An Improved A-Star Path Planning Algorithm Based on Mobile Robots in Medical Testing Laboratories.

In the blood sample management pipeline environment, we have innovatively improved the traditional A-star algorithm to enhance the efficiency of mobile robots. This study employs a grid environmental modeling approach to accurately simulate medical testing laboratories. On the grid map, we utilize an 8-neighbor search method for path planning to accommodate the complex structure within the laboratory. By introducing an improved evaluation function and a bidirectional search strategy, we have successfully reduced the number of search nodes and significantly improved path search efficiency. Additionally, we eliminate redundant nodes in the path, smooth the path using cubic uniform B-spline curves, remove unnecessary inflection points, and further optimize the motion trajectory of the robot. The experimental results of the path planning simulation under different scenarios and specifications show that the improved A-star algorithm has higher search efficiency and traverses fewer nodes compared to the traditional A-star algorithm and the bidirectional A-star algorithm. Overall, the simulation experiments verify the feasibility of the improved A-star algorithm, which can better meet the needs of actual mobile robots in real medical testing laboratories.

Improved A* Algorithm for Path Planning of Spherical Robot Considering Energy Consumption.

Spherical robots have fully wrapped shells, which enables them to walk well on complex terrains, such as swamps, grasslands and deserts. At present, path planning algorithms for spherical robots mainly focus on finding the shortest path between the initial position and the target position. In this paper, an improved A* algorithm considering energy consumption is proposed for the path planning of spherical robots. The optimization objective of this algorithm is to minimize both the energy consumption and path length of a spherical robot. A heuristic function constructed with the energy consumption estimation model (ECEM) and the distance estimation model (DEM) is used to determine the path cost of the A* algorithm. ECEM and DCM are established based on the force analysis of the spherical robot and the improved Euclidean distance of the grid map, respectively. The effectiveness of the proposed algorithm is verified by simulation analysis based on a 3D grid map and a spherical robot moving with uniform velocity. The results show that compared with traditional path planning algorithms, the proposed algorithm can minimize the energy consumption and path length of the spherical robot as much as possible.

Global Path Planning of Unmanned Surface Vehicle Based on Improved A-Star Algorithm.

To make unmanned surface vehicles that are better applied to the field of environmental monitoring in inland rivers, reservoirs, or coasts, we propose a global path-planning algorithm based on the improved A-star algorithm. The path search is carried out using the raster method for environment modeling and the 8-neighborhood search method: a bidirectional search strategy and an evaluation function improvement method are used to reduce the total number of traversing nodes; the planned path is smoothed to remove the inflection points and solve the path folding problem. The simulation results reveal that the improved A-star algorithm is more efficient in path planning, with fewer inflection points and traversing nodes, and the smoothed paths are more to meet the actual navigation demands of unmanned surface vehicles than the conventional A-star algorithm.

Efficient and optimal penetration path planning for stealth unmanned aerial vehicle using minimal radar cross-section tactics and modified A-Star algorithm.

Penetration path planning for stealth unmanned aerial vehicles (SUAVs) in the integrated air defense system (IADS) has been a hot research topic in recent years. The present study examines penetration path planning in different threat environments. Firstly, for the complex terrain and static radar threats, a modified A-Star algorithm containing the bidirectional sector expansion and variable step search strategy is proposed to elude static threats rapidly. Then, with regard to bandit threats, the minimal radar cross-section (RCS) tactics are presented to achieve path replanning. Furthermore, the combinatorial methodology of the minimum RCS tactics and the modified A-Star algorithm is applied to achieve the dynamic path planning for SUAV. The simulation results indicate that the modified A-Star algorithm and minimal RCS tactics can significantly reduce the probability of radar system, which has better superiority in calculation efficiency, path cost and safety. And the minimal RCS tactics have better real-time performance and are more convenient in dealing with dynamic threats, which enhances the survivability of SUAV and verifies the effectiveness of the proposed methodology.

Investigation of TaC and TiC for Particle Strengthening of Co-Re-Based Alloys.

Cobalt-Rhenium (Co-Re)-based alloys are currently investigated as potential high-temperature materials with melting temperatures beyond those of nickel-based superalloys. Their attraction stems from the binary Co-Re phase diagram, exhibiting complete miscibility between Co and Re, whereby the melting temperature steadily increases with the Re-content. Thus, depending on the Re-content, one can tune the melting temperature between that of pure Co (1495 °C) and that of pure Re (3186 °C). Current investigations focus on Re-contents of about 15 at.%, which makes melting with standard equipment still feasible. In addition to solid solution strengthening due to the mixture of Co- and Re-atoms, particle strengthening by tantalum carbide (TaC) and titanium carbide (TiC) precipitates turned out to be promising in recent studies. Yet, it is currently unclear which of the two particle types is the best choice for high temperature applications nor has the strengthening mechanism associated with the monocarbide (MC)-precipitates been elucidated. To address these issues, we perform compression tests at ambient and elevated temperatures on the particle-free base material containing 15 at.% of rhenium (Re), 5 at.% of chromium (Cr) and cobalt (Co) as balance (Co-15Re-5Cr), as well as on TaC- and TiC-containing variants. Additionally, transmission electron microscopy is used to analyze the shape of the precipitates and their orientation relationship to the matrix. Based on these investigations, we show that TiC and TaC are equally suited for precipitation strengthening of Co-Re-based alloys and identify climb over the elongated particles as a rate controlling particle strengthening mechanism at elevated temperatures. Furthermore, we show that the Re-atoms are remarkably strong obstacles to dislocation motion, which are overcome by thermal activation at elevated temperatures.

In Situ Liquid Electrochemical TEM Investigation of LiMn1.5 Ni0.5 O4 Thin Film Cathode for Micro-Battery Applications.

Micro-batteries are attractive miniaturized energy devices for new Internet of Things applications, but the lack of understanding of their degradation process during cycling hinders improving their performance. Here focused ion beam (FIB)-lamella from LiMn1.5 Ni0.5 O4 (LMNO) thin-film cathode is in situ cycled in a liquid electrolyte inside an electrochemical transmission electron microscope (TEM) holder to analyze structural and morphology changes upon (de)lithiation processes. A high-quality electrical connection between the platinum (Pt) current collector of FIB-lamella and the microchip's Pt working electrode is established, as confirmed by local two-probe conductivity measurements. In situ cyclic voltammetry (CV) experiments show two redox activities at 4.41 and 4.58/4.54 V corresponding to the Ni2+/3+ and Ni3+/4+ couples, respectively. (S)TEM investigations of the cycled thin-film reveal formation of voids and cracks, loss of contact with current collector, and presence of organic decomposition products. The 4D STEM ASTAR technique highlights the emergence of an amorphization process and a decrease in average grain size from 20 to 10 nm in the in situ cycled electrode. The present findings, obtained for the first time through the liquid electrochemical TEM study, provide several insights explaining the capacity fade of the LMNO thin-film cathode typically observed upon cycling in a conventional liquid electrolyte.

Application improvement of A* algorithm in intelligent vehicle trajectory planning.

Trajectory planning is one of the key technologies for autonomous driving. A* algorithm is a classical trajectory planning algorithm that has good results in the field of robot path planning. However, there are still some practical problems to be solved when the algorithm is applied to vehicles, such as the algorithm fails to consider the vehicle contours, the planned path is not smooth, and it lacks speed planning. In order to solve these problems, this paper proposes a path processing method and a path tracking method for the A* algorithm. First, the method of configuring safe redundancy space is given considering the vehicle contour, then, the path is generated based on A* algorithm and smoothed using Bessel curve, and the speed is planned based on the curvature of the path. The trajectory tracking algorithm in this paper is based on an expert system and pure tracking theory. In terms of speed tracking, an expert system for the acceleration characteristics of the vehicle is constructed and used as a priori information for speed control, and good results are obtained. In terms of path tracking, the required steering wheel angle is calculated based on pure tracking theory, and the influence factor of speed on steering is obtained from test data, based on which the steering wheel angle is corrected and the accuracy of path tracking is improved. In addition, this paper proposes a target point selection method for the pure tracking algorithm to improve the stability of vehicle directional control. Finally, a simulation analysis of the proposed method is performed. The results show that the method can improve the applicability of the A* algorithm in automated vehicle planning.

Combining structural and chemical information at the nanometer scale by correlative transmission electron microscopy and atom probe tomography.

In many cases, the three-dimensional reconstructions from atom probe tomography (APT) are not sufficiently accurate to resolve crystallographic features such as lattice planes, shear bands, stacking faults, dislocations or grain boundaries. Hence, correlative crystallographic characterization is required in addition to APT at the exact same location of the specimen. Also, for the site-specific preparation of APT tips containing regions of interest (e.g. grain boundaries) correlative electron microscopy is often inevitable. Here we present a versatile experimental setup that enables performing correlative focused ion beam milling, transmission electron microscopy (TEM), and APT under optimized characterization conditions. The setup was designed for high throughput, robustness and practicability. We demonstrate that atom probe tips can be characterized by TEM in the same way as a standard TEM sample. In particular, the use of scanning nanobeam diffraction provides valuable complementary crystallographic information when being performed on atom probe tips. This technique enables the measurement of orientation and phase maps as known from electron backscattering diffraction with a spatial resolution down to one nanometer.