GNSS Aided Long-Range 3D Displacement Sensing for High-Rise Structures with Two Non-Overlapping Cameras

Image-based displacement measurement techniques are widely used for sensing the deformation of structures, and plays an increasing role in structural health monitoring owing to its benefit of non-contacting. In this study, a non-overlapping dual camera measurement model with the aid of global navigation satellite system (GNSS) is proposed to sense the three-dimensional (3D) displacements of high-rise structures. Each component of the dual camera system can measure a pair of displacement components of a target point in a 3D space, and its pose relative to the target can be obtained by combining a built-in inclinometer and a GNSS system. To eliminate the coupling of lateral and vertical displacements caused by the perspective projection, a homography-based transformation is introduced to correct the inclined image planes. In contrast to the stereo vision-based displacement measurement techniques, the proposed method does not require the overlapping of the field of views and the calibration of the vision geometry. Both simulation and experiment demonstrate the feasibility and correctness of the proposed method, heralding that it has a potential capacity in the field of remote health monitoring for high-rise buildings.

SCRnet: A Spatial Consistency Guided Network Using Contrastive Learning for Point Cloud Registration

Point cloud registration is used to find a rigid transformation from the source point cloud to the target point cloud. The main challenge in the point cloud registration is in finding correct correspondences in complex scenes that may contain many noise and repetitive structures. At present, many existing methods use outlier rejections to help the network obtain more accurate correspondences, but they often ignore the spatial consistency between keypoints. Therefore, to address this issue, we propose a spatial consistency guided network using contrastive learning for point cloud registration (SCRnet), in which its overall stage is symmetrical. SCRnet consists of four blocks, namely feature extraction block, confidence estimation block, contrastive learning block and registration block. Firstly, we use mini-PointNet to extract coarse local and global features. Secondly, we propose confidence estimation block, which formulate outlier rejection as confidence estimation problem of keypoint correspondences. In addition, the local spatial features are encoded into the confidence estimation block, which makes the correspondence possess local spatial consistency. Moreover, we propose contrastive learning block by constructing positive point pairs and hard negative point pairs and using Point-Pair-INfoNCE contrastive loss, which can further remove hard outliers through global spatial consistency. Finally, the proposed registration block selects a set of matching points with high spatial consistency and uses these matching sets to calculate multiple transformations, then the best transformation can be identified by initial alignment and Iterative Closest Point (ICP) algorithm. Extensive experiments are conducted on KITTI and nuScenes dataset, which demonstrate the high accuracy and strong robustness of SCRnet on point cloud registration task.

Unusual Removal of T-DNA in T1 Progenies of Rice after Agrobacterium-mediated CRISPR/Cas9 Editing

In the present study, we attempted to knock out the bar gene selection marker in the fixed Bt- and herbicide-resistant transgenic line BT-T07 (T8 generation) to generate a marker-free Bt-resistant rice line. A binary vector containing a CRISPR/Cas9 system targeting the 108–130 bp region of bar was transformed into rice embryonic calli, and plantlets were regenerated under non-selective conditions. Three T0 plants were observed to have non-target point mutations and deletions in the targeted gene and were putatively heterozygous and chimeras. One T0 plant, 130-4, was confirmed to have a 76-nt deletion, from 140 bp to 225 bp, and it showed the segregation of bar in its T1 progenies, with 16 bar-knockout lines and seven normal bar-expressing lines. However, the CRISPR/Cas9 editing vector sequences were not detected in any of the T1 plants. In addition, unusual removal of pre-existing T-DNA was observed in all bar-knockout T1 plants. Illumina sequencing of a bar-knockout line, 130-4-36, revealed a small fraction of read residues of pre-existing T-DNA from the bar sequence to the right border at the original junction site. We speculate that this rare phenomenon might be directed by the homology between pre-existing T-DNA and CRISPR/Cas9 editing vector sequences during meiotic recombination. We report imprecise modifications and unpredictable outcomes of gene-editing techniques, providing valuable perspectives on gene-editing applications.

Application of the Improved Grey Wolf Algorithm in Spacecraft Maneuvering Path Planning

In many space missions, spacecraft are required to have the ability to avoid various obstacles and finally reach the target point. In this paper, the path planning of spacecraft attitude maneuver under boundary constraints and pointing constraints is studied. The boundary constraints and orientation constraints are constructed as finite functions of path evaluation. From the point of view of optimal time and shortest path, the constrained attitude maneuver problem is reduced to optimal time and path solving problem. To address this problem, a metaheuristic maneuver path planning method is proposed (cross-mutation grey wolf algorithm (CMGWO)). In the CMGWO method, we use angular velocity and control torque coding to model attitude maneuver, which increases the difficulty of solving the problem. In order to deal with this problem, the grey wolf algorithm is used for mutation and evolution, so as to reduce the difficulty of solving the problem and shorten the convergence time. Finally, simulation analysis is carried out under different conditions, and the feasibility and effectiveness of the method are verified by numerical simulation.

Optimized Query Algorithms for Top- K Group Skyline

Skyline query is a typical multiobjective query and optimization problem, which aims to find out the information that all users may be interested in a multidimensional data set. Multiobjective optimization has been applied in many scientific fields, including engineering, economy, and logistics. It is necessary to make the optimal decision when two or more conflicting objectives are weighed. For example, maximize the service area without changing the number of express points, and in the existing business district distribution, find out the area or target point set whose target attribute is most in line with the user’s interest. Group Skyline is a further extension of the traditional definition of Skyline. It considers not only a single point but a group of points composed of multiple points. These point groups should not be dominated by other point groups. For example, in the previous example of business district selection, a single target point in line with the user’s interest is not the focus of the research, but the overall optimality of all points in the whole target area is the final result that the user wants. This paper focuses on how to efficiently solve top- k group Skyline query problem. Firstly, based on the characteristics that the low levels of Skyline dominate the high level points, a group Skyline ranking strategy and the corresponding SLGS algorithm on Skyline layer are proposed according to the number of Skyline layer and vertices in the layer. Secondly, a group Skyline ranking strategy based on vertex coverage is proposed, and corresponding VCGS algorithm and optimized algorithm VCGS+ are proposed. Finally, experiments verify the effectiveness of this method from two aspects: query response time and the quality of returned results.

Rediscovery of the transcerebellar approach: improving the risk-benefit ratio in robot-assisted brainstem biopsies

OBJECTIVE Conventional frame-based stereotaxy through a transfrontal approach (TFA) is the gold standard in brainstem biopsies. Because of the high surgical morbidity and limited impact on therapy, brainstem biopsies are controversial. The introduction of robot-assisted stereotaxy potentially improves the risk-benefit ratio by simplifying a transcerebellar approach (TCA). The aim of this single-center cohort study was to evaluate the risk-benefit ratio of transcerebellar brainstem biopsies performed by 2 different robotic systems. In addition to standard quality indicators, a special focus was set on trajectory selection for reducing surgical morbidity. METHODS This study included 25 pediatric (n = 7) and adult (n = 18) patients who underwent 26 robot-assisted biopsies via a TCA. The diagnostic yield, complication rate, trajectory characteristics (i.e., length, anatomical entry, and target-point location), and skin-to-skin (STS) time were evaluated. Transcerebellar and hypothetical transfrontal trajectories were reconstructed and transferred into a common MR space for further comparison with anatomical atlases. RESULTS Robot-assisted, transcerebellar biopsies demonstrated a high diagnostic yield (96.2%) while exerting no surgical mortality and no permanent morbidity in both pediatric and adult patients. Only 3.8% of cases involved a transient neurological deterioration. Transcerebellar trajectories had a length of 48.4 ± 7.3 mm using a wide stereotactic corridor via crus I or II of the cerebellum and the middle cerebellar peduncle. The mean STS time was 49.5 ± 23.7 minutes and differed significantly between the robotic systems (p = 0.017). The TFA was characterized by longer trajectories (107.4 ± 11.8 mm, p < 0.001) and affected multiple eloquent structures. Transfrontal target points were located significantly more medial (−3.4 ± 7.2 mm, p = 0.042) and anterior (−3.9 ± 8.4 mm, p = 0.048) in comparison with the transcerebellar trajectories. CONCLUSIONS Robot-assisted, transcerebellar stereotaxy can improve the risk-benefit ratio of brainstem biopsies by avoiding the restrictions of a TFA and conventional frame-based stereotaxy. Profound registration and anatomical-functional trajectory selection were essential to reduce mortality and morbidity.

Leaderless Maneuver Guidance and Event-Triggered Formation Control for Distributed Multi-Space-Robot Systems

In this paper, the distributed displacement-based formation and leaderless maneuver guidance control problems of multi-space-robot systems are investigated by introducing event-triggered control update mechanisms. A distributed formation and leaderless maneuver guidance control framework is first constructed, which includes two parallel controllers, namely, an improved linear quadratic regulator and a distributed consensus-based formation controller. By applying this control framework, the desired formation configuration of multi-space-robot systems can be achieved and the center of leaderless formation can converge to the target point globally. Second, a pull-based event triggering mechanism is introduced to the distributed formation controller, which makes the control update independent of the events of neighboring robots, avoids unnecessary control updates, and saves the extremely limited energy of space robots. Furthermore, the potential Zeno behaviors have been excluded by proving a positive lower bound for the inter-event times. Finally, numerical simulation verifies the effectiveness of the theoretical results.

Vehicle Motion Planning for the Visually Challenged People Using Ant Colony Optimization

This paper presents a novel proposal to solve the problem of obstacle detection and path planning for visually challenged people based on simple Ant Colony Optimization Meta-heuristic (SACO-MH). The mission of the path planning problem is to enable the vehicle to move from the starting point to the target point while satisfying certain constraints. Constraint conditions are: not a collision with known or unknown obstacles, away from the obstacle as far as possible, determines the shortest path, shortest time and so on. Obstacle detection is made with the help of sensor technology and it is intimated to the user with the help of a smart watch. A voice based navigation system guides the user

DESIGN OF INDOOR ROBOT PROTOTYPE GUIDED BY RFID BASED POSITIONING AND NAVIGATION SYSTEM

In this study, a robot prototype was designed for indoor spaces guided by an RFID-based positioning and navigation system. First, the work area was prepared from cardboard material and RFID cards were placed at predetermined points in the work area. The unique ID number of each RFID card was defined and the coordinates of their location in the work area are known. The RFID reader in the robot prototype reads from less than 5 cm. With a basic approach, when the robot reads an RFID card that it passes over while in motion, the position of the robot is considered the same as the position of the card it is currently reading. The route is defined for the robot prototype whose location is known before starting the movement. When the robot reads a new RFID card during movement, it must move forward or turn left or right to reach the point where the next RFID card is located according to the route. This decision was predetermined and defined according to its location. Alphabot was used as the prototype. Arduino board and additional auxiliary sensors such as gyro sensor, speed sensors, distance sensors are placed on the prototype. The prototype robot is left at any point in the work area and arrives at a target point determined by the user. The required road route to reach the destination is calculated with the shortest path algorithm depending on the road network on the working area and the route is defined. Thus, it is ensured that the prototype reaches the target without any external intervention by the user other than target determination.

Intelligent Point Cloud Edge Detection Method Based on Projection Transformation

An edge detection method based on projection transformation is proposed. First, the vertical projection transformation is carried out on the target point cloud. Data X and data Y are normalized to the width and height of the image, respectively. Data Z is normalized to the range of 0-255, and the depth represents the gray level of the image. Then, the Canny algorithm is used to detect the edge of the projection transformed image, and the detected edge data is back projected to extract the edge point cloud in the point cloud. Evaluate the performance by calculating the normal vector of the edge point cloud. Compared with the normal vector of the whole data point cloud of the target, the normal vector of the edge point cloud can well express the characteristics of the target, and the calculation time is reduced to 10% of the original.