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Mobile robots' efficient path planning has long been a challenging task due to the complexity and dynamism of environments. If an occupancy grid map is used in path planning, the number of grids is determined by grid resolution and the size of the actual environment. Excessively high resolution increases the number of traversed grid nodes and thus prolongs path planning time. To address this challenge, this paper proposes an efficient path planning algorithm based on laser SLAM and an optimized visibility graph for mobile robots, which achieves faster computation of the shortest path using the optimized visibility graph. Firstly, the laser SLAM algorithm is used to acquire the undistorted LiDAR point cloud data, which are converted into a visibility graph. Secondly, a bidirectional A* path search algorithm is combined with the Minimal Construct algorithm, enabling the robot to only compute heuristic paths to the target node during path planning in order to reduce search time. Thirdly, a filtering method based on edge length and the number of vertices of obstacles is proposed to reduce redundant vertices and edges in the visibility graph. Additionally, the bidirectional A* search method is implemented for pathfinding in the efficient path planning algorithm proposed in this paper to reduce unnecessary space searches. Finally, simulation and field tests are conducted to validate the algorithm and compare its performance with classic algorithms. The test results indicate that the method proposed in this paper exhibits superior performance in terms of path search time, navigation time, and distance compared to D* Lite, FAR, and FPS algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a high torque density dual-stator flux-reversal-machine with multiple poles Halbach excitation (MPHE-DSFRM), which uses two pole pairs' numbers (PPNs) of PM excitation on one outer stator tooth, and one PPN of PM excitation on one inner stator tooth. The introduction of different PPNs of PM excitation on the outer and the inner stators can optimize magnetic circuit and airgap flux density. A Halbach array is formed by inserting three pieces of circumferentially magnetized PMs into four pieces of radially magnetized permanent magnets (PMs) on the outer stator, which aims to further enhance torque density, and reduce torque ripple. Based on the flux modulation effect, the analytical modeling of the proposed MPHE-DSFRM is established, together with the evolution process, and the working principle is presented. Then, the key design parameters of MPHE-DSFRM are optimized to achieve high torque density and low torque ripple for high torque quality. Three representative DSFRMs and a conventional FRM are designed and analyzed, and they share the same design key parameters, including PM usage, outer radius of the outer stator, and active airgap length. The electromagnetic performances, including airgap flux density, back electromotive force (back-EMF), and torque characteristics, are analyzed and compared by finite element analysis (FEA). The calculated results show that the proposed MPHE-DSFRM can provide high torque density and high PM utilization. [ABSTRACT FROM AUTHOR]

Several advantages of directed energy deposition-arc (DED-arc) have garnered considerable research attention including high deposition rates and low costs. However, defects such as discontinuity and pores may occur during the manufacturing process. Defect identification is the key to monitoring and quality assessments of the additive manufacturing process. This study proposes a novel acoustic signal-based defect identification method for DED-arc via wavelet time–frequency diagrams. With the continuous wavelet transform, one-dimensional (1D) acoustic signals acquired in situ during manufacturing are converted into two-dimensional (2D) time–frequency diagrams to train, validate, and test the convolutional neural network (CNN) models. In this study, several CNN models were examined and compared, including AlexNet, ResNet-18, VGG-16, and MobileNetV3. The accuracy of the models was 96.35%, 97.92%, 97.01%, and 98.31%, respectively. The findings demonstrate that the energy distribution of normal and abnormal acoustic signals has significant differences in both the time and frequency domains. The proposed method is verified to identify defects effectively in the manufacturing process and advance the identification time. [ABSTRACT FROM AUTHOR]

The aim of this paper is to address the surface reconstruction from point cloud in reverser engineering. The data was acquired through a 3D scan device and was processed as point cloud data. The points in cloud were connected to build 3D surface. The points cloud was processed in four steps to get 3D information surface. First, the subtraction scheme was used to get cover boxes ended with the set of convex was found under the convergence rule. Secondly, the points in the box were projected to the directions which were close to the normal direction method. Thirdly the overlap was avoided by using convergence rule and inner subdivision rule. Finally the information model was used to reconstruction. The method was used in landslide monitoring of Three Gorges area for 3D surface reconstruction and monitoring. The reconstruction method obtains high precision and low complexity. It is effective for large scale monitoring.