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Exhibit describes a new detection method for identifying earthquake precursors through identification of low-frequency electromagnetic signatures in continuously monitored and corrected Global Navigation Satellite and GPS signals.
Purpose: We compare the accuracy of HDR catheter placement for paraspinal lesions using O-arm CBCT imaging combined with StealthStation navigation and traditional fluoroscopically guided catheter placement. Methods: CT and MRI scans were acquired pre-treatment to outline the lesions and design treatment plans (pre-plans) to meet dosimetric constrains. The pre-planned catheter trajectories were transferred into the StealthStation Navigation system prior to the surgery. The StealthStation is an infra red (IR) optical navigation system used for guidance of surgical instruments. An intraoperative CBCT scan (O-arm) was acquired with reference IR optical fiducials anchored onto the patient and registered with the preplan image study to guide surgical instruments in relation to the patients’ anatomy and to place the brachytherapy catheters along the pre-planned trajectories. The final treatment plan was generated based on a 2nd intraoperative CBCT scan reflecting achieved implant geometry. The 2nd CBCT was later registered with the initial CT scan to compare the preplanned dwell positions with actual dwell positions (catheter placements). Similar workflow was used in placement of 8 catheters (1 patient) without navigation, but under fluoroscopy guidance in an interventional radiology suite. Results: A total of 18 catheters (3 patients) were placed using navigation assisted surgery. Average displacement of 0.66 cm (STD=0.37cm) was observed between the pre-plan source positions and actual source positions in the 3 dimensional space. This translates into an average 0.38 cm positioning error in one direction including registration errors, digitization errors, and the surgeons ability to follow the planned trajectory. In comparison, average displacement of non-navigated catheters was 0.50 cm (STD=0.22cm). Conclusion: Spinal lesion HDR brachytherapy planning is a difficult task. Catheter placement has a direct impact on target coverage and dose to critical structures. While limited to a handful of patients, our experience shows navigation and fluoroscopy guided placement yield similar results.
PurposeThe present study was designed to evaluate the geometrical accuracy and clinical applicability of a new, free-hand, CT-guided, optical navigation system.MethodsFifteen procedures in 14 consecutive patients were retrospectively analyzed. The navigation system was applied for interventional procedures on small target lesions, in cases with long needle paths, narrow access windows, or when an out-of-plane access was expected. Mean lesion volume was 27.9 ml, and mean distance to target measured was 107.5 mm. Eleven of 15 needle trajectories were planned as out-of-plane approaches regarding the axial CT plane.ResultsNinety-one percent of the biopsies were diagnostic. All therapeutic interventions were technically successful. Targeting precision was high with a mean distance of the needle tip from planned target of 1.98 mm. Mean intervention time was 1:12 h. A statistically significant correlation between angular needle deviation and intervention time (p = 0.007), respiratory movement of the target (p = 0.008), and body mass index (p = 0.02) was detected. None of the evaluated parameters correlated significantly with the distance from the needle tip to the planned target.ConclusionsThe application of a navigation system for complex CT-guided procedures provided safe and effective targeting within a reasonable intervention time in our series.
The paper develops a real-time vehicle-specific eco-routing model for use in on-board navigation systems for both conventional fuel- and battery-powered electric vehicles. The model uses a novel link cost function that retains all microscopic transient behavior along a link by capturing within-link speed, acceleration, and road grade variations. Specifically, eight vehicle-agnostic link-specific variables are transmitted to the cloud and fused with information received from other connected vehicles. The fused data are then sent back to the connected vehicles and used to compute vehicle-specific eco-routes (edge computing). The solution produces an energy-efficient dynamic user-equilibrium traffic assignment that is solved incrementally. A numerical experiment is first designed to test the model, demonstrating the benefits associated with vehicle-specific eco-routes compared to those produced by traditional models. The model is then implemented in a micro-simulation framework to evaluate the network-wide impacts of the proposed energy-efficient user-optimum incremental traffic assignment for different traffic demand levels. Results demonstrate that the proposed model produces lower network-wide energy consumption levels compared to traditional energy-efficient and travel time user-optimum traffic assignment methods for both conventional fuel and electric vehicles. Conventional fuel savings relative to travel time routing decrease with increasing traffic demand levels, while electric power savings do not vary monotonically with congestion levels. Energy savings relative to traditional eco-routing are also demonstrated to not vary monotonically with demand levels. Results of the study also demonstrate that the transportation network layout significantly affects the eco-routing system performance. Finally, the feasibility of the proposed model in real-world applications is discussed and presented.
Purpose: Images from computed tomography (CT), combined with navigation systems, improve the outcomes of local thermal therapies that are dependent on accurate probe placement. Although the usage of CT is desired, its availability for time-consuming radiological interventions is limited. Alternatively, three-dimensional images from C-arm cone-beam CT (CBCT) can be used. The goal of this study was to evaluate the accuracy of navigated CBCT-guided needle punctures, controlled with CT scans. Methods: Five series of five navigated punctures were performed on a nonrigid phantom using a liver specific navigation system and CBCT volumetric dataset for planning and navigation. To mimic targets, five titanium screws were fixed to the phantom. Target positioning accuracy (TPE{sub CBCT}) was computed from control CT scans and divided into lateral and longitudinal components. Additionally, CBCT-CT guidance accuracy was deducted by performing CBCT-to-CT image coregistration and measuring TPE{sub CBCT-CT} from fused datasets. Image coregistration was evaluated using fiducial registration error (FRE{sub CBCT-CT}) and target registration error (TRE{sub CBCT-CT}). Results: Positioning accuracies in lateral directions pertaining to CBCT (TPE{sub CBCT} = 2.1 {+-} 1.0 mm) were found to be better to those achieved from previous study using CT (TPE{sub CT} = 2.3 {+-} 1.3 mm). Image coregistration error was 0.3 {+-} 0.1 mm, resulting in an average TRE of 2.1 {+-} 0.7 mm (N = 5 targets) and average Euclidean TPE{sub CBCT-CT} of 3.1 {+-} 1.3 mm. Conclusions: Stereotactic needle punctures might be planned and performed on volumetric CBCT images and controlled with multidetector CT with positioning accuracy higher or similar to those performed using CT scanners.
This data set presents results testing the station keeping abilities of a tethered Seabotix vLBV300 underwater vehicle equipped with an inertial navigation system. These results are from an offshore deployment on April 20, 2016 off the coast of Newport, OR (44.678 degrees N, 124.109 degrees W). During the mission period, the sea state varied between 3 and 4, with an average significant wave height of 1.6 m. The vehicle utilizes an inertial navigation system based on a Gladiator Landmark 40 IMU coupled with a Teledyne Explorer Doppler Velocity Log to perform station keeping at a desired location and orientation.
A system and method to distinguish spoofing signals from true GNSS signals is disclosed. One aspect of the present invention combines measuring GNSS carrier signals with measuring jitter in a vehicle's position via a low-cost inertial measurement unit (IMU) to distinguish spoofing signals from true GNSS signals. The present invention employs natural and/or artificial jitter of a vehicle, that, when combined with a tightly coupled inertial navigation system (INS), allows the receiver to distinguish the spoofing GNSS signal from the true GNSS signal. This spoofer survivability algorithm may be implemented, for example, in the software of a GNSS (or GPS) navigation system.
Purpose: Registration is one of the key technical components in an image-guided navigation system. A large number of 2D/3D registration algorithms have been previously proposed, but have not been able to transition into clinical practice. The authors identify the primary reason for the lack of adoption with the prerequisite for a sufficiently accurate initial transformation, mean target registration error of about 10 mm or less. In this paper, the authors present two interactive initialization approaches that provide the desired accuracy for x-ray/MR and x-ray/CT registration in the operating room setting. Methods: The authors have developed two interactive registration methods based on visual alignment of a preoperative image, MR, or CT to intraoperative x-rays. In the first approach, the operator uses a gesture based interface to align a volume rendering of the preoperative image to multiple x-rays. The second approach uses a tracked tool available as part of a navigation system. Preoperatively, a virtual replica of the tool is positioned next to the anatomical structures visible in the volumetric data. Intraoperatively, the physical tool is positioned in a similar manner and subsequently used to align a volume rendering to the x-ray images using an augmented reality (AR) approach. Both methods were assessed using three publicly available reference data sets for 2D/3D registration evaluation. Results: In the authors' experiments, the authors show that for x-ray/MR registration, the gesture based method resulted in a mean target registration error (mTRE) of 9.3 ± 5.0 mm with an average interaction time of 146.3 ± 73.0 s, and the AR-based method had mTREs of 7.2 ± 3.2 mm with interaction times of 44 ± 32 s. For x-ray/CT registration, the gesture based method resulted in a mTRE of 7.4 ± 5.0 mm with an average interaction time of 132.1 ± 66.4 s, and the AR-based method had mTREs of 8.3 ± 5.0 mm with interaction times of 58 ± 52 s. Conclusions: Based on the authors' evaluation, the authors conclude that the registration approaches are sufficiently accurate for initializing 2D/3D registration in the OR setting, both when a tracking system is not in use (gesture based approach), and when a tracking system is already in use (AR based approach)
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This study describes a light detection and ranging (LiDAR)–based autonomous navigation system for an ultralightweight ground robot in agricultural fields. The system is designed for reliable navigation under cluttered canopies using only a 2D Hokuyo UTM–30LX LiDAR sensor as the single source for perception. Its purpose is to ensure that the robot can navigate through rows of crops without damaging the plants in narrow row–based and high–leaf–cover semistructured crop plantations, such as corn ( Zea mays ) and sorghum ( Sorghum bicolor ). The key contribution of our work is a LiDAR–based navigation algorithm capable of rejecting outlying measurements in the point cloud due to plants in adjacent rows, low–hanging leaf cover or weeds. The algorithm addresses this challenge using a set of heuristics that are designed to filter out outlying measurements in a computationally efficient manner, and linear least squares are applied to estimate within–row distance using the filtered data. Moreover, a crucial step is the estimate validation, which is achieved through a heuristic that grades and validates the fitted row–lines based on current and previous information. The proposed LiDAR–based perception subsystem has been extensively tested in production/breeding corn and sorghum fields. In such variety of highly cluttered real field environments, the robot logged more than 6 km of autonomous run in straight rows. These results demonstrate highly promising advances to LiDAR–based navigation in realistic field environments for small under–canopy robots.
Unmanned aerial vehicles (UAVs) equipped with integrated global navigation satellite systems/inertial navigation systems (GNSS/INS) together with cameras and/or LiDAR sensors are being widely used for topographic mapping in a variety of applications such as precision agriculture, coastal monitoring, and archaeological documentation. Integration of image-based and LiDAR point clouds can provide a comprehensive 3D model of the area of interest. For such integration, ensuring a good alignment between data from the different sources is critical. Although many works have been conducted on this topic, there is still a need for a rigorous integration approach that minimizes the discrepancy between camera and LiDAR data caused by inaccurate system calibration parameters and/or trajectory artifacts. This study proposes an automated tightly-coupled camera/LiDAR integration workflow for GNSS/INS-assisted UAV systems. The proposed strategy is conducted in three main steps. First, an image-based point cloud is generated using a LiDAR/GNSS/INS-assisted structure from motion (SfM) strategy. Then, feature correspondences between image-based and LiDAR point clouds are automatically identified. Finally, an integrated-bundle adjustment procedure including image points, LiDAR raw measurements, and GNSS/INS information is conducted to minimize the discrepancy between point clouds from different sensors while estimating system calibration parameters and refining the trajectory information. The proposed SfM strategy and integration framework are evaluated using five datasets. The SfM results show that using LiDAR data can facilitate feature matching and further increase the number of reconstructed 3D points. The experimental results also illustrate that the developed automated camera/LiDAR integration strategy is capable of accurately estimating system calibration parameters to achieve good alignment among camera/LiDAR data from single/multiple systems. Finally, an absolute accuracy in the range of 3–5 cm is achieved for the image/LiDAR point clouds after the integration process.
This report outlines marine field demonstrations for manipulation tasks with a semi-Autonomous Underwater Vehicle (sAUV). The vehicle is built off a Seabotix vLBV300 platform with custom software interfacing it with the Robot Operating System (ROS). The vehicle utilizes an inertial navigation system available from Greensea Systems, Inc. based on a Gladiator Landmark 40 IMU coupled with a Teledyne Explorer Doppler Velocity Log to perform station keeping at a desired location and orientation. We performed two marine trials with the vehicle: a near-shore shared autonomy manipulation trial and an offshore attempted intervention trial. These demonstrations were designed to show the capabilities of our sAUV system for inspection and basic manipulation tasks in real marine environments.
The use of the Global Positioning System (GPS) is a fundamental requirement for most navigation systems today, and this heavy reliance means that denial of GPS service (or extended threats) can pose a significant risk to modern navigation. There is an urgent need for enabling, high-accuracy navigation technologies that can operate without the need for GPS. Ideally, these solutions must be able to initialize in a completely GPS-free environment and continue to navigate even through challenging scenarios. The increasing risk posed to GPS means that trust in this platform is waning—and solutions are required. A future navigator should leverage GPS whenever possible and be capable of identifying and responding to risks while maintaining mission accuracy needs. In the absence of GPS, fully alternative navigation (altnav) technologies are required. This report describes an introductory view of altnav for GPS-impaired and contested environments. Various technologies are collected, presented, and evaluated as potential solutions. A wide snapshot of currently available technologies with a first-order summary of their potential is presented. While this report attempts to be as broad and complete as possible, this is a quickly evolving field.
A three axis electronic flight motion simulator is reported in this paper including the modelling, the controller design as well as the hardware implementation. This flight motion simulator could be used for inertial navigation test and high precision inertial navigation system with good dynamic and static performances. A real time control system is designed, several control system implementation problems were solved including time unification with parallel port interrupt, high speed finding-zero method of rotary inductosyn, zero-crossing management with continuous rotary, etc. Tests were carried out to show the effectiveness of the proposed real time control system.
Inspection of coal-fired power plants is frequently dangerous, includes difficult places to reach, and can turn expensive due to the downtimes and cost of inspection crew. Robotic systems have shown capabilities to address some of these issues, but most of the current robotic inspection technology in power plants is designed for specific components. Conversely, recent advances in machine vision have empowered aerial platforms for long-range, remotely-controlled, GPS-based inspections of industrial plants. This capability has led to wide spread utilization of aerial robots (commonly termed Drones, UVS or UAS) platforms for inspection in less challenging environments where both collision avoidance, and GPS reception are not significant issues. The challenge in adapting airborne technology for power plant inspection lies in internal structures and the complex network of piping, and distribution systems, which impose significant risks for collision and can hinder the reception and transmission of GPS signals. The current state of the art in aerial inspection technology within the energy sector is controlled via radio control, and utilizes GPS-based navigation, for inspection of large-scale plants such as offshore platforms and wind turbine parks. Nevertheless, close-range and autonomous inspection in the GPS-denied environments of power plants has not yet been achieved, as it requires precise guidance and navigation with real-time situational awareness and obstacle avoidance capabilities. This endeavor introduced the use of rotary wing flying robots, due to their station keeping and vertical take-off capabilities for power plant components inspection. To enable close quarter inspection two methods were used. One method uses the 3D CAD (Three-dimensional Computer-Aided Design) model of the asset to inspect to generate the UAV’s inspection path. To acquire, analyze and process the 3D model, first, the STL file is produced to obtain surface points and vectors normal to the surface. Later, by introducing other variables such as wall offset and a controlled trajectory between each outline and each subsequent layer, the flight path is generated. The proposed framework will generate a path that will pass as close as desired from the surface and navigate in intricate environments. A second method, use advanced manufacturing techniques such as CNC (Computer Numerical Control) and additive manufacturing. Once the inspection flight path is obtained, vision-based navigation systems are employed to have the UAV autonomously tracking the provided trajectory. Finally, Artificial Intelligence-enabled developments are in charge of detecting cracks and corrosion in structural components of power plants. The proposed methods are validated in simulations, laboratory and industrial setups, where it is shown that the developed systems acting together enable close-quarter autonomous aerial inspection and mapping in power plant assets. The system can be further improved by adding more sensors to navigate in different GPS-denied environments, with non-homogeneous lighting conditions, dust and in general situations where vision-based systems may fail.
Unmanned aerial vehicles (UAVs ) equipped with imaging sensors and integrated global navigation satellite system/inertial navigation system (GNSS/INS ) units are used for numerous applications. Deriving reliable 3D coordinates from such UAVs is contingent on accurate geometric calibration, which encompasses the estimation of mounting parameters and synchronization errors. Through a rigorous impact analysis of such systematic errors, this article proposes a direct approach for spatial and temporal calibration (estimating system parameters through a bundle adjustment procedure) of a GNSS/INS -assisted pushbroom scanner onboard a UAV platform. The calibration results show that the horizontal and vertical accuracies are within the ground sampling distance of the sensor. Unlike for frame camera systems, this article also shows that the indirect approach is not a feasible solution for pushbroom scanners due to their limited ability for decoupling system parameters. This finding provides further support that the direct approach is recommended for spatial and temporal calibration of UAV pushbroom scanner systems.
Requirements to gyroscopes of strapdown inertial navigation systems for aircraft application are formulated. The construction of a ring helium – neon laser designed for autonomous navigation is described. The processes that determine the laser service life and the relation between the random error of the angular velocity measurement and the surface relief features of the cavity mirrors are analysed. The results of modelling one of the promising approaches to processing the laser gyroscope signals are presented. (laser gyroscopes)