airway environment. We employ auxiliary reconstruction tasks to compress multimodal data and utilize attention mechanisms to achieve an efficient latent representation of this data, serving as input for the RL module. This framework adopts a stepwise training and fine-tuning approach to mitigate the challenges of training difficulty. Our evaluation in the realistic simulation environment reveals that BronchoCopilot, by effectively harnessing multimodal information, attains a success rate of approximately 90% in fifth generation airways with consistent movements. Additionally, it demonstrates a robust capacity to adapt to diverse cases.

Current imaging of the artery relies primarily on computed tomography angiography (CTA), which requires contrast injections and exposure to radiation. In this paper, we present a method for fully autonomous artery 3D image acquisition using a linear ultrasound (US) probe and a 6 DoFs robot arm with a 3D camera. Robotic vessel acquisition can minimize tissue deformation and permit the reproduction of scans. Additionally, the robotic-based acquisition can provide more precise vessel position data that can be utilized for 3D reconstruction as a preoperative image. The first scanning point is determined by the 3D camera using a neural network for leg area estimation. A visual servo algorithm adjusts the in-plane motions using a cross-sectional vessel segmentation produced by a neural network with a UNet structure, while a US confidence map regulates the in-plane rotation. The robot is equipped with impedance control to maintain a constant and safe scan. Experiments on a leg phantom and a volunteer indicate that the robot can follow the vessel and modify its position to provide a sharper US image. The average error of phantom scanning in y-axis and z-axis are 0.2536mm and 0.2928mm, respectively, while the root means square error (RMSE) of contact force in the volunteer experiment is 0.2664N. In addition, a 3D vessel reconstruction demonstrates the possibility of robotic US acquisition as a preoperative image.

Optical colonoscopy is the gold standard for colorectal cancer screening, however even under optimal conditions only 81% of the internal tissue is inspected, in part causing up to 22% of early adenomas to be missed. Blindspots commonly occur at acute bends where the camera’s view is blocked. 3D reconstruction alone is insufficient to assess screening completeness as predictions of both the seen and unseen mucosa are required. Existing works in blindspot detection nevertheless use a highly detailed 3D reconstruction as the first step, the complexity of which degrades processing speed and reliability. We demonstrate that this complexity is not needed to predict whether acute bends have been adequately inspected. We propose a parametric model of the colon with only 2 variables: radius and curvature. By incorporating curvature, our method can predict the occlusion which acute bends cause. We use CT scans from 12 patients which on average contain 20 bends. We use a custom colonoscopy simulator and, assuming known geometry, show that a curved model reliably predicts these blindspots while a non-curved model always misses them. From our frame-by-frame predictions we build a panoramic map of the tissue inspected over the procedure. We show that this correctly identifies all 10 blindspots during 3 minutes of colonoscopy. We envisage that by alerting clinicians to mucosa which they have missed in real time, they can revisit these areas, improving the detection of polyps and cancers.

Endoscopic surgery is an increasingly popular alternative to laparoscopic techniques for many conditions, as the operation site can be reached without skin wounds. In many tasks, sufficient force generation is desired. As endoscopes must be highly flexible and slim, however, the force generation and sensing capabilities associated with these tools is limited due their compliance, significantly hindering the adoption rate of endoscopic surgeries. This paper proposes a technique, termed `environmental scaffolding', to stabilize an actuated, flexible segment in the intestine such that larger forces can be applied. Through the measurement of actuation forces, a method for intrinsically sensing multiple contact forces when in this configuration is presented. Experimental results show that with the environmental scaffolding technique, the tip force generated can be increased by over 50% on average compared to using the device in a purely cantilevered configuration, and the tip force estimation is accurate to within 2.97%.

Intrinsic Tactile Sensing (ITS) is a well-established technique, relying on force/torque and geometric surface description to find contact centroids. The method works well for rigid surfaces. However, finding a solution for deformable surfaces is an open issue. This work presents two solutions to extend ITS to deformable surfaces, relying on force-deformation characteristics of the surface under exploration: (i) a closed-form approach that calculates the contact centroid using standard ITS, but on a shrunk geometry approximating the deformed surface; (ii) an iterative procedure that takes into account soft surface deformation, and force/torque equilibrium to minimize a cost function. We have tested both using ellipsoid silicone specimens, with different softness levels and indented along different directions. Both linear and quadratic fitting for the force-indentation behavior were employed. The two methods have distinct advantages and limitations. However, a combination of two methods, using one to produce the initial guess for the other, turns out to be very effective. Indeed, in our validation this solution showed convergence under 1ms, attaining errors lower than 1 mm. The proposed approaches were implemented in a ROS-based toolbox, integrating both solutions.

Knowledge of externally applied forces is crucial for compliant robotic manipulators in minimally-invasive and endoluminal robotic surgery for both patient safety and controllability of the device. We developed a novel continuum manipulator which comprises hydraulic actuation. In this work we investigate the use of the hydrostatic pressure feedback inside the inflatable actuation chambers to determine the normal and shear forces which are applied to the tip of the robot. For that purpose a nonlinear finite element model is derived and experimentally validated, showing a good approximation between experiment and simulation. The model is then used to derive descriptions for the normal and shear forces applied to the robot tip. The normal force estimation shows good results over the range of experimentally validated tip angles, while the shear force estimation shows good results for small tip deflection angles with an increasing error, with the tip orientation. The algorithm indicates good applicability to force control tasks as the forces are fast to compute.

Medical ultrasound imaging robotics systems often comprise of complex control architectures and hardware integration to enable safe human robot interaction. In this paper, we investigate the applicability of a soft robotic end-effector for ultrasound intervention. A novel, parallel design is derived based on the medical requirements, which addresses common shortcomings in both robotic ultrasound systems and soft robotic devices. Individual actuators are developed and characterized and the performance of the overall platform is evaluated in regards to its stiffness and steerability. It is shown that the platform comprises relatively high longitudinal and transversal stiffness, while still being compliant enough to ensure the safety of the patient. The shear stiffness of the platform is 4.2 times greater than the shear stiffness of an individual actuator. The platform is capable of applying loadings of 10N along its longitudinal axis, which makes it suitable for the given application. Furthermore, the workspace of the platform is suitable to robot-guided ultrasound with a maximum platform rotation range of ±14.8°, while only moving ±7mm in space.

In this paper, a potential-field-based real-time path planning algorithm for a multi-segment continuum manipulator is proposed. This planner is employed to enable a continuum-style manipulator to move autonomously in dynamic environments in real-time. The classic potential field method is modified to make it applicable for a kinematics model based on the constant-curvature assumption. The contribution of this paper lies in the design of a novel potential field in the actuator space satisfying the mechanical constraints of the manipulator. The planning algorithm is tested and validated in real-time simulation for a 3 segments continuum manipulator. Preliminary tests for a tendon-driven single-segment continuum manipulator prototype confirm the performance of the proposed planner.

In this paper, we present a novel pose estimation and obstacle avoidance approach for tendon-driven multi-segment continuum manipulators moving in dynamic environments. A novel multi-stage implementation of an Extended Kalman Filter is used to estimate the pose of every point along the manipulator's body using only the position information of each segment tip. Combined with a potential field, the overall algorithm will guide the manipulator tip to a desired target location and, at the same time, keep the manipulator body safe from collisions with obstacles. The results show that the approach works well in a real-time simulation environment that contains moving obstacles in the vicinity of the manipulator.

Contact force play is a significant role in success of the cardiac ablation. However, it is still challenging to estimate contact force when a catheter is under large bending and multiple contacts. This paper develops a new multi-section static model of the tendon-driven catheters for both real-time intrinsic force sensing and interaction control. The model allows estimating the catheter shape by the external force at arbitrary location. Also, an algorithm is developed for the contact force estimation using the shape estimation with the catheter end-position tracking and tension feedback. In this study, we validated the contact force and shape estimation using a robotic platform, which steers a catheter consisting of 4 tendons with tension feedback. The shape estimation results show that the model can accurately predict the catheter shape; the position difference between measured and estimated was 2.5mm. The results of the contact force estimation show that 3-dimensional contact forces can be estimated accurately using the proposed method. The magnitude of contact force error was 0.0117N with 350Hz update rate.

This work investigates the applicability of stiffness-based modelling in soft robotics manipulation. The methodology is introduced and applied to model a soft robotics manipulator as single 3d Timoshenko beam element. The model is then utilized to solve the forward kinematics problem for the manipulator. The algorithm is validated comparing the simulated deflection with the deflection of the physical manipulator for two defined pressure sequences. It is shown that the model behaves in a highly similar fashion in comparison to the manipulator. For both trajectories the maximum position error is close to 6 mm while the error in orientation not more than 18°. The methodology as described in this work reveals great applicability to the field of soft robots being limited only by the stiffness matrix assembly for the given system. Implementations of inverse kinematics and the effects of external force applications are effectively integrable in the described theory.

Tactile data and kinesthetic cues are two important sensing sources in robot object recognition and are complementary to each other. In this paper, we propose a novel algorithm named Iterative Closest Labeled Point (iCLAP) to recognize objects using both tactile and kinesthetic information. The iCLAP first assigns different local tactile features with distinct label numbers. The label numbers of the tactile features together with their associated 3D positions form a 4D point cloud of the object. In this manner, the two sensing modalities are merged to form a synthesized perception of the touched object. To recognize an object, the partial 4D point cloud obtained from a number of touches iteratively matches with all the reference cloud models to identify the best fit. An extensive evaluation study with 20 real objects shows that our proposed iCLAP approach outperforms those using either of the separate sensing modalities, with a substantial recognition rate improvement of up to 18%.

This paper presents a methodology for the development of a multi-axis force/torque sensor based on optoelectronic technology. The advantages of using this sensing principle are the low manufacturing costs, the simple fabrication, and the immunity to electrical noise. The force/ torque sensor makes use of six optical sensors: each sensor measures the displacement of a reflective surface that moves integrally with a simply-supported beam. The proposed mechanical structure allows for a variety of shapes on the mechanical structure to be easily adaptable to many robot applications. In this paper, we present a five-axis force/torque sensor based on this optoelectronic principle. To measure force/torque components, two identical three-DoF force/torque sensor structures (comprised of three beams) are mounted on top of each other. Photo sensors and mirrors are fixed inside the structure to measure the six beam deflections. In this paper, we describe the sensor structure, design, fabrication, calibration, and verify our sensor development methodology.

Humans can hold a live animal like a hamster without overly squeezing despite the fact that its soft body undergoes impedance and size variations due to breathing and wiggling. Although the exact nature of such biological motor controllers is not known, existing literature suggests that they maintain metastable interactions with dynamic objects based on prediction rather than reaction. Most robotic gripper controllers find such tasks very challenging mainly due to hard constraints imposed on the stability of closed-loop control and inadequate rates of convergence of adaptive controller parameters. This paper presents experimental and numerical simulation results of a control law based on a relaxed stability criterion of reducing the probability of failure to maintain a stable grip on a soft object that undergoes temporal variations in its internal impedance. The proposed controller uses only three parameters to interpret the probability of failure estimated using a history of grip forces to adjust the grip on the dynamic object. Here, we demonstrate that the proposed controller can maintain smooth and stable grip tightening and relaxing when the object undergoes random impedance variations, compared with a reactive controller that involves a similar number of controller parameters.

This paper presents a novel framework for integration of vision and tactile sensing by localizing tactile readings in a visual object map. Intuitively, there are some correspondences, e.g., prominent features, between visual and tactile object identification. To apply it in robotics, we propose to localize tactile readings in visual images by sharing same sets of feature descriptors through two sensing modalities. It is then treated as a probabilistic estimation problem solved in a framework of recursive Bayesian filtering. Feature-based measurement model and Gaussian based motion model are thus built. In our tests, a tactile array sensor is utilized to generate tactile images during interaction with objects and the results have proven the feasibility of our proposed framework.

The absence of touch of sense is a widely known drawback of robotic minimally invasive surgery (MIS). This paper proposes a design of optic soft tactile arrays which is promising to be adapted for MIS. The proposed design consists of multiple soft material channels. Each channel is designed using the Bernoulli pipe structure to amplify the sensor's sensitivity through input and output diameter difference. A multi-core optic fiber cable and a camera are used to capture the change of light intensity caused by the contact forces applied onto the individual soft material channels. The proposed sensor has the following advantages: 1) making use of 3D printing and soft material casting, it is suitable for designing sensors with high density of tactile elements; 2) it also allows the sensor to be designed in an arbitrary shape to fit various MIS applications; 3) compared to other light-intensity based tactile sensor, it is easy to fabricate and miniaturize; it avoids the complexity of attaching reflectors to individual sensing elements; 4) it is immune to electromagnetic interference. In this paper, a prototype which has 3×3 tactile elements in an area of 9.5 × 11 mm2has been developed and test for feasibility study. Also, a noise-filtering algorithm is developed to reduce the imaging noise. Validation experiments were carried out and results show that the average measurable force range for a single tactile element is 0 to 1.622N with an average accuracy of 97%. The sensor has low crosstalk-to-signal ratio, 1.8% on average, and has no signal drift over time.

This paper proposes a novel Cosserat rod model based method to estimate contact forces based on the shape analysis of the catheter. We simplify the original Cosserat rod model to achieve real-time estimation of the force magnitude and directions applied to the catheter tip. The simplified model contains a set of arithmetic equations, which can be rapidly solved using an iterative optimization algorithm. Experimental evaluation shows that the computational frequency of the force estimation was found to be 33.7Hz. Both the magnitude and the direction of the contact force were accurately estimated. The accuracy of the estimations of magnitude was 89.50%, and for the contact direction was 88.13%. Mean errors of the contact force and the contact angle are 7 × 10-4Nand 0.931 degree respectively. To prove the concept, the catheter shape is detected through a RGB camera. However, the proposed method can be easily applied to existing catheter gating and detecting methods using the medical imaging environments such as X-ray fluoroscopy, CT, ultrasound, magnetic field and MRI. The results of evaluation experiments demonstrate what the proposed method is promising for force estimation without the need of a physical force sensor in various types of catheter tips.

This paper presents a highly compact fibre-optic system based on light intensity modulation for multi-segment flex sensing in pliable robot arms, e.g., articulated surgical instruments. This fibre-optic arrangement is 7.5 mm in diameter and is comprised of a two-segment flexible and stretchable Steiner chain arm section with twelve housings at the distal side which accommodates passive cables. The displacement of each cable will be used to determine the bending. This Steiner chain section is followed by a basal rigid fibre-optic sensing unit integrated with a low-friction retractable distance modulation array which couples the motion of the passive cables with light-emitting optical fibres. The low-friction retractable distance modulation array uses steel spring-needle double sliders to reduce the hysteresis and to recover reference sensor values when the arm returns to its original straight configuration. The U-shape loopback design of the optical fibres allows integration of all electronics away from the sensing site. The experimental results indicate a maximum bending angle error of 6° in one individual segment of the two-segment arm with respect to reference angle values calculated from camera images.

To efficiently explore a surface using the sense of touch, a novel contact sensing finger was created and a surface following control algorithm for the finger was devised. Based on the accurate estimation of contact locations, and the direction and magnitude of the normal and tangential forces, the finger can robustly and rapidly follow surfaces with large change in curvature while maintaining a desired constant normal force. In this paper, the design and testing of the contact sensing finger are presented and the control algorithm for surface contour following is proposed and validated using objects with different shapes and surface materials. The results demonstrate that using the developed finger and the control algorithm, a surface can be efficiently explored with rapid sliding speed. To demonstrate the potential applications of the proposed approach, the friction properties of an explored object surface are computed and, for a known object, its pose is estimated.

This paper presents an innovative approach of utilising visual feedback to determine physical interaction forces with soft tissue during Minimally Invasive Surgery (MIS). This novel force sensing device is composed of a linear retractable mechanism and a spherical visual feature. The sensor mechanism can be adapted to endoscopic cameras used in MIS. As the distance between the camera and feature varies due to the sliding joint, interaction forces with anatomical surfaces can be computed based on the visual appearance of the feature in the image. Hence, this device allows the measurement of forces without introducing new stand-alone sensors. A mathematical model was derived based on validation data tests and preliminary experiments were conducted to verify the model's accuracy. Experimental results confirm the effectiveness of our vision based approach.

This paper introduces an optical based three axis force sensor which can be integrated with the robot arm of the EU project STIFF-FLOP (STIFFness controllable Flexible and Learnable Manipulator for Surgical Operations) in order to measure applied external forces. The structure of the STIFF-FLOP arm is free of metal components and electric circuits and, hence, is inherently safe near patients during surgical operations. In addition, this feature makes the performance of this sensing system immune against strong magnetic fields inside magnetic resonance (MR) imaging scanners. The hollow structure of the sensor allows the implementation of distributed actuation and sensing along the body of the manipulator. In this paper, we describe the design and calibration procedure of the proposed three axis optics-based force sensor. The experimental results confirm the effectiveness of our optical sensing approach and its applicability to determine the force and momentum components during the physical interaction of the robot arm with its environment.

This paper introduces a novel continuum-style robot that integrates multiple layers of compliant modules. Its essential features lie in that its bending is not based on natural compliance of a continuous backbone element or soft skeletal elements but instead is based on the compliance of each structured planar module. This structure provides several important advantages. First, it demonstrates a large linear bending motion, whilst avoiding joint friction. Second, its contraction and bending motion are decoupled. Third, it possesses ideal back-drivability and a low hysteresis. We further provide an analytical method to study the compliance characteristics of the planar module and derive the statics and kinematics of the robot. The paper provides an overview of experiments validating the design and analysis.

The break-away friction ratio (BF-ratio), which is the ratio between friction force and the normal force at slip occurrence, is important for the prediction of incipient slip and the determination of optimal grasping forces. Conventionally, this ratio is assumed constant and approximated as the static friction coefficient. However, this ratio varies with acceleration rates and force rates applied to the grasped object and the object material, which lead to difficulties in determining optimal grasping forces that avoid slip. In this paper, we propose a novel approach based on the interactive forces to allow a robotic hand to predict object slip before its occurrence. The approach only requires the robotic hand to have a short haptic surface exploration over the object surface before manipulating it. Then, the frictional properties of the finger-object contact can be efficiently identified, and the BF-ratio can be real-time predicted to predict slip occurrence under dynamic grasping conditions. Using the predicted BF-ratio as a slip, threshold is demonstrated to be more accurate than using the static/Coulomb friction coefficient. The presented approach has been experimentally evaluated on different object surfaces, showing good performance in terms of prediction accuracy, robustness, and computational efficiency.

Flexible manipulators have promising applications in minimally invasive surgery as it allows the surgical tools reach targets which are prohibited by conventional rigid surgical instrument. However one of the technical difficulties of implementing the flexible manipulator is to measure the bending curvature. This paper proposes the design of a novel optical sensor for measuring the bending curvature of a flexible manipulator based on light intensity modulation. The sensor is low cost and is temperature independent. A theoretical model of using the sensor design to deduce the curvature of a flexible robot has been created. Implementing the proposed theoretical model, the developed sensor has been used to measure the bend of a section of a flexible segment. Validation tests have been carried out; the results demonstrate that the developed sensor has good accuracy in measuring the bending angles, the orientation of the bending and the bending radius.

Robot grasping and manipulation relies mainly on two types of sensory data: vision and tactile sensing. Localisation and recognition of the object is typically done through vision alone, while tactile sensors are commonly used for grasp control. Vision performs reliably in uncluttered environments, but its performance may deteriorate when the object is occluded, which is often the case during a manipulation task, when the object is in-hand and the robot fingers stand between the camera and the object. This paper presents a method to use the robot's sense of touch to refine the knowledge of a manipulated object's pose from an initial estimate provided by vision. The objective is to find a transformation on the object's location that is coherent with the current proprioceptive and tactile sensory data. The method was tested with different object geometries and proposes applications where this method can be used to improve the overall performance of a robotic system. Experimental results show an improvement of around 70% on the estimate of the object's location when compared to using only vision.

This paper presents a novel fiber optic tactile probe designed for tissue palpation during minimally invasive surgery (MIS). The probe consists of 3×4 tactile sensing elements at 2.6mm spacing with a dimension of 12×18×8 mm3allowing its application via a 25mm surgical port. Each tactile element converts the applied pressure values into a circular image pattern. The image patterns of all the sensing elements are captured by a camera attached at the proximal end of the sensor system. Processing the intensity and the area of these circular patterns allows the computation of the applied pressure across the sensing array. Validation tests show that each sensing element of the tactile probe can measure forces from 0 to 1N with a resolution of 0.05 N. The proposed sensing concept is low cost, lightweight, sterilizable, easy to be miniaturized and compatible for magnetic resonance (MR) environments. Experiments using the developed sensor for tissue abnormality detection were conducted. Results show that the proposed tactile probe can accurately and effectively detect nodules embedded inside soft tissue, demonstrating the promising application of this probe for surgical palpation during MIS.

This paper introduces a new and low-cost tissue stiffness simulation technique for surgical training and robot-assisted minimally invasive surgery (RMIS) with pseudo-haptic feedback based on tissue stiffness maps provided by rolling mechanical imaging. Superficial palpation and deep palpation pseudo-haptic simulation methods are presented. Although without expensive haptic interfaces users receive only visual feedback (pseudo-haptics) when maneuvering a cursor over the surface of a virtual soft-tissue organ by means of an input device such as a mouse, a joystick, or a touch-sensitive tablet, the alterations to the cursor behavior induced by the method creates the experience of actual interaction with a tumor in the users' minds. The proposed methods are experimentally evaluated for tissue abnormality identification. It is shown that users can recognize tumors with these two methods and the rate of correctly recognized tumors in deep palpation pseudo-haptic simulation is higher than superficial palpation simulation.

This paper proposes a new computationally fast algorithm for classifying the primitive shape and pose of the local contact area in real-time using a tactile array sensor attached on a robotic fingertip. The proposed approach abstracts the lower structural property of the tactile image by analyzing the covariance between pressure values and their locations on the sensor and identifies three orthogonal principal axes of the pressure distribution. Classifying contact shapes based on the principal axes allows the results to be invariant to the rotation of the contact shape. A naïve Bayes classifier is implemented to classify the shape and pose of the local contact shapes. Using an off-shelf low resolution tactile array sensor which comprises of 5×9 pressure elements, an overall accuracy of 97.5% has been achieved in classifying six primitive contact shapes. The proposed method is very computational efficient (total classifying time for a local contact shape = 576μs (1736 Hz)). The test results demonstrate that the proposed method is practical to be implemented on robotic hands equipped with tactile array sensors for conducting manipulation tasks where real-time classification is essential.

This paper investigates possible sources of variability in the dynamics of legged locomotion, even in its most idealized form. The rimless wheel model is a seemingly deterministic legged dynamic system, popular within the legged locomotion community for understanding basic collision dynamics and energetics during passive phases of walking. Despite the simplicity of this legged model, however, experimental motion capture data recording the passive step-to-step dynamics of a rimless wheel down a constant-slope terrain actually demonstrate significant variability, providing strong evidence that stochasticity is an intrinsic-and thus unavoidable-property of legged locomotion that should be modeled with care when designing reliable walking machines. We present numerical comparisons of several hypotheses as to the dominant source(s) of this variability: 1) the initial distribution of the angular velocity, 2) the uneven profile of the leg lengths and 3) the distribution of the coefficients of friction and restitution across collisions. Our analysis shows that the 3rd hypothesis most accurately predicts the noise characteristics observed in our experimental data while the 1st hypothesis is also valid for certain contexts of terrain friction. These findings suggest that variability due to ground contact dynamics, and not simply due to geometric variations more typically modeled in terrain, is important in determining the stochasticity and resulting stability of walking robots. Although such ground contact variability might be an expected result in field robotics on significantly rough terrain, we again note our experimental data applies seemingly deterministic-looking terrains: our results suggest that stochastic ground collision models should play an important role in the analysis and optimization of dynamic performance and stability in robot walking.

Maintaining the grip on an artery with a pulsating impedance, holding the steering wheel of a vehicle on a bumpy terrain, or holding a live hamster without excessive squeezing may be trivial tasks to most humans. However, a robot will find it very difficult to maintain the grip of such uncertain objects based on real-time feedback control. This paper presents a stochastic control law to maintain the grip on an uncertain object while manipulating against external forces. The radial impedance parameters of the soft object is assumed to undergo Gaussian random variations. Here we demonstrate that the proposed model free grip controller can maintain a safe grip at two diagonally opposite points of the object merely based on the statistics of the normal force. It accomplishes this by computing a probability of grip failure to adapt the compression on the soft object. A novel optimal estimation algorithm that can concurrently estimate the unknown impedance parameters of the object and the states of the coupled dynamic system is discussed as a potential tool to be used in predictive optimal impedance control on uncertain objects. Experimental results on adaptive grip control on a cylindrical tube inflated and deflated with a Gaussian random variation has been presented to validate the algorithm.

Object surface properties are among the most important information which a robot requires in order to effectively interact with an unknown environment. This paper presents a novel haptic exploration strategy for recognizing the physical properties of unknown object surfaces using an intelligent finger. This developed intelligent finger is capable of identifying the contact location, normal and tangential force, and the vibrations generated from the contact in real time. In the proposed strategy, this finger gently slides along the surface with a short stroke while increasing and decreasing the sliding velocity. By applying a dynamic friction model to describe this contact, rich and accurate surface physical properties can be identified within this stroke. This allows different surface materials to be easily distinguished even if when they have very similar texture. Several supervised learning algorithms have been applied and compared for surface recognition based on the obtained surface properties. It has been found that the naïve Bayes classifier is superior to radial basis function network and k-NN method, achieving an overall classification accuracy of 88.5% for distinguishing twelve different surface materials.

Slip prediction is important for maintaining the stability of object handling in robust grasping and dexterous manipulation. However, up to date a challenge still remains that how to accurately predict slip occurrence before it actually happens to allow robotic hands to conduct slip compensation in time. The concept of friction cone has been conventionally used to predict slip occurrence, where the static/kinetic friction coefficient is used as a threshold. However, this threshold, i.e. the ratio of the friction and normal forces at slip occurrence (also named as break-away friction ratio), is found not constant but varies with changes in acceleration and disturbing forces applied on the grasped object, raising difficulties when attempting to accurately predict slip. In this paper, we propose a novel approach to accurately predict varying slip thresholds in real time and compensate the predicted slip during a dynamic grasping. To achieve this, first a simple but efficient haptic surface exploration using robotic fingers is carried out to identify the friction properties of an object surface. Once the friction properties are established, the slip threshold at a given grasping condition can be predicted and the grasping forces are adjusted to prevent slip. The presented approach has been evaluated, showing good performance in terms of prediction accuracy and computational efficiency.

This paper presents a novel optical fiber-based rolling indentation probe designed to measure the stiffness distribution of a soft tissue while rolling over the tissue surface during minimally invasive surgery. By fusing the measurements along rolling paths, the probe can generalize a mechanical image to visualize the stiffness distribution within the internal tissue structure. Since tissue abnormalities are often firmer than the surrounding organ or parenchyma, a surgeon then can localize abnormalities by analyzing the image. The performance of the developed probe was validated using simulated soft tissues. Results show that the probe can measure both force and indentation depth accurately with different orientations when the probe approached and rolled on the tissue surface. In addition, experiments for tumor, identification through rolling indentation were conducted. The size and embedded depth of the tumor, as well as the stiffness ratio between the tumor and tissue, were varied during tests. Results demonstrate that the probe can effectively and accurately identify the embedded tumors.

This paper presents a novel miniaturized force-indentation depth (FID) sensor designed to conduct indentation on soft tissue during minimally invasive surgery. It can intra-operatively aid the surgeon to rapidly identify the tissue abnormalities within the tissue. The FID sensor can measure the indentation depth of a semi-spherical indenter and the tissue reaction force simultaneously. It make use of with fiber optical fiber sensing method measure indentation depth and force and is small enough to fit through a standard trocar port with a diameter of 11 mm. The created FID sensor was calibrated and tested on silicone block simulating soft tissue. The results show that the sensor can measure the indentation depth accurately and also the orientation of the sensor with respect to the tissue surface whilst performing indentation.

The authors have recently proposed the method of rolling indentation over soft tissue to rapidly identify soft tissue properties for localization and detection of tissue abnormalities, with the aim of compensating for the loss of haptics information experienced during robotic-assisted minimally invasive surgery (RMIS). This paper investigates the concept of rolling indentation using finite element modeling. To obtain ground truth data, rolling indentation experiments are conducted on a silicone phantom which contains three simulated tumours. The tissue phantom is modeled as hyperelastic material using ABAQUStrade. The identification of tumours includes two parts: firstly, when the spatial location of tumour is known, identify the tumour's mechanical properties (initial shear modulus); secondly if the mechanical properties of tumour are known, identify the tumour's spatial location. The results show that the proposed method can identify information of tumours accurately and robustly. The identified tumour mechanical properties and tumour locations are in good agreement with experimental measurements.

This paper proposes a novel approach for the identification of the internal structure and mechanical properties of biological soft tissue using a force sensitive wheeled probe to generate a 'mechanical image' by rolling across the surface of a solid organ. Initially, a testing facility for validating the concept ex-vivo was developed. Preliminary validation tests were carried out on a silicone phantom with embedded abnormalities with the aim to link the derived 'mechanical image' with the known internal structure. Ex-vivo validation tests were also conducted on excised porcine livers. The data were analyzed in four parts: 1) the dynamic analysis of wheel-tissue interaction to validate that the measured parameters are representative of underlying tissue stiffness; 2) the development of a 'mechanical image' from the rolling data; 3) a comparison of standard 1-DOF indentation testing with 2-DOF rolling and 4) the characterization of the relationship between force and tissue deflection from the data contained within the mechanical image. The results show that the 'rolling mechanical image' is capable of capturing information relating to the underlying tissue stiffness distribution and characterizing the force-tissue deflection profile for a tissue sample. Examples of scenarios, where this information could potentially be used, include providing a surgeon with the ability to probe solid organs in-vivo during robot-assisted MIS or providing prior information for the modeling of tool-tissue interactions, such as steerable needles.

This paper describes the preliminary design and construction of an optical fiber sensor which has been developed for evaluating the feasibility of using an optical-based force sensing methodology to investigate mechanical soft tissue properties during minimally invasive surgery. This sensor applies a novel reflective light intensity modulation scheme using bent-tip optical fibers and a reflector to measure mechanical response of the soft tissue when it interacts with the sensor. By adopting such optical fibers to detect minute deflection of a flexible cylindrical structure at the reflective edge of the reflector, good sensitivity for the force detection can be obtained. The prototype described in this document has demonstrated that it can detect the tissue interaction forces in the axial direction and identify variations in tissue stiffness. The maximum force range that can be detected by the sensor is 3 N. The measurement resolution is 0.02 N.

This paper proposes a novel approach for the identification of tissue properties in-vivo using a force sensitive wheeled probe. The purpose of such a device is to compensate a surgeon for a portion of the loss of haptic and tactile feedback experienced during robotic-assisted minimally invasive surgery. Initially, a testing facility for validating the concept ex-vivo was developed and used to characterize two different testing modalities - static (1-DOF) tissue indentation and rolling (2-DOF) tissue indentation. As part of the static indentation experiments a mathematical model was developed to classify tissue condition based on changes in mechanical response. The purpose of the rolling indentation tests was to detect tissue abnormalities, such as tumors, which are difficult to isolate under static testing conditions. During such tests, the test-rig was capable of detecting simulated miniature buried masses at depths of 12mm. Based on these experiments a portable device capable of carrying out similar tests in-vivo was developed. The device was designed to be operated through a trocar port and its key feature is the ability to transition between static indentation and rolling indentation modalities without retracting and changing the tool.

This paper proposes a novel nonlinear viscoelastic soft tissue model generated from ex vivo experimental results on ovine liver using a force sensitive probe. In order to study the biomechanics of soft tissue, static indentation tests were applied on ovine liver. An empirical constitutive equation was extracted from the examined data. A mechanical model combining linear viscoelasticity with a nonlinear function of strain-stress is proposed. The developed model has been evaluated both statically and dynamically with different strain rates - i.e. where the velocity of indentation is varied. By comparing simulation results and measured experimental data, it has been concluded that the proposed model is robust for modelling both static and dynamic indentation conditions. The effect of changing boundary conditions on the parameters in the proposed model has been studied by choosing test sites with different underlying tissue thicknesses. The results indicate that for small strain, the effect of the thickness condition is reasonable to be neglected.

This paper proposes a novel experimental study of the recovery of soft tissue after the removal of an external load. An optical fiber probe has been developed to measure the tissue recovery precisely through a static indentation method. Ex-vivo soft tissue recovery tests have been conducted on porcine liver. A robotic manipulator is used to control the motion of the probe and the force sensor is used to record the interaction force at the tip of the probe. An empirical mathematical model which describes the relationship of the liver recovery behavior and the holding time during which the probe keeps the tissue statically indented has been developed. The error analysis shows this model is able to predict liver recovery reasonably well. The experimental study shows that the recovery behavior of soft tissue depends on the holding time of the probe. In addition, the formula of the recovering force of the liver, which was deduced from the rate of liver recovery, is proposed.