Hysteresis in the tendons driving continuum robots is frequently regarded as a nuisance and a problem that is best avoided. Some prior work seeks to ameliorate the effects of hysteresis through the selection of materials. Others propose models of hysteresis to compensate for their effects. In this work, we present an empirically validated model of hysteresis in tendon-driven continuum robots. We demonstrate that hysteresis contributes to the stability of these robots by mitigating undesirable tensions in robot's backbone. As a result, a model-based approach to hysteresis can be used not just for compensation of a nuisance, but to enhance the utility of continuum robots in safety critical applications such as medical robots.

In this paper, we present a novel synergistic framework for learning shape estimation and a shape-aware whole-body control policy for tendon driven continuum robots. Our approach leverages the interaction between two Augmented Neural Ordinary Differential Equations (ANODEs) — the Shape-NODE and Control-NODE — to achieve continuous shape estimation and shape-aware control. The Shape-NODE integrates prior knowledge from Cosserat rod theory, allowing it to adapt and account for model mismatches, while the Control-NODE uses this shape information to optimize a whole-body control policy, trained in a Model Predictive Control (MPC) fashion. This unified framework effectively overcomes limitations of existing data-driven methods, such as poor shape awareness and challenges in capturing complex nonlinear dynamics. Extensive evaluations in both simulation and real-world environments demonstrate the framework's robust performance in shape estimation, trajectory tracking, and obstacle avoidance. The proposed method consistently outperforms state-of-the-art end-to-end, Neural-ODE, and Recurrent Neural Network (RNN) models, particularly in terms of tracking accuracy and generalization capabilities. The code and pretrained models are available at https://github.com/SIRGLab/WholeBodyControl_CTR.

Aiming at developing a safe, intuitive, and collaborative steerable drilling robotic system for pedicle screw fixation procedures, in this paper, we leverage our recently developed steerable drilling robotic framework, and developed a collaborative drilling mode to control this system. In this control mode, first a user positions a concentric tube steerable drilling robot (CT-SDR) in the workspace and aligns it based on a preplanned trajectory. Next, the CT-SDR is directly controlled by the user through an admittance mode to perform a drilling procedure and creating a J-shape tunnel. To evaluate the user comfort and intuitiveness of the drilling procedure using this system and the proposed control interface, we performed a user study with 11 subjects, who had no prior experience in using this system. The results of this study were analyzed using various qualitative and quantitative metrics.

To address the challenges associated with shape sensing of continuum manipulators (CMs) using Fiber Bragg Grating (FBG) optical fibers, we present a unique shape sensing assembly utilizing solely a single Optical Frequency Domain Reflectometry (OFDR) fiber attached to a flat nitinol wire (NiTi). Integrating this easy-to-manufacture unique sensor with a long and soft CM with 170 mm length, we performed different experiments to evaluate its C -, J -, and S-shape reconstruction ability. Results demonstrate phenomenal shape reconstruction accuracy for the performed C-shape ($<3.14 ~\text{mm}$tip error,$<1.91 ~\text{mm}$tip error,$<1.11 \mathbf{m m}$shape error), and S-shape ($<\mathbf{1. 7 4 ~ m m}$tip error,$<\mathbf{1. 4 0 ~ m m}$shape error) experiments.

In this study, we introduce a simulation-based modeling framework for the optimal design of our recently developed inflatable endoscopic vision-based tactile sensing balloon (E-VTSB). Of note, E-VTSB is designed for providing a safe and high-resolution textural mapping and morphology characterization of colorectal cancer (CRC) polyps to enhance the early diagnosis of cancerous polyps. Leveraging the Simulation Open Framework Architecture (SOFA) software and by performing complementary experimental validation, we thoroughly analyzed and investigated the impact of the elastic modulus of the material constitution of E-VTSB on its deformation behavior under different applied pressures. Our findings revealed a close correlation between the simulated outcomes and experimental data performed on two different E-VTSBs. In particular, with the maximum absolute deformation error of <12%, our results clearly validated the proposed framework’s accuracy in predicting the E-VTSB’s deformation trend and its potential use for optimizing the design parameters.

Current pelvic fixation techniques rely on rigid drilling tools, which inherently constrain the placement of rigid medical screws in the complex anatomy of pelvis. These constraints prevent medical screws from following anatomically optimal pathways and force clinicians to fixate screws in linear trajectories. This suboptimal approach, combined with the unnatural placement of the excessively long screws, lead to complications such as screw misplacement, extended surgery times, and increased radiation exposure due to repeated X-ray images taken ensure to safety of procedure. To address these challenges, in this paper, we present the design and development of a unique 4-degree-of-freedom (DoF) pelvic concentric tube steerable drilling robot (pelvic CT-SDR). The pelvic CT-SDR is capable of creating long S-shaped drilling trajectories that follow the natural curvatures of the pelvic anatomy. The performance of the pelvic CT-SDR was thoroughly evaluated through several S-shape drilling experiments in simulated bone phantoms.

To address the screw loosening and pullout limitations of rigid pedicle screws in spinal fixation procedures, and to leverage our recently developed Concentric Tube Steerable Drilling Robot (CT-SDR) and Flexible Pedicle Screw (FPS), in this paper, we introduce the concept of Augmented Bridge Spinal Fixation (AB-SF). In this concept, two connecting J-shape tunnels are first drilled through pedicles of vertebra using the CT-SDR. Next, two FPSs are passed through this tunnel and bone cement is then injected through the cannulated region of the FPS to form an augmented bridge between two pedicles and reinforce strength of the fixated spine. To experimentally analyze and study the feasibility of AB-SF technique, we first used our robotic system (i.e., a CT-SDR integrated with a robotic arm) to create two different fixation scenarios in which two J-shape tunnels, forming a bridge, were drilled at different depth of a vertebral phantom. Next, we implanted two FPSs within the drilled tunnels and then successfully simulated the bone cement augmentation process.

Spinal fixation procedures rely on pedicle screws to stabilize the vertebral column, but conventional rigid pedicle screws (RPS) face challenges such as misplacement, pullout, and loosening, particularly in patients with low bone mineral density (BMD). To overcome these limitations, we recently proposed a flexible pedicle screw (FPS) inserted inside a J-shape trajectory drilled by a steerable drilling robot. Towards biomechanical evaluation of our proposed FPS for spinal fixation procedures, in this paper, we introduce the design, integration, calibration, and evaluation of an optical frequency domain reflectometry (OFDR) strain sensor into an FPS. This sensor-integrated FPS (Si-FPS) provides real-time strain and shape-sensing information, facilitating improved implant functionality assessment and optimization. To thoroughly evaluate the Si-FPS, we first additively manufacture a special FPS and integrate a OFDR shape sensing assembly within its structure. We then assess shape sensing performance of this sensorized FPS using static and dynamic FPS insertion experiments.

In this paper, we introduce S3D: A Spatial Steerable Surgical Drilling Framework for Robotic Spinal Fixation Procedures. S3D is designed to enable realistic steerable drilling while accounting for the anatomical constraints associated with vertebral access in spinal fixation (SF) procedures. To achieve this, we first enhanced our previously designed concentric tube Steerable Drilling Robot (CT-SDR) to facilitate steerable drilling across all vertebral levels of the spinal column. Additionally, we propose a four-Phase calibration, registration, and navigation procedure to perform realistic SF procedures on a spine holder phantom by integrating the CT-SDR with a seven-degree-of-freedom robotic manipulator. The functionality of this framework is validated through planar and out-of-plane steerable drilling experiments in vertebral phantoms.

In this study, we introduce a new concept for reconstruction of Volumetric Muscle Loss (VML) injuries and propose the spatial robotic embedded bioprinting technique. As opposed to the traditional layer-by-layer printing, we leverage the support-free nature of embedded bioprinting to print spatial and complex structures of fascicles in a fusiform muscle. To demonstrate feasibility of this concept, we first propose our robotic bioprinting framework including a robotic arm integrated with a custom-designed bioprinting injector. Complementary motion planning algorithms uniquely designed for this printing task are further proposed. Moreover, the effect of embedded bioprinting parameters, as well as the supporting bath and injecting materials compatibility on the uniformity and quality of the printed constructs has been analyzed. Finally, we perform a case study by printing a fusiform muscle-shape construct using the proposed concept and algorithms, and evaluate the quality of the printed structure.

Manipulating articulated objects is especially difficult if the robot is operating autonomously or far from any human operator. Object articulation imposes strict constraints on robot motion, making it a challenge to generate valid trajectories to complete the task. Problems compound when the robot is mobile and operates in an uncontrolled environment, where the location or articulation model is unknown a priori. In this work, we leverage screw theory to model constraints imposed on a generic manipulator by simple articulated objects and present two novel, fast, and robust methods–Sequential Path Stepping (SPS) and Direct Screw Sampling (DSS)–for planning trajectories by directly sampling these constraints. We show that these methods are hardware-agnostic and work in cluttered environments using long, complex paths modeled by multiple screw-axis constraints. We demonstrate that modeling constraints using multiple screw axes handles objects with multiple DoF, or multi-step tasks (e.g., turning a knob before opening the door). In addition, the direct sampling component of the proposed approaches is implemented as a module that used with existing well-known probabilistic planning methods, allowing customization across different hardware, domains, or planning problems. We validate our methods across many planning and inverse kinematic elements, with three different mobile and stationary manipulators, and on a set of challenging planning problems that include single- and multiple-screw constraints. Results demonstrate a 97.6% success rate planning in cluttered environments, in less than 0.2 seconds.Note to Practitioners—This paper was motivated by the articulated manipulation problem for mobile manipulators. The solutions also apply to non-articulated object manipulation and task planning. Existing approaches do not include helical constraints (e.g., turning a threaded bolt), require operator oversight, or lack integration with MoveIt: the de facto kinematic manipulation standard. We address these issues with two methods that utilize screw theory to enable helical (inclusive of revolute and prismatic) constraints, require little input from operators, and align with standard plan-execute, task definition, and robot configuration capabilities offered by MoveIt and ROS. Through experimentation, we show that our methods easily plan manipulations of arbitrary articulated objects-including those with multiple DoF-are relatively quick, successful, and hardware agnostic.

In situbioprinting has been identified as a promising tissue engineering technique for treating volumetric muscle loss (VML) injuries. However, the success of this procedure significantly depends on the uniform and precise deposition of cells contributing to the regeneration of muscles. To address this critical need, in this work, we present design and quantitative evaluation of a novel autonomousin situbioprinting surgical robotic framework that can be used with a generic bioprinting material. The proposed framework consists of three main components: (i) a bioprinting tool integrated with a seven-degree-of-freedom robotic manipulator to perform a precise autonomous bioprinting procedure; (ii) a unique 3D visual measurement framework comprised of a high-accuracy structured light camera with complementary 2D/3D computer vision algorithms-to enable online and accurate measurement and reconstruction of the bioprinted constructs; and (iii) a quantitative evaluation module with novel assessment metrics-to characterize and evaluate the performance of the bioprinting process toward finding optimal bioprinting parameters. To ensure the biological functionality of a printed construct using our robotic system, we performed 90 experiments and identified optimal bioprinting parameters using the proposed novel assessment metrics.Note to Practitioners—This paper was motivated by the problem of volumetric muscle loss treatment using anin situbioprinting procedure but it also can be applied for treatment of skin and cartilage injuries. Existing approaches to performin situbioprinting is limited to either manual handheld bioprinting devices– that suffer from poor manual control and inaccurate printing constructs– or robotic systems– that have been developed without (i) considering a realistic surgical workflow and (ii) quantitatively evaluating the quality of printed constructs. To collectively address these issues, in this paper, we propose a novel autonomousin siturobotic bioprinting framework. We also introduce unique and complementary quantitative assessment metrics to characterize and evaluate the performance of the bioprinting process. Experiments suggest that the proposed framework can robustly identify optimal bioprinting parameters to ensure the biological functionality of a printed construct using our robotic system.

Robots operating in unpredictable environments require versatile, hardware-agnostic frameworks capable of adapting to various tasks. While a recent screw-based affordance approach shows promise, it faces challenges in avoiding undesirable configurations, singularity navigation, and task success prediction. To address these limitations, we propose a novel framework that incorporates gripper orientation control and generates complete joint trajectories in real time for screw-based task affordance execution. Our method models the affordance and manipulator as a closed-chain mechanism, introducing an innovative approach to solving closed-chain inverse kinematics. It encapsulates task constraints and simplifies task definitions, while remaining hardware and robot agnostic, robust to errors, and invariant to the initial grasp. We validate our framework with simulations on a UR5 robot and real-world implementation on a Boston Dynamics Spot robot. Our experiments demonstrate rapid joint trajectory generation (0.0077 to 0.098 seconds) for various tasks, including a 420-degree valve turn with consideration of the gripper orientation. Comparison with the state-of-the-art methods shows a 4x improvement in planning time, reduced joint movement and achievement of greater task goals.

In this paper, a novel mathematical framework is introduced for modeling deformation behavior of Tendon-Driven Continuum Manipulators (TD-CMs) featuring discontinuous cross-sectional geometries (i.e., having flexural patterns). Leveraging this framework, we also introduce the concept of design space by which the deformation-behavior space of a TD-CM can intuitively be analyzed via its geometrical design parameters. To thoroughly evaluate the performance of the proposed modeling framework, we have conducted various simulation studies and experiments.

In this work, we propose a semi-autonomous scheme to synergistically share the complicated task of manipulation and cutting of an unknown deformable tissue (U-DT) between a remote surgeon and a surgical robot. Particularly, utilizing the da Vinci Research Kit (dVRK) platform, we have designed and successfully demonstrated a fully functional shared control scheme for an autonomous tensioning and tele-cutting of a U-DT. We have shown the system’s ability to cooperate with a remote surgeon by leveraging an online data-driven learning and adaptive control method coupled with a reduced-order trajectory planning module that depends on just two parameters. By performing 25 experiments on custom-designed silicon phantoms and defining a set of success/failure metrics, we have put forward findings that establish a causal relationship between these two important parameters and the success or failure of the performed experiments.

In this paper, with the goal of enhancing the safety of current colonoscopic procedures and providing the pressure and location of the contact between the colonoscope and the colon’s surface, we propose design and development of a unique Soft Pressure Sensing Sleeve (SPSS). SPSS can seamlessly be integrated with the existing colonoscopic devices and would not change the existing diagnosis workflow. The pressure sensing of SPSS is performed based on the resistance change of a liquid metal (i.e., Gallium) embedded into several micro-channels located within SPSS’s deformable sleeve when it interacts with the colon surface. To demonstrate functionality of the SPSS, without loss of generality, in this paper, we designed and fabricated a SPSS with 4 sensing regions. We also proposed and experimentally evaluated an empirical calibration function for this sensor. Results demonstrate high accuracy (RMSE=2.45 and mean absolute error <3%) of the proposed calibration function compared with the evaluation experiments.

In this paper, to collectively address the existing limitations on endoscopic diagnosis of Advanced Gastric Cancer (AGC) Tumors, for the first time, we propose (i) utilization and evaluation of our recently developed Vision-based Tactile Sensor (VTS), and (ii) a complementary Machine Learning (ML) algorithm for classifying tumors using their textural features. Leveraging a seven DoF robotic manipulator and unique custom-designed and additively-manufactured realistic AGC tumor phantoms, we demonstrated the advantages of automated data collection using the VTS addressing the problem of data scarcity and biases encountered in traditional ML-based approaches. Our synthetic-data-trained ML model was successfully evaluated and compared with traditional ML models utilizing various statistical metrics even under mixed morphological characteristics and partial sensor contact.

Spinal fixation procedures are currently limited by the rigidity of the existing instruments and pedicle screws leading to fixation failures and rigid pedicle screw pull out. Leveraging our recently developed Concentric Tube Steerable Drilling Robot (CT-SDR) in integration with a robotic manipulator, to address the aforementioned issue, here we introduce the transformative concept of Spatial Spinal Fixation (SSF) using a unique Flexible Pedicle Screw (FPS). The proposed SSF procedure enables planar and out-of-plane placement of the FPS throughout the full volume of the vertebral body. In other words, not only does our fixation system provide the option of drilling in-plane and out-of-plane trajectories, it also enables implanting the FPS inside linear (represented by an I-shape) and/or non-linear (represented by J-shape) trajectories. To thoroughly evaluate the functionality of our proposed robotic system and the SSF procedure, we have performed various experiments by drilling different I-J and J-J drilling trajectory pairs into our custom-designed L3 vertebral phantoms and analyzed the accuracy of the procedure using various metrics.

Multiple object tracking (MOT) is a valuable perception function for robots and intelligent systems. Despite rapid improvements in metrics such as Average Multiple Object Tracking Accuracy (AMOTA) on MOT benchmarks, many trackers are application-specific or run at speeds10 FPS. MaRMOT is open source and can be extended with new detectors, process models, matching algorithms, and track management techniques.

Human–robot interaction (HRI) in human social environments (HSEs) poses unique challenges for robot perception systems, which must combine asynchronous, heterogeneous data streams in real time. Multimodal perception systems are well-suited for HRI in HSEs and can provide more rich, robust interaction for robots operating among humans. In this article, we provide an overview of multimodal perception systems being used in HSEs, which is intended to be an introduction to the topic and summary of relevant trends, techniques, resources, challenges, and terminology. We surveyed 15 peer-reviewed robotics and HRI publications over the past 10+ years, providing details about the data acquisition, processing, and fusion techniques used in 65 multimodal perception systems across various HRI domains. Our survey provides information about hardware, software, datasets, and methods currently available for HRI perception research, as well as how these perception systems are being applied in HSEs. Based on the survey, we summarize trends, challenges, and limitations of multimodal human perception systems for robots, then identify resources for researchers and developers and propose future research areas to advance the field.

We present a novel Cascade Reliability Framework (CRF) that integrates two independent cascade layers of reliability (i.e., variational temperature scaling and conformal prediction) with a pre-trained Machine Learning (ML) model in order to provide clinicians with a more reliable and tunable tool for early-stage diagnosis of Colorectal Cancer (CRC) polyps. The conformal prediction layer generates predictive sets that are guaranteed to contain the true polyp type with an adjustable error rate tuned by clinicians, while the confidence calibration generates meaningful confidence estimates for each predicted label. These two layers provide additional information and an error-tuning-ability for clinicians to assist them in making informed and intuitive decisions considering the outputs of the pre-trained ML model. Utilizing a novel vision-based tactile sensor and unique 3D-printed CRC polyp phantoms, we evaluated the trustworthiness of the proposed architecture and particularly dual outputs of four different types of CRF models, integrated with two different pre-trained ML models (i.e., ResNet18 and Dilated Residual Network) to highlight the model-agnostic feature of the architecture. To thoroughly assess the performance of the proposed approach, we used reliability diagrams and metrics such as accuracy, coverage, and average set size, while also addressing inter-class performance. Results demonstrate that the calibrated CRF models are well capable of handling non-ideal inputs with noise and blur. Moreover, using the conformal prediction with a user-defined error rate and various experiments, we show how clinicians can intuitively interact with a pre-trained ML model to make informed decisions and minimize the risk of CRC polyps misdiagnoses.

Communication latency in any delicate telerobotic operation (such as remote surgery over distance) would impose a significant challenge due to the temporal degradation of visual perception and can substantially affect the outcomes. Less is known, however, about the neurophysiological basis of how operators adapt/react to delayed visual feedback. Identification of such neural markers might provide novel ways for future applications to monitor the mental workload (MW). In this study, we recorded electroencephalography (EEG) data from nine users while performing a peg transfer task using the da Vinci Research Kit with three levels of induced visual delay in the video feedback. Our results suggest that spectral EEG-based features can provide markers of the operator's MW modulated by arbitrary visual delay. We also show that the exposure to different visual delays could be successfully classified/detected solely from EEG data, using a Riemannian geometry-based classifier, which highlights the utility of EEG signals for detecting the effect of visual delay on brain activity.

In this paper, we present the design, fabrication, and evaluation of a novel flexible, yet structurally strong, Concentric Tube Steerable Drilling Robot (CT-SDR) to improve minimally invasive treatment of spinal tumors. Inspired by concentric tube robots, the proposed two degree-of-freedom (DoF) CT-SDR, for the first time, not only allows a surgeon to intuitively and quickly drill smooth planar and out-of-plane J- and U- shape curved trajectories, but it also, enables drilling cavities through a hard tissue in a minimally invasive fashion. We successfully evaluated the performance and efficacy of the proposed CT-SDR in drilling various planar and out-of-plane J-shape branch, U-shape, and cavity drilling scenarios on simulated bone materials.

In this paper, with the goal of addressing the high early-detection miss rate of colorectal cancer (CRC) polyps during a colonoscopy procedure, we propose the design and fabrication of a unique inflatable vision-based tactile sensing balloon (VTSB). The proposed soft VTSB can readily be integrated with the existing colonoscopes and provide a radiation-free, safe, and high-resolution textural mapping and morphology characterization of CRC polyps. The performance of the proposed VTSB has been thoroughly characterized and evaluated on four different types of additively manufactured CRC polyp phantoms with three different stiffness levels. Additionally, we integrated the VTSB with a colonoscope and successfully performed a simulated colonoscopic procedure inside a tube with a few CRC polyp phantoms attached to its internal surface.

This paper proposes a smart handheld textural sensing medical device with complementary Machine Learning (ML) algorithms to enable on-site Colorectal Cancer (CRC) polyp diagnosis and pathology of excised tumors. The proposed unique handheld edge device benefits from a unique tactile sensing module and a dual-stage machine learning algorithms (composed of a dilated residual network and a t-SNE engine) for polyp type and stiffness characterization. Solely utilizing the occlusion-free, illumination-resilient textural images captured by the proposed tactile sensor, the framework is able to sensitively and reliably identify the type and stage of CRC polyps by classifying their texture and stiffness, respectively. Moreover, the proposed handheld medical edge device benefits from internet connectivity for enabling remote digital pathology (boosting the diagnosis in operating rooms and promoting accessibility and equity in medical diagnosis).

This study proposes a novel diagnosis framework to decrease the early detection miss rate of colorectal cancer (CRC) polyps by using a hypersensitive vision-based tactile sensor (HySenSe) and a deep residual neural network. The HySenSe generates high-resolution 3D textural images of 160 realistic polyp phantoms for accurate classification via the proposed deep learning (DL) architecture. The DL module explores lightweight dilated convolutions, residual neural network architecture, and transfer learning to overcome the challenge of a small dataset of 229 images. Results show that the proposed architecture outperforms state-of-the-art DL models (i.e., EfficientNet and DenseNet) with a 94% accuracy, offering a promising solution for improving early detection of CRC polyps. The proposed framework can be used as a diagnostic module within tele-assessment medical robots, highlighting the potential of advanced technology and deep learning to revolutionize the early detection and treatment of CRC.

Concentric Tube Robots (CTR) have been gaining ground in minimally-invasive robotic surgeries due to their small footprint, compliance, and high dexterity. CTRs can assure safe interaction with soft tissue, provided that precise and effective motion control is achieved. Controlling the motion of CTRs is still challenging. Commonly used model-based control approaches often employ simplified geometric/dynamic assumptions, which could be very inaccurate in the presence of unmodelled disturbances and external interaction forces. Additionally, application of emerging data-driven algorithms in real-time control of CTRs is limited due to the fact that these controllers require considerable amount of time to let the algorithm develop enough to reach a desired accuracy and relevancy. In this paper, we present a hybrid approach to overcome the aforementioned difficulties. This hybrid solution uses the solution of a kinematic model of the robot to estimate initial values for a model-free data-driven method. The proposed algorithm combines both model-based and data-driven algorithms to provide real-time motion control of CTRs interacting with an unknown external environment. Three different simulations studies were performed to thoroughly evaluate the efficacy of the proposed hybrid control approach as compared to two common model-based and data-driven control techniques. The results demonstrate superior performance of the proposed method. The root-mean-square error of the proposed hybrid approach is less than 1.1 mm, which is 9 times less than a common model-based controller.

In this paper, we present an analytical modeling approach to address the problem of tension loss in a generic variable curvature tendon-driven continuum manipulators (TD-CM) occurring due to the tendon-sheath distributed friction force. Despite the previous approaches in the literature, our presented model and the iterative solution algorithm do not rely on a priori known curvature/shape of the TD-CM and can be implemented on any TD-CM with constant/ variable curvatures with a continuous neutral axis function. The performance of the proposed modeling approach in predicting the distributed tendon tension and tension loss has been evaluated via simulation and experimental studies on a TD-CM with planar bending. Results demonstrate the outstanding and accurate performance of our novel modeling and the proposed solution algorithm.

In this article, we present a novel stochastic algorithm called simultaneous sensor calibration and deformation estimation (SCADE) to address the problem of modeling deformation behavior of a generic continuum manipulator (CM) in free and obstructed environments. In SCADE, using a novel mathematical formulation, we introduce a priori model-independent filtering algorithm to fuse the continuous and inaccurate measurements of an embedded sensor (e.g., magnetic or piezoelectric sensors) with an intermittent but accurate data of an external imaging system (e.g., optical trackers or cameras). The main motivation of this article is the crucial need of obtaining an accurate shape/position estimation of a CM utilized in a surgical intervention. In these robotic procedures, the CM is typically equipped with an embedded sensing unit (ESU) while an external imaging modality (e.g., ultrasound or a fluoroscopy machine) is also available in the surgical site. The results of two different set of prior experiments in free and obstructed environments were used to evaluate the efficacy of SCADE algorithm. The experiments were performed with a CM specifically designed for orthopaedic interventions equipped with an inaccurate Fiber Bragg Grating (FBG) ESU and overhead camera. The results demonstrated the successful performance of the SCADE algorithm in simultaneous estimation of unknown deformation behavior of the utilized unmodeled CM together with realizing the time-varying drift of the poor-calibrated FBG sensing unit. Moreover, the results showed the phenomenal out-performance of the SCADE algorithm in estimation of the CM's tip position as compared to FBG-based position estimations.

When robotic assistance is present into vitreoretinal surgery, the surgeon will experience reduced sensory input that is otherwise derived from the tool's interaction with the eye wall (sclera). We speculate that disconnecting the surgeon from this sensory input may increase the risk of injury to the eye and affect the surgeon's usual technique. On the other hand, robot autonomous motion to enhance patient safety might inhibit the surgeons tool manipulation and diminish surgeon comfort with the procedure. In this study, to investigate the parameters of patient safety and surgeon comfort in a robot-assisted eye surgery, we implemented three different approaches designed to keep the scleral force in a safe range during a synergic eye manipulation task. To assess the surgeon comfort during these procedures, the amount of interference with the surgeons usual maneuvers has been analyzed by defining quantitative comfort metrics. The first two utilized scleral force control approaches are based on an adaptive force control method in which the robot actively counteracts any excessive force on the sclera. The third control method is based on a virtual fixture approach in which a virtual wall is created for the surgeon in the unsafe directions of manipulation. The performance of the utilized approaches was evaluated in user studies with two experienced retinal surgeons and the outcomes of the procedure were assessed using the defined safety and comfort metrics. Results of these analyses indicate the significance of the opted control paradigm on the outcome of a safe and comfortable robot-assisted eye surgery.

Tracking and controlling the shape of continuum dexterous manipulators (CDM) in constraint environments is a challenging task. The imposed constraints and interaction with unknown obstacles may conform the CDM's shape and therefore demands for shape sensing methods which do not rely on direct line of sight. To address these issues, we integrate a novel Fiber Bragg Grating (FBG) shape sensing unit into a CDM, reconstruct the shape in real-time, and develop an optimization-based control algorithm using FBG tip position feedback. The CDM is designed for less-invasive treatment of osteolysis (bone degradation). To evaluate the performance of the feedback control algorithm when the CDM interacts with obstacles, we perform a set of experiments similar to the real scenario of the CDM interaction with soft and hard lesions during the treatment of osteolysis. In addition, we propose methods for identification of the CDM collisions with soft or hard obstacles using the jacobian information. Results demonstrate successful control of the CDM tip based on the FBG feedback and indicate repeatability and robustness of the proposed method when interacting with unknown obstacles.

A new type of cable-driven continuum manipulator (CM) is presented, in which the stiffness of the device along its body length can be controlled using the thermomechanical properties of a phase changing alloy. The liquid phase of the alloy is used for achieving high dexterity and the solid phase for high stiffness. Joule heating and water cooling is used for transitioning the phase changing alloy between stiff and compliant states. Single-segment and two-segment working prototypes of the CM are demonstrated. The mechanical and thermodynamic features of these prototypes are discussed and their physical performance is investigated. Advantages of the presented design with phase changing alloy include: significantly improved dexterity, high payload to weight ratio, controllable stiffness, energy efficiency, and a large lumen.

This paper focuses on the design and quantitative characterization of a debriding tool integrated with a robotic system to treat osteolysis (bone degradation). Osteolysis typically occurs due to wear of the polyethylene liner of the acetabular implant after total hip replacement surgery. In less invasive treatment of osteolysis, surgeons conventionally use rigid tools to debride the lesion, however with these inflexible instruments, complex lesion shapes are not completely treatable (about 50%). To address this issue, we have developed a debriding tool that passes through the lumen of a continuum dexterous manipulator (CDM). Integration of the CDM with a robotic arm assists the surgeon to reach the desired region behind the implant. Performance of the debriding tool integrated with this system was quantitatively evaluated during a simulated robot-assisted lesion debriding scenario. Rotational speed, aspiration pressure and irrigation flow of the debriding tool, as well as the sweeping velocity of the robotic system were identified as effective parameters in this procedure. Results indicate that maximum efficiency of the tool is achievable in a particular combination of these parameters.