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Background: Pedicle screw placement during spine fusion is physically and mentally demanding for surgeons. Consequently, spine surgeons can become fatigued, which has implications for both patient safety and surgeon well-being. Purpose: We sought to assess the cognitive workload of surgeons placing pedicle screws using robotic-assisted navigation compared with fluoroscopic and computed tomography (CT)-assisted placement. Methods: We performed a nonrandomized prospective controlled trial to compare the cognitive workload of 3 surgeons performing single-level minimally invasive transforaminal lumbar interbody fusion (MI-TLIF) using robotic, CT, or fluoroscopic navigation on 25 patients (15 in the robotic navigation group and 10 in the nonrobotic group). Immediately after each procedure, surgeons submitted the National Aeronautics and Space Administration–Task Load Index (NASA-TLX), which has 6 subscales: mental demands, physical demands, temporal demands, performance, effort, and frustration. Four tasks associated with pedicle screw placement were assessed independently: (1) screw planning, (2) calibrating robot/obtaining imaging/registration, (3) pedicle cannulation, and (4) screw placement. Patient demographics and surgical characteristics were obtained and reviewed. Results: Surgeons' self-reported cognitive workload was significantly reduced when using robotic-assisted navigation versus CT/fluoroscopic navigation. Workload was reduced for screw planning, pedicle cannulation, and screw placement. In addition, there were significant reductions in each subdomain for these 3 tasks, encompassing mental demand, physical demand, temporal demand, effort, and frustration with improved task performance. Conclusions: This study found significant reductions in mental workload with improved perceived performance for robotic-assisted pedicle screw placement compared with fluoroscopic and CT-navigation techniques. Lowering the cognitive burden associated with screw placement may allow surgeons to address the remainder of the operative case with less decision fatigue, prevent complications, and increase surgeon wellness. [ABSTRACT FROM AUTHOR]

Objectives: In the current clinical practice of thermal ablation treatment for liver tumors, achieving consistent and effective clinical outcomes across tumors of varying shapes, sizes and locations remains challenging. The aim of this study was to evaluate the repeatability of a novel robotic approach for configurable ablation of distinct tumor shapes and compare it to the standard ablation technique for creating ellipsoidal ablation volumes. Materials and methods: The repeatability was evaluated in terms of width variability in created ablation volumes. Using a robotic navigation platform, custom ablation profiles configured with power, time, and distance parameters were designed to create four distinct ablation shapes. The profiles were applied for microwave ablation in a tissue-mimicking liver model. For comparison of ablation shape variability, six standard ellipsoidal shapes were created using the standard ablation technique by configuring power and time parameters. For each sample, the resulting ablation area was segmented, and the resulting shape width and length were calculated at the measurement points. Width variability was calculated as the median of the absolute pairwise differences in width at each measurement point, and configurable versus standard ablation shapes were compared using the Mann–Whitney U test. Results: All tissue-mimicking samples were successfully ablated using both configurable (n = 48) and standard ablation technique (n = 35). Study findings revealed noninferiority regarding repeatability of created ablation shapes using the robotic platform for configurable ablation, compared to created standard ellipsoidal ablation shapes (p < 0.001, 95% CI ≤ -0.05 mm, Δ = -0.22 mm). Median repeatability of created configurable shapes was 1.00 mm, and for standard shapes 1.22 mm. Maximal repeatability for both groups was below 3 mm. Conclusion: The repeatability of configurable ablation shapes was observed to be noninferior to the standard ablation shapes. Achieving configurable ablation volumes underscores the potential to advance personalization of thermal ablation treatment and broaden its applicability to distinct tumor cases. In-vivo validation is needed for evaluation of the clinical implications of this novel treatment technique. [ABSTRACT FROM AUTHOR]

This article introduces a novel approach to constructing a topometric map that allows for efficient navigation and decision-making in mobile robotics applications. The method generates the topometric map from a 2D grid-based map. The topometric map segments areas of the input map into different structural-semantic classes: intersections, pathways, dead ends, and pathways leading to unexplored areas. This method is grounded in a new technique for intersection detection that identifies the area and the openings of intersections in a semantically meaningful way. The framework introduces two levels of pre-filtering with minimal computational cost to eliminate small openings and objects from the map which are unimportant in the context of high-level map segmentation and decision making. The topological map generated by GRID-FAST enables fast navigation in large-scale environments, and the structural semantics can aid in mission planning, autonomous exploration, and human-to-robot cooperation. The efficacy of the proposed method is demonstrated through validation on real maps gathered from robotic experiments: 1) a structured indoor environment, 2) an unstructured cave-like subterranean environment, and 3) a large-scale outdoor environment, which comprises pathways, buildings, and scattered objects. Additionally, the proposed framework has been compared with state-of-the-art topological mapping solutions and is able to produce a topometric and topological map with up to 92% fewer nodes than the next best solution. The method proposed in this article has been implemented in the robotics framework ROS and is open-sourced. The code is available at: . [ABSTRACT FROM AUTHOR]

The predictive brain hypothesis suggests that perception can be interpreted as the process of minimizing the error between predicted perception tokens generated via an internal world model and actual sensory input tokens. When implementing working examples of this hypothesis in the context of in-air sonar, significant difficulties arise due to the sparse nature of the reflection model that governs ultrasonic sensing. Despite these challenges, creating consistent world models using sonar data is crucial for implementing predictive processing of ultrasound data in robotics. In an effort to enable robust robot behavior using ultrasound as the sole exteroceptive sensor modality, this paper introduces EchoPT (Echo-Predicting Pretrained Transformer), a pretrained transformer architecture designed to predict 2D sonar images from previous sensory data and robot ego-motion information. We detail the transformer architecture that drives EchoPT and compare the performance of our model to several state-of-the-art techniques. In addition to presenting and evaluating our EchoPT model, we demonstrate the effectiveness of this predictive perception approach in two robotic tasks. [ABSTRACT FROM AUTHOR]

Eighty consecutive complex spinal robotic cases utilizing intraoperative 3D CT imaging (E3D, Group 2) were compared to 80 age-matched controls using the Excelsius robot alone with C-arm Fluoroscopic registration (Robot Only, Group 1). The demographics between the two groups were similar—severity of deformity, ASA Score for general anesthesia, patient age, gender, number of spinal levels instrumented, number of patients with prior spinal surgery, and amount of neurologic compression. The intraoperative CT scanning added several objective factors improving patient safety. There were significantly fewer complications in the E3D group with only 3 of 80 (4%) patients requiring a return to the operating room compared to 11 of 80 (14%) patients in the Robot Only Group requiring repeat surgery for implant related problems (Chi squared analysis = 5.00, p = 0.025). There was a significant reduction the amount of fluoroscopy time in the E3D Group (36 s, range 4–102 s) compared to Robot only group (51 s, range 15–160 s) (p = 0.0001). There was also shorter mean operative time in the E3D group (257 ± 59.5 min) compared to the robot only group (306 ± 73.8 min) due to much faster registration time (45 s). A longer registration time was required in the Robot only group to register each vertebral level with AP and Lateral fluoroscopy shots. The estimated blood loss was also significantly lower in Group 2 (mean 345 ± 225 ml) vs Group 1 (474 ± 397 ml) (p = 0.012). The mean hospital length of stay was also significantly shorter for Group 2 (3.77 ± 1.86 days) compared to Group 1 (5.16 ± 3.40) (p = 0.022). There was no significant difference in the number of interbody implants nor corrective osteotomies in both groups—Robot only 52 cases vs. 42 cases in E3D group. Level of evidence: IV, Retrospective review. [ABSTRACT FROM AUTHOR]

Interventional oncology is routinely tasked with the feat of tumor characterization or destruction, via image-guided biopsy and tumor ablation, which may pose difficulties due to challenging-to-reach structures, target complexity, and proximity to critical structures. Such procedures carry a risk-to-benefit ratio along with measurable radiation exposure. To streamline the complexity and inherent variability of these interventions, various systems, including table-, floor-, gantry-, and patient-mounted (semi-) automatic robotic aiming devices, have been developed to decrease human error and interoperator and intraoperator outcome variability. Their implementation in clinical practice holds promise for enhancing lesion targeting, increasing accuracy and technical success rates, reducing procedure duration and radiation exposure, enhancing standardization of the field, and ultimately improving patient outcomes. This narrative review collates evidence regarding robotic tools and their implementation in interventional oncology, focusing on clinical efficacy and safety for nonhepatic malignancies. [ABSTRACT FROM AUTHOR]

Osteochondral lesion of the talus (OLT) is a localized cartilage and subchondral bone injury of the talus trochlea. OLT is caused by trauma and other reasons, including osteochondritis dissecans of the talus (OCD) and talus osteochondral tangential fracture. OLT can develop from being asymptomatic to subchondral bone cysts accompanied by deep ankle pain. OLT tends to occur on the medial and lateral sides of the talar vault. OLT seriously affects the patients' life and work and may even lead to disability. Herein, we reviewed advances in the treatment of OLT and the strengths and weaknesses of various treatments. Different treatment methods, including conservative treatments and surgical treatments, can be adopted according to the different subtypes or clinical symptoms of OLT. Conservative treatments mostly relieve symptoms in the short term and only slow down the disease. In recent years, it has been discovered that platelet-rich plasma injection, microfracture, periosteal bone grafting, talar cartilage transplantation, allograft bone transplantation, reverse drilling under robotic navigation, and other methods can achieve considerable benefits when each of these treatment methods is applied. Furthermore, microfracture combined with platelet-rich plasma injections, microfracture combined with cartilage transplantation, and various other treatment methods combined with anterior talofibular ligament repair have all led to good treatment outcomes. [ABSTRACT FROM AUTHOR]

Robotic navigation has been shown to increase precision, accuracy, and safety during spinal reconstructive procedures. There is a paucity of literature describing the best techniques for robotic-assisted spine surgery for complex, multilevel cases or in cases of significant deformity correction. We present a case series of 100 consecutive multilevel posterior spinal fusion procedures performed for multilevel spinal disease and/or deformity correction. 100 consecutive posterior spinal fusions were performed for multilevel disease and/or deformity correction utilizing robotic-assisted placement of pedicle screws. The primary outcome was surgery-related failure, which was defined as hardware breakage or reoperation with removal of hardware. A total of 100 consecutive patients met inclusion criteria. Among cases included, 31 were revision surgeries with existing hardware in place. The mean number of levels fused was 5.6, the mean operative time was 303 min, and the mean estimated blood loss was 469 mL. 28 cases included robotic-assisted placement of S2 alar-iliac (S2AI) screws. In total, 1043 pedicle screws and 53 S2AI screws were placed with robotic-assistance. The failure rate using survivorship analysis was 18/1043 (1.7%) and the failure rate of S2AI screws using survivorship analysis was 3/53 (5.7%). Four patients developed postoperative wound infections requiring irrigation and debridement procedures. None of the 1043 pedicle screws nor the 53 S2AI screws required reoperation due to malpositioning or suboptimal placement. This case series of 100 multilevel posterior spinal fusion procedures demonstrates promising results with low failure rates. With 1043 pedicle screws and 53 S2AI screws, we report low failure rates of 1.7% and 5.7%, respectively with zero cases of screw malpositioning. Robotic screw placement allows for accurate screw placement with no increased rate of postoperative infection compared to historical controls. Level of evidence: IV, Retrospective review. [ABSTRACT FROM AUTHOR]

In an urban city, the daily challenges of managing cleanliness are the primary aspect of routine life, which requires a large number of resources, the manual process of labour, and budget. Street cleaning techniques include street sweepers going away to different metropolitan areas, manually verifying if the street required cleaning taking action. This research presents novel street garbage recognizing robotic navigation techniques by detecting the city's street-level images and multi-level segmentation. For the large volume of the process, the deep learning-based methods can be better to achieve a high level of classification, object detection, and accuracy than other learning algorithms. The proposed Histogram of Oriented Gradients (HOG) is used to features extracted while using the deep learning technique to classify the ground-level segmentation process's images. In this paper, we use mobile edge computing to process street images in advance and filter out pictures that meet our needs, which significantly affect recognition efficiency. To measure the urban streets' cleanliness, our street cleanliness assessment approach provides a multi-level assessment model across different layers. Besides, with ground-level segmentation using a deep neural network, a novel navigation strategy is proposed for robotic classification. Single Shot MultiBox Detector (SSD) approaches the output space of bounding boxes into a set of default boxes over different feature ratios and scales per attribute map location from the dataset. The SSD can classify and detect the garbage's accurately and autonomously by using deep learning for garbage recognition. Experimental results show that accurate street garbage detection and navigation can reach approximately the same cleaning effectiveness as traditional methods. [ABSTRACT FROM AUTHOR]

The article presents the research on navigation algorithms of a wheeled mobile robot with the use of a vision mapping system and the analysis of energy consumption of selected navigation algorithms, such as RRT and A-star. Obstacle maps were made with the use of an RGBW camera, and binary occupation maps were also made, which were used to determine the traffic path. To recreate the routes in hardware, a programmed Pure Pursuit controller was used. The results of navigation were compared on the basis of the forward kinematics model and odometry measurements. Quantities such as current, except (x, y, phi), and linear and angular velocities were measured in real time. As a result of the conducted research, it was found that the RRT star algorithm consumes the least energy to reach the designated target in the designated environment. [ABSTRACT FROM AUTHOR]

Purpose: To assess the feasibility and safety of a robotic system for percutaneous needle insertion during thermal ablation of liver tumors. Materials and Methods: This study analyzed the CT-guided percutaneous needle insertion using the EPIONE robotic device (Quantum Surgical, Montpellier, France) for radiofrequency or microwave liver ablation. The main criteria of the study were feasibility (possibility to perform the thermal ablation after needle insertion), the number of needle adjustments (reiteration of robotically assisted needle insertion when initial needle positioning is considered insufficient to perform ablation), and robotic-guided procedure safety (complications related to the needle insertion). Patients were followed up at 6 months post-intervention to assess local tumor control. Results: Twenty-one patients with 24 tumors, including 6 HCC and 18 metastases measuring 15.6 ± 7.2 mm, were enrolled. One patient (with one tumor) was excluded for protocol deviation. Robotic assisted thermal ablation was feasible for 22/23 lesions (95.7%) and 19/20 patients (95.0%), as validated by a data safety monitoring Board (95% CI [76.39%; 99.11%]) for the per-protocol population. The mean number of needle adjustments per tumor treated was 0.4 (SD: 0.7), with 70.8% of tumors requiring no adjustment. No adverse events were depicted. Rate of local tumor control was 83.3% for patients and 85.7% for tumors, at 6 months. Conclusion: This bicentric first-in-human pilot study suggests both feasibility and safety of a stereotactic CT-guided EPIONE device for the percutaneous needle insertion during liver tumor thermal ablation. [ABSTRACT FROM AUTHOR]

We present an implantable brain-computer interface surgical case assisted by robotic navigation system in an elderly patient with tetraplegia caused by cervical spinal cord injury. Left primary motor cortex was selected for implantation of microelectrode arrays based on fMRI location of motor imagery. Robotic navigation system was used during this procedure for precise and stable manipulation. A design of bipartite incision was made to reduce the risk of surgery-related infection and facilitate BCI training. At 1-year follow-up, the neural signals were robust, and this patient was able to control three-dimensional movement of a prosthetic limb without any complications. [ABSTRACT FROM AUTHOR]

Aims: Robotic magnetic navigation (RMN) provides increased catheter precision and stability. Formerly, only the CARTO 3 mapping system was integrated with the RMN system (CARTO-RMN). Recently, a novel high-resolution non-contact mapping system (AcQMap) has been integrated with the RMN system (AcQMap-RMN) for the treatment of atrial fibrillation (AF) and atrial tachycardias (AT). We aim to compare the safety, efficiency, and efficacy of AcQMap-RMN with CARTO-RMN guided catheter ablation (CA) procedures. Material and methods: In this prospective registry, procedural safety efficiency and outcome data from total of 238 consecutive patients (147 AcQMap-RMN and 91 CARTO-RMN patients) were compared. Results: AcQMap-RMN is non-inferior in the primary endpoint of safety as compared to CARTO-RMN across the whole group (overall procedural complications in 5 (3.4%) vs. 3 (3.3%) patients, p = 1.0). Overall procedure durations were longer and associated with more fluoroscopy use with AcQMap-RMN (172.5 vs. 129.6 min, p

Background: Robot-assisted surgery was developed to improve accuracy and outcomes in total knee arthroplasty (TKA). One important determinant of TKA success is a well-balanced knee throughout the range of motion. The purpose of this study is to determine if robot-assisted TKA (RA-TKA) results in improved intracompartmental ligament balance compared with conventional jig-based instrumentation (CM-TKA). Methods: This retrospective cohort study included 2 cohorts—a CM-TKA (n = 49) vs RA-TKA (n = 37) cohort. Demographic and intraoperative data, including intraoperative compartment loads, were measured after final implant implantation in extension (10°), mid-flexion (45°), and full flexion (90°), using an intraoperative compartment pressure sensor. An a priori power analysis revealed our study exhibited >80% power in detecting a 5-pound (lb) difference in compartment loads in the 2 cohorts. Results: There was no difference between medial and lateral compartment loads in extension, mid-flexion, and full flexion for the conventional (15.1 lbs, 15.9 lbs, and 13.4 lbs, respectively) vs RA-TKA (14.2 lbs, 15.1 lbs, and 10.3 lbs, respectively). The percentage of patients with high load compartment pressure in flexion (>40 lbs) by the conclusion of the surgery was significantly greater for the conventional (18%) vs the robotic TKA cohort (3%, P = .025). The percentage of patients with unbalanced knees (>20 lbs differential between medial and lateral compartments) in flexion was significantly greater in the conventional (24%) vs robotic TKA cohort (5%, P = .018). Conclusions: In this series, RA-TKA resulted in improved intraoperative compartment balancing in flexion with no observed difference in mid-flexion and extension compared with CM-TKA.

Introduction: Exposure to ionizing radiation occurs during most EP procedures and is associated with increased risk of cancers and orthopedic complications associated with wearing heavy protective apparel. The use of fluoroless ablation technique has been gaining popularity over the past decade and has been shown to be safe and efficient although the data has been limited to the manual catheter ablation. Fluoroless robotic navigation (RMN) ablation procedure for atrial fibrillation has not been described previously and the impact of its adoption on procedural time and safety is unknown. Material and Methods: The impact of adoption of fluoroless AF ablation was studied in this single-operator time-series analysis. A total of 58 consecutive patients undergoing RMN AF ablation were included in this study and different components of the procedural duration were assessed before and after the introduction of fluoroless technique. A meta-analysis of previously published procedural times using manual fluoroless technique was performed and used for comparison. Results: Upon introduction of fluoroless RMN ablation, there was an increase in the access and mapping time of the procedure by 16.9±4.3 min (P<0.001). However, this increase was counteracted by a reduction in the ablation time and as a result the total procedure time was not significantly impacted (increase of 5.2±15.7 min, P=0.7). The total procedure time was comparable to previously published data on fluoroless manual AF ablation. No major intra-procedural complications occurred. Conclusion: Zero fluoroscopy using Remote Magnetic Navigation is safe and efficient. The total procedural time is not significantly impacted after adoption of fluoroless technique. [ABSTRACT FROM AUTHOR]

This paper presents a novel real-time obstacle avoidance and navigation technique called as "Free-configuration Eigenspace" (FCE). The FCE enables an autonomous robot to detect unknown obstacles and avoid collisions while simultaneously steering the robot towards the target. The methodology utilizes a two-dimensional (2D) Cartesian Eigenspace as a world model. 2D way points (in world model) are extracted by computing (through FCE model) stacked Eigenvectors of laser data at discrete time scans which manifest desired robotic trajectory. The world model is updated continuously upon obtaining discrete time laser scans sampled by on-board Light Detection and Ranging (LiDAR) sensors. The FCE technique has been implemented on Robotic Operating System and real-time robotics simulator Gazebo ® . Encouraging results are obtained and shown in this paper. The FCE has also been tested in real-time with laser scans obtained from VL6180X LiDAR sensor mounted on a custom autonomous robot. The results obtained are further compared with the state-of-the-art obstacle avoidance and path planning method called as vector field histogram (VFH). The FCE results (shown in the paper) are comparable, encouraging, and outperform VFH in path length and safe distance maintenance. [ABSTRACT FROM AUTHOR]

Background: The quality of catheter–tissue contact is one of the most important determinants of catheter ablation (CA) success. The absence of catheter–tissue contact feedback has been regarded a major limitation of remote magnetic navigation (RMN)–guided CA. The e-Contact module (ECM) is a novel feature designed for RMN that measures the quality of catheter–tissue contact. Objective: The purpose of this study was to describe the first clinical experience with this feature and to test its effect on procedural parameters and interference with other ablation equipment during CA procedures as well as its safety. Methods: This was a prospective, single-center, 2-phase study investigating ECM use during complex RMN procedures in 30 patients. Impact of ECM on procedural parameters was evaluated in the feasibility phase (FP), and its interference with other equipment was tested in the interference phase (IP) using pacing maneuvers at 3 randomly selected right atrial sites. Intracardiac electrograms were evaluated for disturbances by 2 independent electrophysiologists. Results: For FP, mean procedural time was 162 ± 66 minutes, fluoroscopy time 21 ± 9 minutes, and ablation time 34 ± 21 minutes. For IP, no significant differences in pacing capture or thresholds were found (ECM– vs ECM+: site 1: 2.05 vs 2.21 mA; P = .320; site 2: 2.15 vs 2.12 mA; P = .873; site 3: 2.51 vs 2.50 mA; P = .976). Electrogram disturbances did not significantly differ between ECM– and ECM+. No adverse events were reported. Conclusion: The ECM is a novel catheter–tissue contact technology designed for RMN-guided CA. Our study suggests that this feature is feasible and does not interfere with other electrophysiology equipment while maintaining an excellent safety profile.

The ability to navigate the anterior lumbar disc space may improve clinical outcomes and implant longevity. However, no robotic navigation systems are presently authorized by the U.S. Food and Drug Administration to assist with anterior retroperitoneal lumbar interbody surgery. Furthermore, no studies to date have investigated such an application of this technology. This study examines the application of robotic navigation to anterior lumbar total disc replacement surgery to improve retroperitoneal exposure and orientation of the anterior lumbar spine, enhance coronal plane centralization of the implant, optimize surgical trajectory, and mitigate radiologic exposure. Postoperative outcomes of a small cohort of patients undergoing anterior lumbar total disc replacement surgery using robotic navigation were analyzed. The results of the study revealed that a modified use of the aforementioned robot-assisted surgical technology enhances coronal plane centralization and trajectory, all while mitigating radiologic exposure, resulting in more accurate placement of the implant within the intervertebral space at each level.

Pedicle screw fixation in pediatric spine surgery has become common practice given the fixation stability and improved curve correction. However, due to proximity to vital structures, accuracy is paramount. Literature has reported accuracy rates from 87.5 to 90% using traditional freehand techniques. This study presents our initial experience with pedicle screw placement using the newest generation of spinal robotics for treatment of pediatric spinal deformity. A cohort of patients, aged 8–21 years, undergoing spinal fusion surgery using robotic-assisted technology was reviewed. Diagnoses, Cobb angles, surgical time, robot time, number of screws placed, and complications were recorded. Accuracy of screw placement was assessed based on analysis of successful screw execution, evaluation screw position using intraoperative fluoroscopy and post-operative radiographs, and clinical evaluation. The average age was 14.5 years. Prevalent diagnoses included idiopathic (65%) and neuromuscular scoliosis (13%). Mean preoperative curve measured 66.8°. The median time for operation was 235 minutes with medians of 8 levels fused and 5 screws placed per patient. Of the 314 screws placed, we recorded a 98.7% accuracy rate. Lateral deviation was the most common cause of malpositioning. Post-operative plain films revealed no grossly misplaced screws. There were no perioperative neurologic deficits or malpositioned screws requiring reoperation. This is the first reported series of navigated spinal robotics used for pedicle screw placement in children. Our clinical success rate was 98.7% and there were no clinically relevant screw related complications. The study shows promising initial results of combined robotic-navigation techniques in pediatric patients. [ABSTRACT FROM AUTHOR]

As robotics in spine surgery has progressed over the past 2 decades, studies have shown mixed results on its clinical outcomes and economic impact. In this review, we highlight the evolution of robotic technology over the past 30 years, discussing early limitations and failures. We provide an overview of the history and evolution of currently available spinal robotic platforms and compare and contrast the available features of each. We conclude by summarizing the literature on robotic instrumentation accuracy in pedicle screw placement and clinical outcomes such as complication rates and briefly discuss the future of robotic spine surgery. [ABSTRACT FROM AUTHOR]

This paper presents a novel robotic navigation system integrating a visual simultaneous localization and mapping (V-SLAM) based global planner with a deep reinforcement learning (DRL) based local planner. On one hand, map of many modern popular V-SLAM systems is inhomogeneous point cloud, which contains many outliers and is too sparse for reliable global path planning. To address this problem, we propose a novel approach to generate a topological map with both trajectories and map points of V-SLAM. On the other hand, current state-of-the-art (SOTA) DRL-based local planners have shown great efficiency in obstacle avoidance. However, the SOTA DRL-based local planners are sometimes trapped by large obstacles and would fall into some local minimum during training. To address the problems, we propose a sub-target module and a mirror experience replay approach. Test results demonstrate that, our topological map generation is robust against outliers and sparsity of map points of V-SLAM, while our local planner achieves 9.61% success rate of obstacle avoidance higher than the SOTA DRL-based approach. Tests in real environment demonstrate the feasibility of our navigation system. [ABSTRACT FROM AUTHOR]

Objective Depth electrode implantation for invasive monitoring in epilepsy surgery has become a standard procedure. We describe a new frameless stereotactic intervention using robot-guided laser beam for making precise bone channels for depth electrode placement. Methods A laboratory investigation on a head cadaver specimen was performed using a CT scan planning of depth electrodes in various positions. Precise bone channels were made by a navigated robot-driven laser beam (erbium:yttrium aluminum garnet [Er:YAG], 2.94-μm wavelength,) instead of twist drill holes. Entry point and target point precision was calculated using postimplantation CT scans and comparison to the preoperative trajectory plan. Results Frontal, parietal, and occipital bone channels for bolt implantation were made. The occipital bone channel had an angulation of more than 60 degrees to the surface. Bolts and depth electrodes were implanted solely guided by the trajectory given by the precise bone channels. The mean depth electrode length was 45.5 mm. Entry point deviation was 0.73 mm (±0.66 mm SD) and target point deviation was 2.0 mm (±0.64 mm SD). Bone channel laser time was ∼30 seconds per channel. Altogether, the implantation time was ∼10 to 15 minutes per electrode. Conclusion Navigated robot-assisted laser for making precise bone channels for depth electrode implantation in epilepsy surgery is a promising new, exact and straightforward implantation technique and may have many advantages over twist drill hole implantation. [ABSTRACT FROM AUTHOR]

In the field of robotics, it is essential to know the work area in which the agent is going to develop, for that reason, different methods of mapping and spatial location have been developed for different applications. In this article, a machine vision algorithm is proposed, which is responsible for identifying objects of interest within a work area and determining the polar coordinates to which they are related to the observer, applicable either with a fixed camera or in a mobile agent such as the one presented in this document. The developed algorithm was evaluated in two situations, determining the position of six objects in total around the mobile agent. These results were compared with the real position of each of the objects, reaching a high level of accuracy with an average error of 1.3271% in the distance and 2.8998% in the angle. [ABSTRACT FROM AUTHOR]

Vision navigation technique, especially the vision-based simultaneous localization and mapping (V-SLAM), plays a critical role in robotic navigation. As a relative positioning technique, V-SLAM often suffers from drift and scale uncertainty problems which incur bias increasing over time. To overcome these drawbacks and to improve the robustness and accuracy of localization, an effective way is to fuse global navigation satellite system (GNSS) with V-SLAM. In this paper, we propose a novel GNSS and SLAM fusion algorithm, which provides ego-motion estimation through tightly coupling GNSS pseudo-range measurements and camera feature points. It first decomposes the pose state into basic motion vectors, based on which an asynchronous tracking is performed. Then, a 10-DoF joint-optimization formulation on manifold is proposed to achieve tight fusion of the raw measurement from camera and GNSS. Finally, this formulation is solved to calculate the ego-motion state. The proposed algorithm is verified on an autonomous ground vehicle in two typical environments. The results demonstrated that, the new algorithm can amend the bias in vision SLAM and constrain the GNSS solution, which achieves a better localization result than the traditional methods. [ABSTRACT FROM AUTHOR]

In Eikonal equations, rarefaction is a common phenomenon known to degrade the rate of convergence of numerical methods. The "factoring" approach alleviates this difficulty by deriving a PDE for a new (locally smooth) variable while capturing the rarefaction-related singularity in a known (non-smooth) "factor". Previously this technique was successfully used to address rarefaction fans arising at point sources. In this paper we show how similar ideas can be used to factor the 2D rarefactions arising due to nonsmoothness of domain boundaries or discontinuities in PDE coefficients. Locations and orientations of such rarefaction fans are not known in advance and we construct a "just-in-time factoring" method that identifies them dynamically. The resulting algorithm is a generalization of the Fast Marching Method originally introduced for the regular (unfactored) Eikonal equations. We show that our approach restores the first-order convergence and illustrate it using a range of maze navigation examples with non-permeable and "slowly permeable" obstacles. [ABSTRACT FROM AUTHOR]

Recent years have seen a fast growth in the number of applications of Machine Learning algorithms from Computer Science to Robotics. Nevertheless, while most such attempts were successful in maximizing robot performance after a long learning phase, to our knowledge none of them explicitly takes into account the budget in the algorithm evaluation: e.g. budget limitation on the learning duration or on the maximum number of possible actions by the robot. In this paper, we introduce an algorithm for robot spatial localization based on image classification using a sequential budgeted learning framework. This aims to allow the learning of policies under an explicit budget. In this case our model uses a constraint on the number of actions that can be used by the robot. Our approach enables to reduce the problem to a classification task under budget constraint. We apply this algorithm to a localization problem in a simulated environment. We compare it first to simple neural networks for the classification part and second to different techniques of policy selection. The results show that the model can effectively learn an efficient active sensing policy (i.e. alternating between sensor measurement and movement to get additional information in different positions) in order to optimize its localization performance under each tested fixed budget. We also show that with this algorithm the simulated robot can transfer the learned policy as well as knowledge about which budget gives the best performance/budget ratio in a given environment to other environments with similar properties. We finally test the algorithm with real navigation data acquired in an indoor environment with the PR2 robot. Altogether, these results suggest a promising framework for enabling budgeted localization in robots and avoiding to make robots relearn everything from scratch in each new environment. [ABSTRACT FROM AUTHOR]

Antennae are the main sensory organs of insects which allow them to gather information from environment through direct contact and active sensing. In this study, a self-powered bionic antenna (SBA) was developed to emulate the structure and function of insect antenna for robotic tactile sensing. The sensing component of SBA based on the triboelectric nanogenerator (TENG) technology enables the conversion of mechanical stimuli into voltage signal without requiring external power source, and is capable of identifying different contact materials. To imitate the active sensing performance of insect antenna, a two-stage actuator was designed and integrated into the SBA, enabling active sensing in both the horizontal and vertical planes. The ultra-sensitive, lightweight and drivable design of SBA renders it highly compatible with micro soft robots featured with low actuation force and compact size, facilitating both passive and active real-time sensing and making it a promising solution for future robotic tactile systems. [Display omitted] • A self-powered bionic antenna is proposed for the tactile sensing of micro soft robots, featuring ultra-sensitivity, lightness, and drivability. • The receptor of bionic antenna based on TENG can be utilized to detect both mechanical stimuli and different materials. • Equipped with a two-stage SMA actuator, the self-powered bionic antenna can actively move at 2 degrees of freedom. • The micro-robot equipped with the bionic antenna can complete tasks including obstacle avoidance, instruction collection and environmental detection. [ABSTRACT FROM AUTHOR]

This paper presents a novel multi-sensor navigation system for the urban ground vehicle. The integration system can combine the measurements from a polarized skylight sensor, an inertial sensor, and a monocular camera. Utilizing the polarized skylight sensor, we propose a robust orientation algorithm with the total least squares to provide the orientation constraint for the integrating system. In our algorithm, the ambiguity problem of polarized orientation is solved without any other sensor. In order to enhance the algorithm’s robustness in the urban environment, we also propose a real-time method that uses the gradient of the degree of the polarization to remove the obstacles. With a monocular camera, we build a metric map and recognize places in the map to provide the position constraint for the integrating system. We develop the Kalman filter to integrate these constraints with the inertial navigation results to estimate the orientation and position for the ground vehicle. The results demonstrate that our proposed system outperforms other vision-based navigation algorithms–the RMSE of the position error is 2.04 m (0.01% of the travelled distance) and the RMSE of the orientation error is 0.84°. Finally, we present interesting insights gained with respect to the further work in sensors and robotics. [ABSTRACT FROM PUBLISHER]

Navigation, mapping, and tracking are state estimation problems relevant to a wide range of applications. These problems have traditionally been formulated using random vectors in stochastic filtering, smoothing, or optimization-based approaches. Alternatively, the problems can be formulated using random finite sets, which offer a more robust solution in poor detection conditions (i.e., low probabilities of detection, and high clutter intensity). This paper mathematically shows that the two estimation frameworks are related, and equivalences can be determined under a set of ideal detection conditions. The findings provide important insights into some of the limitations of each approach. These are validated using simulations with varying detection statistics, along with a real experimental dataset. [ABSTRACT FROM PUBLISHER]

Background: Long-term results after circumferential pulmonary vein isolation (CPVI) for the treatment of paroxysmal atrial fibrillation (PAF) using a robotic navigation system (RNS) have not yet been reported. Objective: To evaluate long-term results of patients with PAF after CPVI using RNS. Methods: In this study, 200 patients ( n = 151 (75.5 %) male; median age 62.2 (54.7-67.7) years) with PAF were evaluated. In 100 patients, RNS (RN-group) was used for CPVI and compared to 100 manually ablated control patients (MN-group). Radiofrequency was used in conjunction with 3D electroanatomic mapping. Power was limited to 30 watts (W) at the posterior left atrial (LA) wall in the first 49 RNS patients (RN-group-a). After esophageal perforation occurred in one RN-group-a patient, maximum power was reduced to 20 W for the subsequent 51 patients (RN-group-b). Results: After a median follow-up of 2 years, single (77/100 vs 77/100, p = 0.89) and multiple (90/100 vs 93/100, p = 0.29) procedure success rates were comparable between RN-group and MN-group. Single procedure success rate was significantly lower in RN-group-a as compared to RN-group-b (65.3 vs 88.2 %, p = 0.047). In RN-group-a patients, procedural times [200 (170-230) vs 152 (132-200) minutes, p < 0.01] and fluoroscopy times [16.6 (12.9-21.6) minutes vs 13.7 (9.5-19) minutes, p = 0.043] were significantly longer compared to RN-group-b patients. Conclusion: Long-term success rate after CPVI using RNS was comparable to manual ablation. Despite a lower power limit of 20 W at the posterior LA wall, single procedure success rate was higher in RN-group-b as compared to RN-group-a. Procedure time and fluoroscopy time decreased, whilst success rate increased with increasing experience in the RN-group. [ABSTRACT FROM AUTHOR]

In this paper, a control architecture based on the reactive paradigm to solve the problem of autonomous navigation of mobile robots with limited computational capabilities and sensor system is presented. Stand out as the most significant contributions to this work, the encapsulation of a behavior-based control architecture in a programmable embedded system with limited processing capabilities and the use of fuzzy control theory in the implementation of a wall following behavior. Experimental tests were performed on a robotic platform equipped with three ultrasonic sensors and a wireless communication module. This module supports the 802.15.4 protocol in the ISM (Industrial, Scientific and Medical) band, through it sensory measures are transferred to a PC where computational representations of the explored environment are made. [ABSTRACT FROM AUTHOR]

This paper proposes a new pose estimation algorithm in the framework of robotic navigation problems. The algorithm gives the mobile robot (MR) pose on the basis of the difference between the MR desired pose and the MR current pose. In this regard the MR sensor readings and the readings of a virtual sensor are employed. The algorithm is advantageous in comparison with other pose estimation algorithms including those based on classical filter approaches because of the small computation time. Simulation and real-world experimental results are included to illustrate the effectiveness of the pose estimation algorithm and its potential for integration in MR control structures and algorithms. [ABSTRACT FROM AUTHOR]

Background. Time-of-flight (TOF) cameras can guide surgical robots or provide soft tissue information for augmented reality in the medical field. In this study, a method to automatically track the soft tissue envelope of a minimally invasive hip approach in a cadaver study is described. Methods. An algorithm for the TOF camera was developed and 30 measurements on 8 surgical situs (direct anterior approach) were carried out. The results were compared to a manual measurement of the soft tissue envelope. Results. The TOF camera showed an overall recognition rate of the soft tissue envelope of 75%. On comparing the results from the algorithm with the manual measurements, a significant difference was found (P > .005). Conclusions. In this preliminary study, we have presented a method for automatically recognizing the soft tissue envelope of the surgical field in a real-time application. Further improvements could result in a robotic navigation device for minimally invasive hip surgery. [ABSTRACT FROM PUBLISHER]

The problem of autonomous navigation of small mobile robots in indoors is addressed. The proposed solution incorporates a behavior-based control architecture implemented through fuzzy logic, and a hierarchical scheme for behaviors coordination. This combination was tested in a small mobile robotic platform that has a ring with SRF02 sonars and a MCF51QE128 microcontroller resulting in a safe navigation of the environment at a low computational cost. [ABSTRACT FROM AUTHOR]

Background: Left atrial isthmus (LAI)-ablation in addition to circumferential pulmonary vein isolation (CPVI) may improve outcomes in select patients with atrial fibrillation (AF). However, bidirectional LAI-block is difficult to obtain. No systematic evaluation reporting on the feasibility and efficacy of LAI-ablation using a robotic navigation system (RNS) exists. Methods and results: In this pilot study, CPVI combined with LAI-ablation were performed using a RNS and 3D-mapping system in 42 patients with persistent ( n = 24, 57.1 %) or longstanding persistent AF. Ablation was performed using either a 3.5 mm irrigated tip catheter (ITC) with 6 (group-A, n = 16; max. 40 W, contact force 10–40 g) or (after a steam pop occurred in one patient) with a 4 mm ITC with 12 irrigation holes (group-B, n = 26; max. 30 W, contact force 10–30 g). Epicardial ablation was performed manually whenever bidirectional LAI-block could not be obtained with a maximum of 20 endocardial RF-applications. LAI-conduction block was achieved in all patients using RNS; in six patients (14.3 %), additional epicardial ablation was required to achieve LAI-block. A steam pop occurred during LAI-ablation resulting in cardiac tamponade in one patient in group-A. After a median follow-up period of 21 months, arrhythmia recurrence was seen in in 23/42 patients (18 patients with AF and 5 patients with atrial tachycardia) and repeat procedure was performed in 12 (28.6 %) patients; recovered LAI-conduction was found in 5/12 (41.7 %) patients. The RNS-group was compared to a historical group of 20 patients with manual LAI-ablation. Using RNS, LAI-block was more often achieved (42 (100 %) vs 16 (80 %), p < 0.01) and epicardial ablation was required in a significantly smaller number of patients (6 (14.3) vs 10 (50 %), p < 0.01). Conclusions: LAI-ablation using RNS appears to be feasible in all patients. At repeat procedure, LAI-conduction can frequently occur; power and contact-force adaption appears to be mandatory to reduce the risk of complications. Using RNS, instead of a manual approach for LAI-line ablation may facilitate creation of a bidirectional LAI-block. [ABSTRACT FROM AUTHOR]

Worldwide Survey on Robotic AF Ablation. Introduction: The Hansen Robotic system has been utilized in ablation procedures for atrial fibrillation (AF). However, because of the lack of tactile feedback and the rigidity of the robotic sheath, this approach could result in higher risk of complications. This worldwide survey reports a multicenter experience on the methodology, efficacy, and safety of the Hansen system in AF ablations. Methods and Results: A questionnaire addressing questions on patient's demographics, procedural parameters, ablation success rate and safety information was sent to all centers where more than 50 robotic AF ablation cases have been performed. From June 2007 to December 2009, 1,728 procedures were performed at 12 centers utilizing the Hansen robotic navigation technology. The overall complication rate was 4.7% and the success rate was 67.1% after 18 ± 4 months of follow-up. In 5 low volume centers there appeared to be a learning curve of about 50 cases (complication rate 11.2% for the first 50 cases vs 3.7% for the 51-100 cases; P = 0.044) and a trend showing a decrease of complication rate with increasing case volume. However, in the remaining 7 centers no learning curve was present and the complication rate was stable over time (3.7% for the first 50 cases vs 3.6% for the 51st case thereafter; P = 0.942). Conclusion: The Hansen robotic system can be used for AF ablation safely. In low volume centers, there appeared to be a learning curve of the first 50 cases after which the complication rate decreased. With a higher case volume, the success rate increased. (J Cardiovasc Electrophysiol, Vol. 23, pp. 820-826, August 2012) [ABSTRACT FROM AUTHOR]

The authors evaluate the occurrence and long-term follow-up of silent cerebral lesions (SCLs) after atrial fibrillation (AF) ablation in patients who underwent robotically assisted pulmonary vein isolation (PVI) and manual PVI. No significant difference in SCL incidence between patients under robotically assisted PVI and manual PVI. In the manual PVI group, one patient had transient ischemic attack after two days. No residual SCLs were found during long-term follow-up.

Background: Catheter ablation of ventricular tachycardia (VT) can be technically challenging due to difficulty with catheter positioning in the left ventricle (LV) and achieving stable contact. The Hansen Sensei Robotic system (HRS) has been used in atrial fibrillation but its utility in VT is unclear. Objective: The purpose of this study was to test the technical feasibility of robotic catheter ablation of LV ventricular tachycardia (VT) using the HRS. Methods: Twenty-three patients underwent LV VT mapping and ablation with the HRS via a transseptal, transmitral valve approach. Nineteen patients underwent substrate mapping and ablation (18 had ischemic cardiomyopathy, 1 had an apical variant of hypertrophic cardiomyopathy). Four patients had focal VT requiring LV VT mapping and ablation. Procedural endpoints included substrate modification by endocardial scar border ablation and elimination of late potentials, or elimination of inducible focal VT. Results: Mapping and ablation were entirely robotic without requiring manual catheter manipulation in all patients and reaching all LV regions with stable contact. Fluoroscopy time of the LV procedure was 22.2 ± 11.2 minutes. Radiofrequency time was 33 ± 21 minutes. Total procedural times were 231 ± 76 minutes. Complications included a left groin hematoma (opposite to the HRS sheath), 1 pericardial effusion without tamponade that was drained successfully, and transient right ventricular failure in a patient with previous left ventricular assist device. At 13.4 ± 6.7 months of follow-up (range 1–19 months), recurrence of VT occurred in 3 of 23 patients. Conclusion: Our initial experience suggests that the HRS allows successful mapping and ablation of LV VT. [Copyright &y& Elsevier]