Path Length Research Articles

Route planning and obstacle avoidance using Q-Learning in MATLAB

Abstract. Recently, autonomous navigation has become important for many applications like self-driving cars, delivery drones, and robots. These domains are typically characterized by issues in planning an efficient route and object avoidance. Therefore, the paper describes work that utilizes Q-Learning as a means of reinforcement learning for autonomous navigation in terrain based on grid representations. Q-Learning allows an agent to acquire optimal signals in terms of strategy without the need for awareness of the surrounding environment. MATLAB was employed for the construction of the Q-Learning algorithm, as it was necessary to execute, evaluate, and simulate the algorithm in a controlled setting. In this study, the agent was tested in other environments of varying sizes (1010, 1515, and 2020) populated with randomly generated obstacles. Benchmark values such as average path length, number of collisions and convergence speed were observed as a measure of the agents performance. The analysis of the values obtained confirms that the agent is able to capture the shortest path from the starting point to the goal while avoiding the obstacles. Q-Learning is found to be flexible and effective in solving the presented navigation problems in this research. The future work would include the enhancement of the efficiency of the algorithm along with the applying of the system in dynamic and more complex scenarios, in order to solve practical problems.

Solving Paths Search Problems in Complex Graphs

The construction of models of various systems is associated with the enumeration of the values of the parameters of the elements of the structure and taking into account all the characteristics of operation and interaction of components to find a certain set of solutions that determine the configuration of the system. Such tasks belong to enumeration type tasks and imply that some of the next solutions from this set are obtained from the previous solution in a certain order. It is known that any problem of the enumeration type is solved only by methods of exhaustive search, and other methods for their enumeration do not exist yet. The paper presents a new method of searching paths in a graph – the method of node-graph transformation. The proposed method, unlike the existing ones, allows one to search all directed simple paths in an oriented graph of arbitrary structure much faster. In the known graph search methods (Breadth First Search and Depth First Search), the object of the search is a path. The total number of such paths in the graph determines the size of the search space. The main idea of the node-graph transformation method is to significantly reduce the size of the search space by enlarging the search objects. The enlargement of enumeration objects is performed by clustering paths into combinatorial objects that concentrate some set of paths of the same length according to certain rules. These combinatorial objects are called node-graphs. A node-graph refers to center-peripheral combinatorial objects, and specific node-graph transformation operations have been developed to enumerate all paths in the graph, which allow finding new paths based on previous paths. The method can be used as a basic toolkit to reduce the dimensionality of the search space for solutions to NP-complete problems while maintaining the universality and accuracy of the full search.

Multitask-Based Anti-Collision Trajectory Planning of Redundant Manipulators

During performing multiple tasks of a redundant manipulator, the obstacles affect the sequential order of task areas and the joint trajectories. The end-effector is constrained to visit multiple task areas with an optimal anti-collision path, while the joints are required to move smoothly and avoid predefined obstacles. A special encoding genetic algorithm (SEGA) is proposed for multitask-based anti-collision trajectory planning. Firstly, the spatial occupancy relationship between obstacles and manipulator is developed utilizing the theory of spherical enclosing box and spatial superposition. The obstacles are detected according to the relative position relationship between linear segments and spheres. Secondly, each joint trajectory between adjacent task areas is depicted with a sixth-degree polynomial. Additionally, each joint trajectory is improved via optimizing the unknown six-order coefficient. By searching for optimal sequential order of task areas, optimal collision detection results, and optimal joint trajectories, the multitask-based anti-collision trajectory planning problem is transformed into a parameter optimization problem. In SEGA, the cost function consists of two parts, including the end-effector path length and the variation of joint angles. Moreover, each chromosome consists of three categories of genes, including the sequential order of task areas, the sequential order of joint configurations corresponding to task areas, and the unknown coefficients for anti-collision joint trajectories. Finally, numerical simulations are carried out to verify the proposed method.

Research on the A* Algorithm for Automatic Guided Vehicles in Large-Scale Maps

The traditional A* algorithm faces the challenges of low search efficiency and large node extension range in the field of path planning. These directly restrict the overall performance of the algorithm. In this study, we aimed to improve the search efficiency and path planning quality of the A* algorithm in complex and large-scale environments through a series of optimisation measures, including the innovation of weight design, flexible adjustment of the search neighbourhood, improvement of the heuristic function, and optimisation of the node selection strategy. Specifically, this study innovatively introduces the local obstacle rate as the core index of weight design, and it dynamically adjusts the weights according to the change of the obstacle rate during the node movement process, which effectively reduces the search space and significantly improves the search speed. At the same time, according to the real-time change of the local obstacle rate, this study dynamically adjusts the range of the search neighbourhood, so that the algorithm can choose the optimal search strategy according to different environmental information. In terms of the improvement of the heuristic function, this study adopted the diagonal distance as the benchmark for cost estimation, and it innovatively introduces the angle coefficient to reflect the complexity of path turning, thus providing the algorithm with a more accurate guidance for the search direction. In addition, this study optimises the node selection method by drawing on the idea of simulated annealing, which eliminates the need to calculate and compare all possible surrogate values during the node selection process, thus significantly reducing the running time of the algorithm. The results of the simulation experiments fully verify the effectiveness and practicality of the improved algorithm. Compared with the traditional A* algorithm, the improved algorithm achieved significant optimisation in terms of the average running time, the number of expansion nodes, and the path length, with the average running time shortened by 84%, the number of expansion nodes reduced by 94%, and the path length also shortened by 2.3%.

Mid‐Infrared Sensing Using a Hollow–Core Fiber in a Nonlinear Interferometer

AbstractIncreasing the interaction path length is a well‐known method for enhancing the sensitivity of the optical detection system. Hollow–core fibers (HCFs) represent a viable alternative to the traditional multi‐path cells offering low optical losses and strong confinement of the optical field. Here, the incorporation of an Antiresonant Hollow–core Fiber (AR‐HCF) section into a nonlinear interferometer, where the AR‐HCF section serves as a gas‐sensing cell operating in the IR range is presented. By exploiting the effect of nonlinear interference, the detection is brought into the more operation‐friendly visible range. The detection of methane (CH4) gas at mid‐IR wavelengths within a half‐meter section of AR‐HCF, with an estimated concentration accuracy of 200 ppm·m is demonstrated. These results represent the combination of two research fields within a single instrument and pave the way for further advancement of quantum‐inspired gas sensing techniques.

AnACor2.0: a GPU-accelerated open-source software package for analytical absorption corrections in X-ray crystallography

Analytical absorption corrections are employed in scaling diffraction data for highly absorbing samples, such as those used in long-wavelength crystallography, where empirical corrections pose a challenge. AnACor2.0 is an accelerated software package developed to calculate analytical absorption corrections. It accomplishes this by ray-tracing the paths of diffracted X-rays through a voxelized 3D model of the sample. Due to the computationally intensive nature of ray-tracing, the calculation of analytical absorption corrections for a given sample can be time consuming. Three experimental datasets (insulin at λ = 3.10 Å, thermolysin at λ = 3.53 Å and thaumatin at λ = 4.13 Å) were processed to investigate the effectiveness of the accelerated methods in AnACor2.0. These methods demonstrated a maximum reduction in execution time of up to 175× compared with previous methods. As a result, the absorption factor calculation for the insulin dataset can now be completed in less than 10 s. These acceleration methods combine sampling, which evaluates subsets of crystal voxels, with modifications to standard ray-tracing. The bisection method is used to find path lengths, reducing the complexity from O(n) to O(log2 n). The gridding method involves calculating a regular grid of diffraction paths and using interpolation to find an absorption correction for a specific reflection. Additionally, optimized and specifically designed CUDA implementations for NVIDIA GPUs are utilized to enhance performance. Evaluation of these methods using simulated and real datasets demonstrates that systematic sampling of the 3D model provides consistently accurate results with minimal variance across different sampling ratios. The mean difference of absorption factors from the full calculation (without sampling) is at most 2%. Additionally, the anomalous peak heights of sulfur atoms in the Fourier map show a mean difference of only 1% compared with the full calculation. This research refines and accelerates the process of analytical absorption corrections, introducing innovative sampling and computational techniques that significantly enhance efficiency while maintaining accurate results.

A high-effective swarm intelligence-based multi-robot cooperation method for target searching in unknown hazardous environments

To solve target searching problems for multi-robot cooperation with inaccurate target distance perception in unknown hazardous environments, a hybrid adaptive robotic particle swarm optimizer (RPSO) and grey wolf optimizer (GWO) based algorithm with continuous virtual target guidance is proposed for high effective path planning in the searching. In the initial searching stages, both the wolf behavior-generated position and the gbest position and the pbesti positions from RPSO are employed to guide the motions of robots. With the information provided by these initial robot movement paths, a geometric model is established to generate potential targets. The K-means cluster algorithm is introduced to estimate a virtual target position online from potential targets, with new robot-presenting route information to update the history path information. Then the virtual position is employed as one of the direction components to help the robots approach the actual target. In addition, to avoid mobile robots falling into local convergence, a heuristic moving direction determination scheme is utilized to make robots circumvent obstacles in swarm motions, as well as a mutual repulsion algorithm to keep them in a scattering state. Simulation experiments on different types of unknown environments with varied robot numbers and adaptability testing for a dynamic target are carried out to verify the feasibility of the proposed target searching method with comparisons to the other three famous target searching algorithms. It is verified from the results that the presented method can not only contribute a 100% success rate in all runs of searching for a stochastic dynamic target under a limited maximal velocity, but also produce both the shortest path length and minimum iterations in terms of statistical metrics over the comparative methods.

A network evaluation of human and animal movement data across multiple swine farm systems in North America

INTRODUCTIONThe U.S. swine industry is vulnerable to the rapid spread of disease due to systemic structural issues. While animal movement networks are used to identify disease spread risks and design response plans, human movement between farms were rarely accounted for. Human movements, when integrated with animal movement models, create a different, more inclusive, and accurate network structure when compared to animal movements alone. METHODSOne year of propriety farm visit data was analyzed and consisted of anonymized property IDs, location, and user/truck IDs, along with visit dates, property, vehicle, and entry types from three swine management companies. A static directed network was created using the igraph package in R for all movements, with separate sub-networks for each entry type (animal, human, and subsets of vehicle types). Network statistics for each sub-network were compared. RESULTSThe full network included 455 properties, 11 property types, 9 vehicle types, 12 entry types, and 320001 edges (trips between properties). The longest path length was 10 in the animal movement network but decreased to 5 for the full and human movement network, while the average path length decreased from 3.2 to 2.2. Edge density increased from 0.03 to 0.09 for the human network and 0.1 for the full network. For all network properties examined, the full and human movement networks demonstrated higher connectivity than the animal network. A heavy right skew in the degree distributions indicates a 'hub' structure (scale-free-like network) and the shorter path lengths indicates a small-world network topology. DISCUSSIONThe full network is very well connected, more so than expected based on animal movement alone. Hubs may indicate points of disease susceptibility and 'super-spreader' properties. The high connectivity shows that swine farm networks may be more susceptible to spready of an introduced disease than expected from previous analyses. CONCLUSIONSMonitoring human, as well as animal movement, provides for a more complete and accurate understanding of swine farm biosecurity risks.

К РАСЧЕТУ ЖИДКОСТНЫХ ТАРЕЛЬЧАТЫХ СЕПАРАТОРОВ С ПАРАБОЛИЧЕСКИМИ ВСТАВКАМИ

The object of the study is liquid disk separators with curvilinear inserts, which are described by equations of the type . The study was conducted to study the effect of the insert shape on the intensity of the centrifugal force on the liquid flow, as well as on the length of the sedimentation path (or float) of dispersed medium particles. The efficiency of clarification of dispersed media is predetermined by two design characteristics of the device - the length of the particle sedimentation path and the direction of the centrifugal force relative to the walls of the curved channel. For parabolic inserts, the distance between them and the angle of inclination of the walls are variable quantities. To calculate these quantities, nonlinear equations are constructed and an algorithm for numerical calculations is proposed. The calculation results are presented as graphical dependencies of the above parameters along the channel length. The calculations were performed for paraboloids of revolution with degrees m=2, 3 and 4 at coefficient a=2, 3 and 4. As the coefficient a increases, the distance between the inserts decreases. The influence of the exponent m on the gap between the paraboloids of revolution is more complex. The gap size along the channel length decreased from 3 cm to several millimeters. The angle of inclination of the walls relative to the rotation axis decreased in the range from 70 to 10 degrees. As the parameters a and m increase, this angle decreases, therefore, the component of the centrifugal force directed perpendicular to the channel wall increases. The equations of motion of the liquid system were solved by the method of equal flow surfaces, according to which the flow is represented as several layers with their own velocities. At the inlet section, the velocity diagram develops from a flat profile to a parabolic form. As a result of the action of the centrifugal force, an asymmetry of the velocity profile is observed. The obtained results can be used to justify the geometric characteristics of a plate separator with parabolic inserts and to coordinate them with the flow regime parameters.

Structural brain connectivity does not associate with childhood trauma in individuals with schizophrenia

BackgroundSchizophrenia is a brain dysconnectivity disorder. However, it is not well understood whether the experience of childhood trauma (CT) affects dysconnectivity in individuals with schizophrenia (SZ). Using a network-based approach, we examined whether self-reported CT would explain additional variance compared to whole-brain topology and structural connectivity changes in SZ versus healthy controls (HC). Material and methodsCT was assessed in 51 SZ (mean age ± standard deviation 44 ± 11 years) and 140 HC (34.0 ± 12 years). Structural brain networks were constructed from T1-weighted MR and diffusion-MRI scans using non-tensor based tractography. Group differences in whole-brain topology and permutation-based statistics were examined and corrected for age and sex. ResultsSZ showed reductions in efficiency, strength, clustering and density (p <0.01) as well as increases in path length (F(range) = 4.71-18.1, p <0.03) when compared to HC. We also observed hypoconnectivity in a subnetwork of frontotemporal, frontoparietal and occipital regions in SZ relative to HC (T > 4.0, p <0.001). However, we did not find that high CT levels were related to structural network differences or structural connectivity changes in SZ. ConclusionsCT did not impact on topology or subnetwork connectivity changes in SZ. High CT levels were also not associated with any differences in network organisation irrespective of diagnosis. However, our findings confirm that SZ showed both network-level reductions and increases in a subnetwork. These findings suggest that the patterns of neuroanatomical dysconnectivity in established schizophrenia may not be influenced by CT. Future studies are needed to investigate the association between CT and structural dysconnectivity in schizophrenia.

Speed of Sound Measurements for Liquid Squalane Up to a Pressure of 20 MPa

The speed of sound of liquid squalane is measured with the double-path pulse-echo technique, utilizing a piezoelectric quartz (8 MHz) positioned between two reflectors with distinct path lengths. Calibration of the apparatus is carried out with water, for which highly accurate reference data exist. The present experiments with squalane cover the temperature range from 298.15 K to 493.15 K and a pressure from 0.1 MPa to 20 MPa. The total relative expanded (k=2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k = 2$$\\end{document}) uncertainty Ur(w)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$U_r(w)$$\\end{document} for the speed of sound is estimated to be ±0.1 %. The present data are consistent with literature values, which are, however, only available at ambient pressure. To validate the present speed of sound measurements, the density and isobaric heat capacity are integrated numerically from the sampled data over most of the measurement range, employing rigorous thermodynamic identities. With deviations of about 0.1 %, the resulting density data are favorably compared with literature values. The resulting isobaric heat capacity, for which just a single reference with data at pressures above the ambient exists, deviates by up to 6 %.

A complex network analysis of groundwater wells in and around the Doñana Natural Space, Spain

This paper proposes the use of Complex Network Analysis tools to study the spatial distribution and temporal evolution of groundwater abstraction wells within a region of interest. To that end a weighted undirected network connecting wells within a certain distance was built. The edge weights take into account the distance between the wells as well as their corresponding abstraction volumes. In addition to the conventional complex network metrics, two specific abstraction-based centrality indexes are proposed, based on the total impact of a groundwater abstraction well on its surrounding wells and on the total impact of surrounding wells on a specific location. The proposed approach has been applied to the area of the Doñana Natural Space, one of the most important wetlands in Europe. The topology of the network reveals a large number of small, disconnected components and a few large components. This is reflected also in the degree distribution, which follows a Power Law distribution. The network has a large average clustering coefficient, a small average path length and a low level of centralisation. Global and average local network efficiency are also high. The network is assortative in terms of degree-degree and strength-strength correlations. Eigenvector centrality of the nodes reveals the location of influential wells.

Age-Related Diversification and Specialization in the Mental Lexicon: Comparing Aggregate and Individual-Level Network Approaches.

The mental lexicon changes across the lifespan. Prior work, aggregating data among individuals of similar ages, found that the aging lexicon, represented as a network of free associations, becomes more sparse with age: degree and clustering coefficient decrease and average shortest path length increases. However, because this work is based on aggregated data, it remains to be seen whether or not individuals show a similar pattern of age-related lexical change. Here, we demonstrate how an individual-level approach can be used to reveal differences that vary systematically with age. We also directly compare this approach with an aggregate-level approach, to show how these approaches differ. Our individual-level approach follows the logic of many past approaches by comparing individual data as they are situated within population-level data. To do this, we produce a conglomerate network from population-level data and then identify how data from individuals of different ages are situated within that network. Though we find most qualitative patterns are preserved, individuals produce associates that have a higher clustering coefficient in the conglomerate network as they age. Alongside a reduction in degree, this suggests more specialized but clustered knowledge with age. Older individuals also reveal a pattern of increasing distance among the associates they produce in response to a single cue, indicating a more diverse range of associations. We demonstrate these results for three different languages: English, Spanish, and Dutch, which all show the same qualitative patterns of differences between aggregate and individual network approaches. These results reveal how individual-level approaches can be taken with aggregate data and demonstrate new insights into understanding the aginglexicon.

Ultrafast Water Transport of Reverse Osmosis Membrane Based on Quasi-Vertically Oriented 2D Interlayer.

Interlayered thin-film composite (i-TFC) membranes based on 2D materials have been widely studied due to their high efficiency in mass transfer. However, the randomly stacked 2D nanosheets usually increase the fluid path length to some extent. Herein, in situ-grown quasi-vertically oriented 2D ZIF-L was introduced as an interlayer for preparing high-performance reverse osmosis (RO) membranes. Through the optimization of the crystal growth based on the inert polyethylene substrate, the novel i-TFC RO membrane via interfacial polymerization shows an outstanding water permeance (5.50 L m-2 h-1 bar-1) and good NaCl rejection (96.3%). The membrane also shows promising potential in domestic water purification and organic solvent separation applications. Compared with the randomly stacked ZIF-L interlayer, the advantages of the vertically oriented one were ascribed to the excellent storage capacity of the amine monomers and the intensified gutter effect. This work will encourage more exploration on the interlayer architectures for high-performance i-TFC membranes.

Fatigue-induced alterations in force production, trajectory and performance in alpine skiing

ABSTRACT In giant slalom, the ability to apply a high amount of force in the radial direction is essential for performance. A race is characterized by repeated turns performed at high velocity, potentially inducing fatigue. Therefore, this study aimed to assess the effect of fatigue on performance, trajectory characteristics, and force production capacities onto the snow. Twelve skiers ran a 4-turn section with (FATIGUE) and without pre-induced fatigue (CONTROL). Knee extensor maximal voluntary contraction (MVC) was performed before the experiment and after both conditions. Section time, energy dissipation, path length, total force output, force application effectiveness, and EMG activity of the main lower-limb muscles were compared between conditions. Multiple linear regressions were used to understand whether interindividual variability in the kinematic, kinetic and EMG between conditions explains variability in performance changes with fatigue. MVC was lower after FATIGUE (−19.1 ± 6.4%, p < 0.001) but did not change after CONTROL. FATIGUE was associated with longer section times (+0.21 ± 0.11 s, p < 0.001), energy dissipation (−0.78 ± 1.05 J.s.kg.m−1, p = 0.026), path length (+1.1 ± 1.6 m, p = 0.033) and lower force application effectiveness (−0.1 ± 0.1, p = 0.017). This study experimentally demonstrates that fatigue in giant slalom results in lower force application effectiveness, inducing over-dissipation of mechanical energy and longer path length, leading to lower performance.

Automatic Segmentation Model and Parameter Extraction Algorithm for Lightning Whistlers

AbstractBased on the magnetic field data recorded by the ZH‐1 electromagnetic satellite, we cerat a training set of 1,300 spectrograms containing the dispersion spectrum of lightning whistlers (LW). The Segment Anything Model (SAM) in the field of image segmentation is trained through the training set to obtain a fine‐tuned SAM model that can be used to detect and segment the dispersion spectrum of LW at pixel level. All track regions of LW are effectively separated from other non‐lightning whistlers regions in the spectrograms after being segmented by the model. The segmentation effect is excellent and detection accuracy is 96.89%, which is better than the previous segmentation model for LW based on ground station data. Then we apply the traditional image processing methods to extract the dispersion spectrum of LW one by one, and develop an algorithm to automatically extract the physical parameters of each LW. The root mean square error between the automatically extracted dispersion parameter and the manually extracted dispersion parameter is only 0.1654 s1/2. The model and algorithm studied in this paper are employed to analyze the dispersion of LW received by the ZH‐1 satellite over China. It is found that the whistlers dispersion received by satellites during summer in the northern hemisphere and summer in the southern hemisphere shows opposite trends with receiving latitude. Both trends can be explained by the relationship between the dispersion and the length of propagation paths of LW.

Gain-based Green Ant Colony Optimization for 3D Path Planning on Remote Sensing Images

Metaheuristic algorithms are powerful methods for handling complexities in 3D environments because of their adaptability property. This paper proposes a gain-based, green-ant colony optimization (GGACO) method for 3D path planning on remote sensing images. Shortest paths do not always imply minimum energy consumption. Moreover, computational complexity tends to increase in the case of higher-dimensional data. A novel method is proposed to alleviate this issue, one that provides an efficient path with minimum energy consumption by adding a gain quantity during its search. The results are validated using performance measures, viz., path length, time, and energy cost. Real-time images, along with their corresponding ground truth and “digital surface models (DSM)”, have been sourced from the “International Society for Photogrammetry and Remote Sensing (ISPRS)”. Comparisons have been made against state-of-the-art algorithms and analyzed. Finally, the convergence and stability of the proposed method have been verified; it has been found that the proposed method outperforms the existing method by 6%, 11% and 5% regarding length, computation time, and energy, respectively.

Realization of Integrated Regional Ecological Management Based on Ecosystem Service Supply and Demand Flow Networks: An Example from a Dominant Mineral Resources Development Area

Understanding the flow processes and pattern optimization of ecosystem services (ESs) supply and demand is crucial for integrated regional ecological management. However, the understanding of the flow process of ESs at the 1 km grid scale is still limited, especially in areas dominated by mineral resource development. The landscape in these areas has undergone significant changes due to mining activities. It is urgent to construct a regional management model that integrates the flow of ecosystem services and mine restoration. This study developed a framework that links ecosystem service flows (ESFs) and ecological security patterns (ESP) based on multi-source ecological monitoring data, constructed an ES supply-demand flow network through the flow properties, and determined the sequence and optimization strategies for mine rehabilitation to achieve integrated regional management. The results show that, except for food production (FP), other services were in surplus overall, mostly in synergistic relationships, but the spatial distribution of their supply and demand was not coordinated. Surplus areas were located mainly in the eastern woodlands, and deficit areas were located in the northwestern production agglomeration centers, suggesting that areas of supply-demand imbalance can be mitigated through ecological integration. Among these, water yield (WY) had a small number of sources and sinks and is limited in area range. Habitat quality (HQ) sources and sinks had the largest area coverage and the highest number. The distribution of ESF corridors, influenced by factors such as the number of sources and sinks, flow characteristics, and spatial resistance, varied significantly. HQ exhibited a more uniform distribution range, while WY had a longer average length of flow path. Overlaying ecological and mining factors, we identified ecological strategic spots, important supply areas, beneficiary areas, and mine priority restoration areas to further optimize the overall layout and rationally allocate the intrinsic structure of the patches based on ES supply and demand.

Simultaneous detection of soot, temperature, and C2H2 in laminar premixed flames using a multi-pass diode laser absorption spectrometer

We report the development of a multi-pass diode laser absorption spectroscopy system for simultaneous measurements of soot volume fraction (SVF), temperature, and C2H2 concentration using a single diode laser near 1.543 µm. A line-shaped beam spot pattern is chosen for the open-path Herriott multi-pass cavity, enabling sensitive detection at various heights above the burner with an effective optical absorption path length of approximately 1.2 m in a 6 cm diameter flame region. The gas parameters (temperature and C2H2 concentration) and the SVF are determined from the absorption spectra of the target C2H2 line pair and the laser extinction of the soot, which can be extracted from the detected signal, respectively. The performance of the system was confirmed in laminar premixed ethylene and air (C2H4/air) sooting flames produced by a standard bronze plug McKenna burner at four representative equivalence ratios. All the measurement results were compared with the two-dimensional (2D) computational fluid dynamics (CFD) simulations using a skeletal mechanism with the Moss−Brookes model. The good quantitative and qualitative agreement between the TDLAS measurements and 2D CFD simulations confirms the powerful capability of the developed system.

Path Planning for Mobile Robots Based on the Improved DAPF-QRRT* Strategy

The rapidly exploring random tree star (RRT*) algorithm is widely used to solve path planning problems. However, the RRT* algorithm and its variants fall short of achieving a balanced consideration of path quality and safety. To address this issue, an improved discretized artificial potential field-QRRT* (IDAPF-QRRT*) path planning strategy is introduced. Initially, the APF method is integrated into the Quick-RRT* (Q-RRT*) algorithm, utilizing the attraction of goal points and the repulsion of obstacles to optimize the tree expansion process, swiftly achieving superior initial solutions. Subsequently, a triangle inequality-based path reconnection mechanism is introduced to create and reconnect path points, optimize the path length, and accelerate the generation of sub-optimal paths. Finally, by refining the traditional APF method, a repulsive orthogonal vector field is obtained, achieving the orthogonalization between repulsive and attractive vector fields. This places key path points within the optimized vector field and adjusts their positions, thereby enhancing path safety. Compared to the Q-RRT* algorithm, the DPF-QRRT* algorithm achieves a 37.66% reduction in the time taken to achieve 1.05 times the optimal solution, and the IDAPF-QRRT* strategy nearly doubles generated path safety.