Magnet localization is widely used for real-time medical instrument tracking because it offers a radiation-free alternative to fluoroscopy. While physics-model-based tracking methods are standard, localization accuracy can suffer owing to imperfect physical assumptions and computational inefficiencies in iterative convergence. This paper proposes a learning-based method that uses a single, compact neural network to achieve efficient magnetic localization with low computation cost. The proposed method achieves few-shot generalization through transfer learning based on a simulation dataset. Mean magnet position and orientation errors are 1.66 mm and 2.15° in a 100×100×100mm workspace using only four 3-channel sensors, providing a 2× improvement compared to a magnetic model-based method. A real-time catheter tracking experiment demonstrates that the efficacy of the method for practical medical applications.

Trans-catheter cardiac intervention has become an increasingly available option for high-risk patients without the complications of open heart surgery. However, current catheter-based platforms suffer from a lack of dexterity, force application, and compliance required to perform complex intracardiac procedures. An exemplary task that would significantly ease minimally invasive intracardiac procedures is the implantation of anchor coils, which can be used to fix and implant various devices. We introduce a robotic platform capable of delivering anchor coils. We develop a kineto-statics model of the robotic platform and demonstrate low positional error. We leverage the passive compliance and high force output of the actuator in a multi-anchor delivery procedure against a motile simulator with millimeter level accuracy.

Kinetic asymmetries in gait caused by neuropathology are a major factor in mobility deficiency. Gait perturbation training has shown promise for rehabilitating neurological gait asymmetry, but the transfer of benefits from treadmill training to overground walking is limited. To enable kinetic gait perturbation training on treadmills and overground, we developed novel adjustable compliance footwear technology. The system consists of a wearable pneumatic actuation module which controls the pressure of custom air pockets embedded in a soft lattice midsole, and is capable of independently and wirelessly adjusting the midsole compliance under each foot from 300 kN/m and 60% energy absorption to 18 kN/m and 75% energy absorption in about 4 seconds. Hindfoot and forefoot sole deflection data are measured via Hall effect sensors and paired magnets characterized to 1.7 mm accuracy. To demonstrate the experimental capability of the system, we successfully triggered multiple compliance changes during overground walking and recorded ground reaction forces and embedded sensor values during a prolonged asymmetric compliance walking bout on a treadmill. Future work will include a full experimental study of gait adaptation to asymmetric footwear compliance perturbations.

Continuum robots (CR) can achieve excellent dexterity and flexibility, making them suitable for navigating through cluttered environments and safely interacting with obstacles. Due to the underactuated nature of CRs, the contact mode between the robot and environment affects the static robot configuration. We show that the configuration space topology induced by environmental obstacles can be characterized by a quotient structure with a quotient space consisting of zero-actuation configurations. We propose to use the quotient space as a road map for motion planning to reduce computational load for exploration. Specifically, we propose an algorithm that identifies the quotient space as a graph of configuration modes by constructing a graph of convex sets in the free workspace, conducting tree search and convex optimizations to find candidate configurations, and then using elastic energy minimization to find the modes. We then use a motion planner which finds a path in the quotient space graph and constructs a continuous path in the configuration space. We demonstrate our method in several complex 3D environments and show that our method outperforms baselines in terms of computation time and success rate.

This paper presents a unified framework for autonomous flexible needle control in soft tissues using real-time finite element (FE) simulation and cross-entropy (CE) optimization. The method combines a sampling-based model predictive controller (MPC) for trajectory tracking with a kinematic-based bang-bang strategy to coordinate needle insertion, lateral adjustments, and bevel rotations. Sparse electromagnetic (EM) tracking feedback enables needle state reconstruction and compensates for model uncertainties. Experiments in plastisol and chicken breast phantoms show sub-millimeter targeting accuracy, with respective targeting errors 0.16 ± 0.29 mm and 0.22 ± 0.78 mm as reported by the tracker.

Minimally invasive endovascular and transcather procedures frequently involve delivering tools to the surgical site via a guidewire. Even when made steerable, the guidewire tip has the potential to cause tissue damage while traversing the vasculature and the connected organs. In this work, a methodology is introduced to integrate a balloon onto a tendon-driven robotically steerable guidewire to cushion the contact with tissue during traversal, stabilize within the vasculature once the guidewire has passed the surgical site, and arrest bloodflow to prevent distal embolization. A model is presented for the effects of a pressurized fluid channel on the curvature of the tendon-driven joint during bending, and also for the inflation of the hydraulic element on the tip of the guidewire. The balloon is fabricated via injection molding and a hydraulic pump is integrated with the actuation system to facilitate distal balloon inflation and deflation. The accuracy of the bending and inflation models was experimentally verified to demonstrate the validity of the approach with steerable balloon guidewire systems. The guidewire was tested in a phantom vasculature model under pulsatile flow to demonstrate traversal, inflation, and flow arrest in pseudo-physiological conditions.

Conventional freehand ultrasound (US) imaging is highly dependent on the skill of the operator, leading to inconsistent results and increased physical burden on sonographers. Robotic Ultrasound Systems (RUSS) aim to address these limitations by providing standardized and automated imaging solutions, especially in environments with limited access to skilled operators. This paper presents the development of a RUSS system that employs a novel end-effector, A-SEE2.0, which uses dual RGB-D depth cameras to maintain the US probe normal to the skin surface, a default starting configuration for anatomical landmarks identification. Our RUSS integrates RGB-D camera data with robotic control algorithms to maintain orthogonal probe alignment on uneven surfaces without preoperative data. Validation tests using a phantom model show that the system achieves robust normal positioning accuracy. A-SEE2.0 demonstrates 2.47 ± 1.25 degrees normal positioning error on a flat surface and 12.19 ± 5.81 degrees error on a mannequin surface. This work highlights the clinical potential of A-SEE2.0 by demonstrating that, during in-vivo forearm ultrasound examinations, it achieves image quality comparable to manual scanning by a human sonographer.

Minimally invasive surgery is regarded as a safer approach than open craniotomy to removing deep intracerebral mass lesions such as hematomas. It is usually performed by introducing a straight suction tool, sometimes combined with accessories for tissue debridement and irrigation, into the brain. Since collateral trauma to healthy tissue is proportional to the diameter of the tools, slender tools with small diameters are desired. However, current minimally invasive tools are inadequate for removal of large, multi-focal, and fibrous mass lesions. In this work, we present a new robotic surgical device for removing intracerebral mass lesions. The device consists of four concentric tubes. From outermost to innermost, they include a straight rigid stainless steel tube, a precurved superelastic nitinol tube with asymmetric notches, a braid-reinforced composite tube with tissue cutting holes at the tip, and a suction tube connected with a suction machine. A Pebax sleeve covers the notched area of the outer tube except the two most distal notches. By rotating and translating the notched nitinol tube, the robot tip can be manipulated inside a mass lesion. By concurrently rotating the cutting tube and applying negative pressure, tissues can be cut and removed through the suction tube. In this paper, we present our design and fabrication of this robotic device, kinematic modeling of the robot in terms of the rotation and translation of the notched tube and rotation of the cutting tube, and the results of feasibility studies show 540% improvement of mass lesion removal efficiency.

Navigating through the peripheral lung branches poses a significant challenge in diagnosing lesions during bronchoscopy. Soft robots are well-suited to address current limitations in bronchoscopy due to their scale, dexterity, and adaptability. In this paper, we propose a real-time, semi-autonomous navigation platform that leverages a soft continuum robot with an outer diameter of 2.5 mm for tip steering and a UR5e robot arm for insertion, translation, and rotation. Closed-loop feedback is provided via on-board visualization and electromagnetic tracking. Steering capability and workspace are characterized to demonstrate sufficient robot tip dexterity. A driving algorithm combined with a YOLO-based computer vision algorithm is developed to enable the robot to steer toward the target branch along preplanned paths. Multiple successful navigational experiments were performed within an in-vitro lung phantom to validate the proposed platform. The scale of the robot allows for successful navigation deep into the smaller, peripheral branches of the lung (6th generation) and exits the lung phantom, demonstrating the ability to reach the lung periphery with an average error at the target location of 1.1 mm.

Rehabilitation robotics continues to confront sub-stantial challenges, particularly in achieving smooth, safe, and intuitive human-robot interactions for upper limb motor training. Many current systems depend on complex mechanical designs, direct physical contact, and multiple sensors, which not only elevate costs but also reduce accessibility. Additionally, delivering seamless weight compensation and precise motion tracking remains a highly complex undertaking. To overcome these obstacles, we have developed a novel magnetic-based actuation mechanism for end-effector robotic rehabilitation. This innovative approach enables smooth, non-contact force transmission, significantly enhancing patient safety and comfort during upper limb training. To ensure consistent performance, we integrated an Extended Kalman Filter (EKF) alongside a controller for real-time position tracking, allowing the system to maintain high accuracy or recover even in the event of sensor malfunction or failure. In a user study with 12 participants, 75% rated the system highly for its smoothness, while 66.7% commended its safety and effective weight compensation. The EKF demonstrated precise tracking performance, with root mean square error (RMSE) values remaining within acceptable limits (under 2 cm). By combining magnetic actuation with advanced closed-loop control algorithms, this system marks a significant advancement in the field of upper limb rehabilitation robotics.

Firefighters consistently rank stair ascent with gear, which can weigh over 35 kg, as their most demanding activity. Weighted stair climbing requires dynamic motions and large knee torques, which can cause exhaustion in the short term, and overuse injuries in the long term. An active knee exoskeleton could potentially alleviate the burden on the wearer by injecting positive energy at key phases of the gait cycle. Similar devices have reduced the metabolic cost for various locomotion activities in previous studies. However, no information is available on the effect of active knee exoskeletons on muscular effort during prolonged weighted stair ascent. Here we show that our knee exoskeletons reduce the net muscular effort in the lower limbs when ascending several flights of stairs while wearing additional weight. In a task analogous to part of the physical fitness test for firefighters in the US, eight participants climbed stairs for three minutes at a constant pace while wearing a 9.1 kg vest. We compared lower limb muscle activation required to perform the task with and without two bilaterally worn Utah Knee Exoskeletons. We found that bilateral knee assistance reduced average peak quadriceps muscle activation measured through surface electromyography by 32% while reducing overall muscle activity at the quadriceps by 29%. These results suggest that an active knee exoskeleton can lower the overall muscular effort required to ascend stairs while weighted. In turn, this could aid firefighters by preserving energy for fighting fires and reducing overexertion injuries.

Traditional robotic knee-ankle prostheses categorize ambulation modes such as level walking, ramps, and stairs. However, human movement scales continuously across various states rather than discretely, making traditional mode classifiers inadequate for accurate intent recognition. This paper proposes a mode-unified intent recognition strategy that continuously estimates terrain slopes across five modes: level ground, ramp ascent/descent, and stair ascent/descent. Locomotion data from 16 individuals with transfemoral amputation were utilized to train slope estimation and mode classification models based on deep temporal convolutional networks. The proposed method was compared to the traditional mode classifier via offline test, using leave-one-subject-out validations for the user-independent performance. The mode-unified slope estimator achieved an MAE of 1.68 ± 0.60 degrees, outperforming the mode classifier's MAE of 1.94 ± 0.97 degrees (p<0.05). The lower slope estimation errors resulted in higher accuracy in replicating knee kinematics of able-bodied subjects, with the proposed system achieving an average MAE of 5.13 ± 2.00 degrees for knee clearance and 6.74 ± 2.97 degrees for knee contact angle, compared to the traditional classifier's 12.10 ± 5.20 degrees and 13.80 ± 3.28 degrees (p<0.01), respectively, in stair ascent. These results suggest that our mode-unified approach can enable continuous adjustment to terrains without mode classification.

Tendon-driven continuum robots (TDCRs) have received widespread attention in the medical domain due to their slender shape and flexibility. Modeling the dynamics of TDCRs involves continuum mechanics that result in nonlinear and computationally intensive models posing challenges for real-time control. In this work, we propose efficient, and controloriented modeling of the nonlinear dynamics of TDCRs leveraging the deep Koopman approach. This method transforms the states of the system into an intrinsic nonlinear manifold, where the autonomous dynamics are approximated linearly, and the actuation input enters the system with a bilinear term. The proposed model captures the nonlinearities, including space-dependent variations in the system spectrum. Position control is implemented using a linear quadratic controller, leveraging the linear nature of the Koopman operator. The accuracy of the proposed method is experimentally validated using a dualtendon robotic steerable catheter, achieving a position tracking error of 1.64 mm (SD = 0.74) for multi-sinusoidal, and 0.60 mm (SD = 0.30) for sinusoidal (0.05 Hz) target trajectories. The results demonstrate the potential for applying the proposed approach for real-time control of a broad range of TDCRs.

Post-stroke gait is slow, energetically costly, and unstable. Rehabilitation is necessary to encourage, retrain, and assist proper gait mechanics in stroke survivors. Evidence indicates robotic ankle exoskeletons can improve gait outcomes in stroke survivors, however challenges remain with proper lower limb positioning for optimal receipt of the assistance. Biofeedback can be used to improve positioning of the limb for receipt of robotic ankle exoskeleton assistance. In this study, four stroke survivors used bilateral powered robotic ankle exoskeletons (Dephy Exoboots) and an innovative, custom-designed vibrotactile-audio biofeedback interface targeting trailing limb angle to test the hypotheses that each intervention alone improves gait outcomes over baseline, and when combined they improve outcomes over either intervention alone. Compared to baseline, we found increases in average paretic propulsive impulse during the biofeedback-only and exoskeleton-plus-biofeedback conditions. Biofeedback alone induced the greatest increase on average self-selected walking speed, and the combination of exoskeleton assistance and biofeedback increased speed more compared to the robotic exoskeleton-only condition. Our preliminary results indicate that biofeedback in combination with a robotic exoskeleton produces greater synergistic benefits on gait performance than the use of an exoskeleton alone.

Attaching a wearable device to the user's body for comfort and function while accommodating the differences and changes in body shapes often represents a challenge. In this paper, we propose an approach that addresses this problem through granular jamming, where a granule-filled membrane stiffens by rapidly decreasing the internal air pressure (e.g., vacuum), causing the granule material to be jammed together due to friction. This structure was used to conform to complex shapes of the human body when it is in the soft state while switching to the rigid state for proper robot functions by jamming the granules via vacuum. We performed an experiment to systematically investigate the effect of multiple design parameters on the ability of such jamming-based interfaces to hold against a lateral force. Specifically, we developed a bench prototype where modular granular-jamming structures are attached to objects of different sizes and shapes via a downward suspension force. Our data showed that the use of jamming is necessary to increase the overall structure stability by 1.73 to 2.16 N. Furthermore, using three modules, high suspension force, and a low membrane infill (~25%) also contribute to high resistance to lateral force. Our results lay a foundation for future implementation of wearable attachments using granular-jamming structures.

Bronchoalveolar lavage (BAL) is a minimally invasive procedure for diagnosing lung infections and diseases. However, navigating tortuous lung anatomy to the distal branches of the bronchoalveolar tree for adequate sampling using BAL remains challenging. Continuum robots have been used to improve the navigation of guidewires, catheters, and endoscopes and could be applied to the BAL procedure as well. One class of continuum robots is constructed from a notched tube and actuated using a tendon. Many tendon-driven notched continuum robots use uniform machining parameters to achieve approximately constant-curvature configurations, which may be unsuitable for traversing the tortuous anatomy of the lungs. This paper presents a model that predicts the curvature of a robot with arbitrary notch shapes subjected to tendon tension. The model predicted the deflection of rectangular, elliptical, and sinusoidal notches in a 0.89 mm diameter nitinol tube with 2.32%, 3.65%, and 6.32% error, respectively. Furthermore, an algorithm is developed to determine the optimal pattern of notches to achieve a desired nonuniform robot curvature. A simulated robot designed using the algorithm achieved the desired shape with a root mean square error (RMSE) of 1.52°. Additionally, we present a model for predicting the shape of nonuniformly notched continuum robots which incorporates friction and pre-curvature. This model predicted the shape of a continuum robot with nonuniform rectangular notches with an average RMSE of 5.20° with respect to the actual robot. We also demonstrated navigating the continuum robot through a pulmonary phantom.

This paper presents a novel cable-driven exosuit intended for head-neck support and movement assistance. Mobility limitations in the head-neck, such as dropped head syndrome, can result from various neurological disorders. Current solutions, ranging from static neck collars to rigid-link robotic neck exoskeletons, are unsatisfactory. Neck collars are the most used clinically but fail to restore head-neck motion. Rigid-link neck exoskeletons can enable head movement but are bulky and restrictive. In this paper, we present the design of this exosuit, an analysis of its ability to balance the gravitational moment of the head in simulation, and the results of a user study comparing its kinematic performance to a state-of-the-art rigid-link neck exoskeleton. The exosuit is able to support the head across its full range of motion according to simulation results. It fits users of different sizes and participants exhibited more natural head-neck movement wearing the exosuit as compared to wearing the rigid-link exoskeleton. The exosuit allowed more head rotations than the rigid-link neck exoskeleton and required less compensatory torso movement for three daily tasks (looking for traffic, drinking from a bottle, and picking up an object from the floor). Its absolute range of motion was also much larger than the one allowed by the rigid-link neck exoskeleton. These results demonstrate the kinematic benefits of a cable-driven neck exosuit and provide justification for studying the use of such an exosuit for head-neck movement assistance in patient groups.

Tissue engineering techniques and particularly bioprinting using handheld devices and robotic systems have recently demonstrated promising outcomes to address volumetric muscle loss injuries. Nevertheless, these approaches suffer from insufficient printing precision and/or lack of quantitative analysis of the thickness and uniformity of bioprinted constructs (BPCs) - which are critical for ensuring cell viability and growth. To address these limitations, in this study, we present a framework for robotic bioprinting and complementary vision-based algorithms to quantitatively analyze thickness and uniformity of BPCs with curved geometries. The performance of the proposed robotic bioprinting and complementary algorithms has been thoroughly evaluated using various simulation and experimental studies on BPCs with constant and variable thicknesses. The results clearly demonstrate the remarkable and accurate performance of the proposed method in calculating the thickness and its variations along the geometry of the BPCs.

The treatment of many intravascular procedures begins with a clinician manually placing a guidewire to the target lesion to aid in placing other devices. Manually steering the guidewire is challenging due to the lack of direct tip control and the high tortuosity of vessel structures, potentially resulting in vessel perforation or guidewire fracture. These challenges can be alleviated through the use of robotically steerable guidewires that can improve guidewire tip control, provide force feedback, and, similar to commercial guidewires, are inherently safe due to their compliant structure. However, robotic guidewires are not yet clinically viable due to small robot lengths or large actuation systems. In this paper, we develop a highly compact spooling mechanism for the COaxially Aligned STeerable (COAST) guidewire robot, capable of dispensing a clinically viable length of 1.5 m of the robotic guidewire. The mechanism utilizes a spool with several interior armatures to actuate each component of the COAST guidewire. The kinematics of the robotic guidewire are then modeled considering additional friction forces caused by interactions within the mechanism. The actuating mechanisms of the compact spooling mechanism are calibrated and the kinematics of the guidewire are validated resulting in an average curvature RMSE of 0.24 m.

Transcatheter mitral valve repair (TMVr) is growing in popularity for non-surgical mitral regurgitation (MR) patients, but the manual operation of current TMVr devices increases radiation exposure and limits telesurgery feasibility. A robotically steerable delivery system can alleviate these problems, improving safety and precision while reducing staff fatigue. However, precise manipulation of a surgical robotic system requires system modeling and reliable external feedback. Ultrasound imaging provides visualization and guidance for precise instrument maneuvers within the body. Moreover, it is a readily available, safe, and cost-effective feedback modality, ideal for this procedure. Therefore, in this work, we use a previously derived model for the robotic transcatheter system and perform ultrasound-guided joint space control through real-time (algorithm run time: ~0.011 s) estimation of four joints simultaneously. The joints are estimated using kinematically-derived weight maps, a new technique, and a feature detection algorithm, with an accuracy of 3.19°, 2.76°, 2.41 mm, and 6.83° for the proximal bending, distal bending, prismatic motion, and distal torsion joints, respectively. This approach leverages existing knowledge about the system, demonstrating computational efficiency, intuitive comprehension, and independence from a training dataset, making it a versatile joint estimation technique. Experiments were conducted to compare the proposed method with currently employed joint estimation strategies. Additionally, real-time control was demonstrated using ultrasound feedback in a water bath, while subjecting the robotic transcatheter delivery system to similar tortuosity as encountered during a TMVr procedure.

Transcatheter mitral valve repair (TMVr), a minimally invasive approach, is becoming increasingly popular for treating mitral regurgitation (MR) since nearly half of the MR patients are non-surgical candidates. However, current TMVr devices are operated manually, increasing radiation exposure to the clinical staff and making telesurgery infeasible. A robotically steerable transcatheter delivery system can alleviate these issues while also enhancing consistency, improving precision, and mitigating human fatigue during the procedure. Moreover, precise manipulation of a surgical robotic system requires effective system modeling. Therefore, in this work, we model a full-scale robotically steerable transcatheter delivery system, accounting for the hysteresis, friction, tendon elongation, and catheter configuration. We also account for the coupling between the joints of the robotic steerable end and the effect of the catheter configuration on the joints. Experiments were conducted in free air to validate the proposed model while subjecting the robotic transcatheter delivery system to similar tortuosity as encountered during a TMVr procedure.