Raster Maps Research Articles

A new multi-objective point path planning algorithm for mobile robots

Aiming at the path planning problem of mobile robot passing through multiple target points with the shortest path under two-dimensional raster map, this paper proposes a method combining an improved A* algorithm with an annealing genetic algorithm. For the problem that A* algorithm will search adjacent nodes in turn, Jump Point Search (JPS) algorithm is introduced to avoid the search of surrounding nodes by searching the jump point of the current node and reduce the calculation time. In view of the prematurity problem of genetic algorithm, the simulated annealing algorithm is introduced to make the simulated annealing algorithm and genetic algorithm complement each other and overcome the prematurity problem of genetic algorithm effectively. By using TSPLIB dataset, the path planning of the improved genetic algorithm is obviously better than that of the original genetic algorithm. Finally, the feasibility and effectiveness of the method are verified by simulation, and the shortest safe path can be found quickly in the process of optimization.

Intelligent Warehouse Robot Path Planning Based on Improved Ant Colony Algorithm

To improve the safety and accuracy of path planning of intelligent warehouse robots, this paper establishes a storage shelf model, incorporates Poisson Distribution to simulate the influence of unknown factors, and establishes a three-color raster map. The pheromone update mechanism is optimized by considering the path safety, path length, and turning elements under the influence of unknown factors. The two models based on the three-dimensional shelves are simulated separately, and the planned paths are de-pointed and smoothed. The simulation results show that the improved algorithm can design the optimal route safely and effectively in the storage environment under the influence of unknown factors. The proposed algorithm not only solves the blind search and deadlock problems, but also has better performances than other algorithms, i.e., 4 iterations compared to 22 and 30 iterations, 3 turns compared to 9 and 7 turns, 8.468s running time compared to 16.974s and 13.754s.

A second-order dynamic and static ship path planning model based on reinforcement learning and heuristic search algorithms

Ship path planning plays an important role in the intelligent decision-making system which can provide important navigation information for ship and coordinate with other ships via wireless networks. However, existing methods still suffer from slow path planning and low security problems. In this paper, we propose a second-order ship path planning model, which consists of two main steps, i.e., first-order static global path planning and second-order dynamic local path planning. Specifically, we first create a raster map using ArcGIS. Second, the global path planning is performed on the raster map based on the Dyna-Sarsa(lambda) model, which integrates the eligibility trace and the Dyna framework on the Sarsa algorithm. Particularly, the eligibility trace has a short-term memory for the trajectory, which can improve the convergence speed of the model. Meanwhile, the Dyna framework obtains simulation experience through simulation training, which can further improve the convergence speed of the model. Then, the improved ship trajectory prediction model based on stacked bidirectional gated recurrent unit is used to identify the risk of ship collision and switch the path planning from the first order to the second order. Finally, the second-order dynamic local path planning is presented based on the FCC-A* algorithm, where the cost function of the traditional path planning A* algorithm is rewritten using the fuzzy collision cost membership function (fuzzy collision cost, FCC) to reduce the collision risk of ships. The proposed model is evaluated on the Baltic Sea geographic information and ship trajectory datasets. The experimental results show that the eligibility trace and the Dyna learning framework in the proposed model can effectively improve the planning efficiency of the ship’s global path planning, and the collision risk membership function can effectively reduce the number of collisions in A* local path planning and thus improve the navigation safety of encountering ships.

Versatile Confocal Raman Imaging Microscope Built from Off-the-Shelf Opto-Mechanical Components.

Confocal Raman microscopic (CRM) imaging has evolved to become a key tool for spatially resolved, compositional analysis and imaging, down to the μm-scale, and nowadays one may choose between numerous commercial instruments. That notwithstanding, situations may arise which exclude the use of a commercial instrument, e.g., if the analysis involves toxic or radioactive samples/environments; one may not wish to render an expensive instrument unusable for other uses, due to contamination. Therefore, custom-designed CRM instrumentation-being adaptable to hazardous conditions and providing operational flexibility-may be beneficial. Here, we describe a CRM setup, which is constructed nearly in its entirety from off-the-shelf optomechanical and optical components. The original aim was to develop a CRM suitable for the investigation of samples exposed to tritium. For increased flexibility, the CRM system incorporates optical fiber coupling to both the Raman excitation laser and the spectrometer. Lateral raster scans and axial profiling of samples are facilitated by the use of a motorized xyz-translation assembly. Besides the description of the construction and alignment of the CRM system, we also provide (i) the experimental evaluation of system performance (such as, e.g., spatial resolution) and (ii) examples of Raman raster maps and axial profiles of selected thin-film samples (such as, e.g., graphene sheets).

PENERAPAN FUZZY C MEANS PADA NILAI NDVI LANDSAT 8 UNTUK KLASTERISASI KEHIJAUAN KELURAHAN DI KOTA BOGOR

This study aims to cluster sub-districts (kelurahan) in Bogor Municipality based on greenness level. Normalized Difference Vegetation Index (NDVI) values were processed from Landsat 8 OLI recorded on 24 May 2020, downloaded from United States Geological Survey (USGS) website. NDVI values greater than 0,3 indicate that vegetation pixels are separated from overall raster maps. These NDVI values over 0,3 were extracted based on each sub-district poligon within Bogor Municipality. For sub-districts with NDVI 0,3, the percentage of the area and NDVI mean values were generated using Geographic Information System (GIS). In order to cluster 68 sub-districts in Bogor Municipality, two variables of NDVI, namely area percentage and mean NDVI values, were processed using the Fuzzy C Means (FCM) method. Greenness level clustering using the FCM method shows 14 sub-districts in high class, 28 in medium class, and 26 in low cluster class. Overlay analysis among clusters shows two sub-districts (7.69%) in the low cluster class inside the medium class, one sub-district (3.57%) in the medium class within the low class, and one sub-district (7.14%) in the high cluster class inside the medium class. There are two main indications for an overlapping sub-district located in multiple clusters, namely the sub-district that has little different values with neighbouring cluster centres, and the sub-district that has similar different values with two cluster centres.

Simulation Diagram of Unmanned Vehicle Driving from Current Position to Optimal Parking Space Based on A* Algorithm

To draw the simulation diagram of the unmanned vehicle driving from the current position to the optimal parking space, the parking space with the shortest distance from the starting point to the control point is defined as the optimal point in this paper. We simplify the driving route into a straight line and establish the coordinate axes. Depending on the driving direction, the driving trajectory can be divided into different cases. In each case, the order of the parking spaces passed by the vehicle is fixed. Therefore, if the parking space is occupied, its value is assigned as 0; otherwise, it is 1. The initial value is assigned as 0, and we accumulate the weighted value in each case. When the total weight reaches 1, accumulation stops, and the optimal parking space in this case is recorded. The optimal parking space is in which the vehicle travels the shortest distance among all the cases. Based on the A* algorithm, a raster map is constructed. The obstacle area is expanded by narrowing the feasible area range of control points in the parking lot so that the path of control points can meet the kinematic and obstacle avoidance constraints of vehicles. Finally, a simulation diagram of the parking trajectory is drawn.

Analysis of the spatial and temporal evolution of water and soil resource carrying capacity in arid region of northwest China

AbstractThe spatial and temporal evolution process of the water and soil resource carrying capacity in artificial oases in the arid zone of China was accurately revealed. This study constructs a water and soil resource analysis system based on system dynamics. It integrates cloud theory and hierarchical analysis to analyze the evolutional trend of the water and soil resource carrying capacity in irrigation areas. The spatial analysis technique was used to obtain a raster map of the spatial and temporal characteristics of the influencing factors, and analyze the evolutionary characteristics of the water and soil resources carrying capacity by integrating multi-source data. The results showed that: (1) the evaluation clouds of water and soil resources carrying capacity in 2002, 2010, and 2018 were (0.5034, 0.0236, 0.0071), (0.5586, 00218, 0.0062), and (0.5989, 0.0249, 0.0061), respectively. This showed that the carrying capacity of water and soil resources was transitioning from ‘critical bearing’ to ‘good bearing’, but the evolution rate was gradually decreasing; (2) from 2002 to 2018, the size of the ‘good bearing’ area increased by 8.38%, while the size of the ‘severe bearing’ area decreased by 6.4%; (3) the degree of dramatic evolution of the carrying capacity of water and soil resources is: continuous change > pre-change > post-change > continuous stable > repeated change; (4) the serious carrying capacity area shows a spatial pattern of decreasing in an arc from the town to the surrounding areas. The results showed that water and soil resource-carrying capacity of the irrigation area shows a healthy and continuous spatial and temporal evolution trend.

Usage PlanetScope Images and LiDAR Point Clouds for Characterizing the Forest Succession Process in Post-Agricultural Areas

The paper investigates using PlanetScope satellite images together with LiDAR data for automation of land use/cover (LULC) mapping and 3D vegetation characteristics in the aspect of mapping and monitoring of the secondary forest succession areas. The study was performed for the tested area in the Biskupice district (South of Poland), where a forest succession occurs on post-agricultural lands. The research area was parcels where the forest overgrowing process was identified. It was verified whether the image processing allows for reliable LULC classification as an identification forest succession area. The PlanetScope classification showed forested areas as 62.77 ha (44.91% of the analyzed area). The overall accuracy of the image classification was 96.40% The airborne laser scanning (ALS) point clouds were used for deriving detailed information about the forest succession process. The precise vegetation parameters i.e., height and canopy cover were determined and presented as raster maps, histograms, or profiles.

Engaging Students in Learning the Relations of Geographical Elements through GIS-Enabled Property Price Visualization

Understanding the relations of urban elements is important in secondary school geography. Based on the price data of 22,684 listed apartments, this article aims to enable students to understand how geographical elements are related to the distribution of apartment prices through spatial analysis using ArcGIS 10.8. The spatial analysis showed that apartment prices are related to their proximity to government offices, schools, hospitals and main traffic roads. The content analysis of field notes, interviews and discussions suggests that students favor geographic information system (GIS)-based teaching and feel geospatial technology is useful to visualize the relationship of apartment prices to other geographical elements. Moreover, GIS-based teaching not only improves students’ skill of generating datasets and raster maps, but also provides more room for students’ enquiries compared to conventional teaching.

Spatial Gap-Filling of GK2A Daily Sea Surface Temperature (SST) around the Korean Peninsula Using Meteorological Data and Regression Residual Kriging (RRK)

Satellite remote sensing can measure large ocean surface areas, but the infrared-based sea surface temperature (SST) might not be correctly calculated for the pixels under clouds, resulting in missing values in satellite images. Early studies for the gap-free raster maps of satellite SST were based on spatial interpolation using in situ measurements. In this paper, however, an alternative spatial gap-filling method using regression residual kriging (RRK) for the Geostationary Korea Multi-Purpose Satellite-2A (GK2A) daily SST was examined for the seas around the Korean Peninsula. Extreme outliers were first removed from the in situ measurements and the GK2A daily SST images using multi-step statistical procedures. For the pixels on the in situ measurements after the quality control, a multiple linear regression (MLR) model was built using the selected meteorological variables such as daily SST climatology value, specific humidity, and maximum wind speed. The irregular point residuals from the MLR model were transformed into a residual grid by optimized kriging for the residual compensation for the MLR estimation of the null pixels. The RRK residual compensation method improved accuracy considerably compared with the in situ measurements. The gap-filled 18,876 pixels showed the mean bias error (MBE) of −0.001 °C, the mean absolute error (MAE) of 0.315 °C, the root mean square error (RMSE) of 0.550 °C, and the correlation coefficient (CC) of 0.994. The case studies made sure that the gap-filled SST with RRK had very similar values to the in situ measurements to those of the MLR-only method. This was more apparent in the typhoon case: our RRK result was also stable under the influence of typhoons because it can cope with the abrupt changes in marine meteorology.

The origin of edge-enhanced second harmonic generation in monolayer MoS2 flakes

The sensitiveness of second-harmonic generation (SHG) signal to crystal lattice orientation and crystal symmetry makes it a powerful tool for structural characterization of low dimensional transition metal dichalcogenide (TMD) materials. In this paper, we report the strain-induced SHG enhancement at the edge of the monolayer MoS2 flakes. By performing raster mapping of SHG intensity, the images show an enhanced SHG intensity at the flake edge with cubic-power dependence. The AFM analysis of the MoS2 flake edge with enhanced SHG, indeed, detected a topographic height change as compared to the center region. Micro-Raman spectroscopy analysis shows a blue shift in A1g phonon mode at the flake edge. This edge-enhanced SHG may be attributed to the intrinsic strain effect induced during the chemical vapor deposition growth condition, which modulates the nonlinear susceptibility. The micro-Raman spectroscopy implies that the flake edge has a compressed and stiffened structure in an out-of-plane direction. This work brings out the awareness of the residual strain effect on 2D TMD device performance, and the adequate characterization of the strained structure by SHG intensity mapping.

Robot Trajectory Planning Based on the Energy Management Strategy

With the increasing demand for automated production technology in national defense, industry, agriculture, and other fields, the status and role of mobile robots are becoming more and more pivotal. The intelligent technology of robots is becoming more and more demanding in terms of reliability, stability, efficiency, and adaptability, and the autonomous navigation technology of mobile robots is facing new challenges. This paper introduces the basic principle of traditional A∗ algorithm, points out the problems of this algorithm such as cumbersome calculation, large turning angle, and unsmooth derived trajectory planning, and proposes an improved A∗ algorithm. The improved algorithm introduces a two-way alternating search mechanism to improve the efficiency of the search path, and the improvement of the heuristic function solves the problem that the two-way alternating search A∗ algorithm takes more time when there are obstacles perpendicular to the path on the way to the encounter. Simulation experiments in different raster environments prove that compared with the traditional A∗ algorithm, genetic algorithm, and simulated annealing algorithm, the improved A∗ algorithm can significantly improve the search path speed, while overcoming the disadvantages of many path turning angles and large turning angles and is an efficient and feasible algorithm for raster map environments.

Tourism Potentiality Zone Mapping by Using the AHP Technique: A Study on Bankura District, West Bengal, India

Tourism is a multidimensional sector having significant contributions to sustainable development, economic upliftment, and social benefits. Although the Bankura is full of tourism potential, but its’ tourism potentiality has not been properly used. This study is designed to identify the tourism potential zones of the Bankura district using a 5-steps integrated methodology, which includes the preparation of the thematic layers (Eight parameters and thirty-four indicators) (Step-1); creation of matrices for pair-wise comparisons (Step-2); classification of the raster maps (Step-3); use of the weighted sum method to get the composite variables (Step-4) and classification of tourism potential map using the natural breaks (Step-5). This study has selected eight parameters, which are the Tourist Intensity Measure (TIM), Climate (C), Physical (P), Social (S), Economy (E), Environmental Quality (EQ), Amenities (AN), and Diversity in Local Art and Crafts (LAC′). Approximately, 23.33% area is identified with a high tourism potentiality, 58.74% area is observed under the moderate tourism potentiality, and 17.92% area is recognized as a low tourism potentiality. Efficiency testing of the tourism potentiality map indicates high precision utilizing the considerably high R-square value (0.94). The study uses an exceptional methodological framework applicable in any region.

Mapping method of single LiDAR for indoor degraded environment

Aiming at the problem that the mobile robot equipped with only a single-line LiDAR sensor is highly prone to mapping errors in the indoor degraded environment with long corridors, this paper proposes a Cartographer-SLAM based method to overcome the failure of laser odometer in problematic scenarios and construct high-precision raster maps. The method consists of two main parts. First, corridor detector for environment sensing (CDES) endows the mobile platform with the ability to perceive degraded environment through real-time obstacle monitoring and robust linear feature extraction. Second, specific observations for pose optimization (SOPO), additional observations are introduced for pose correction by measuring the variation of wall spacing. Experiment results demonstrate that the proposed method can overcome the failure problem of laser odometer in the long featureless corridor, and the area error of map building is 0.63% in the corridor environment with a total length of 45.15 m.

Research on Underground Coal Mine Map Construction Method Based on LeGO-LOAM Improved Algorithm

The application of intelligent equipment and technologies such as robots and unmanned vehicles is an important part of the construction of intelligent mines, and has become China’s national coal energy development strategy and the consensus of the coal industry. Environment perception and instant positioning is one of the key technologies destined to realize unmanned and autonomous navigation in underground coal mines, and simultaneous location and mapping (SLAM) is an effective method of deploying this key technology. The underground space of a coal mine is long and narrow, the environment is complex and changeable, the structure is complex and irregular, and the lighting is poor. This is a typical unstructured environment, which poses a great challenge to SLAM. This paper summarizes the current research status of underground coal mine map construction based on visual SLAM and Lidar SLAM, and analyzes the defects of the LeGO-LOAM algorithm, such as loopback detection errors or omissions. We use SegMatch to improve the loopback detection module of LeGO-LOAM, use the iterative closest point (ICP) algorithm to optimize the global map, then propose an improved SLAM algorithm, namely LeGO-LOAM-SM, and describe its principle and implementation. The performance of the LeGO-LOAM-SM was also tested using the KITTI dataset 00 sequence and SLAM experimental data collected in two coal mine underground simulation scenarios, and the performance indexes such as the map construction effect, trajectory overlap and length deviation, absolute trajectory error (ATE), and relative pose error (RPE) were analyzed. The results show that the map constructed by LeGO-LOAM-SM is clearer, has a better loopback effect, the estimated trajectory is smoother and more accurate, and the translation and rotation accuracy is improved by approximately 5%. This can construct more accurate point cloud map and low drift position estimation, which verifies the effectiveness and accuracy of the improved algorithm. Finally, to satisfy the navigation requirements, the construction method of a two-dimensional occupancy grid map was studied, and the underground coal mine simulation environment test was carried out. The results show that the constructed raster map can effectively filter out outlier noise such as dynamic obstacles, has a mapping accuracy of 0.01 m, and the required storage space compared with the point cloud map is reduced by three orders of magnitude. The research results enrich the SLAM algorithm and implementation in unstructured environments such as underground coal mines, and help to solve the problems of environment perception, real-time positioning, and the navigation of coal mine robots and unmanned vehicles.

Raster Map Line Element Extraction Method Based on Improved U-Net Network

To address the problem of low accuracy in line element recognition of raster maps due to text and background interference, we propose a raster map line element recognition method based on an improved U-Net network model, combining the semantic segmentation algorithm of deep learning, the attention gates (AG) module, and the atrous spatial pyramid pooling (ASPP) module. In the proposed network model, the encoder extracts image features, the decoder restores the extracted features, the features of different scales are extracted in the dilated convolution module between the encoder and the decoder, and the attention mechanism module increases the weight of line elements. The comparison experiment was carried out through the constructed line element recognition dataset. The experimental results show that the improved U-Net network accuracy rate is 93.08%, the recall rate is 92.29%, the DSC accuracy is 93.03%, and the F1-score is 92.68%. In the network robustness test, under different signal-to-noise ratios (SNRs), comparing the improved network structure with the original network structure, the DSC improved by 13.18–17.05%. These results show that the network model proposed in this paper can effectively extract raster map line elements.

Semantic segmentation and path planning for orchards based on UAV images

A new semantic segmentation network, Space Atrous Depthwise Network (SADNet), was proposed to segment orchard unmanned aerial vehicle (UAV) images. In SADNet, a depthwise identity convolution block (DIC) was introduced to optimise the network feature extraction capabilities. An atrous spatial pyramid pooling module (ASPP) was introduced to fuse multi-scale semantic information. To improve the receptive field of the network, the concurrent spatial and channel squeeze and excitation module (scSE) were also introduced to focus the network on the pixels that need attention. The performance of SADNet on the orchard dataset was found to be better than that of DeepLabv3, PSPNet, and R2U-Net. The pixel accuracy and mean intersection over union reached 93.61 % and 88.28 %, respectively. MIoU of SADNet reached 86.2 % on the public dataset Pascal VOC 2012. Then, based on the results of orchard segmentation, we proposed a new method for constructing orchard raster maps. Based on this method, we planned the intra- and inter-row paths of the orchard. The planned paths have the smallest root mean square error of 3.19 m from the optimal path. The orchard segmentation and path planning method based on UAV images can plan the path of the whole orchard at the same time and can assist agricultural robots in pesticide spraying and fruit harvesting.

Multi-PPTP: Multiple Probabilistic Pedestrian Trajectory Prediction in the Complex Junction Scene

Pedestrian trajectory prediction in dynamic, multi-agent traffic junction scene is an important problem in the context of self-driving cars. Accurately predicting the trajectory of the agent, especially the pedestrian with high randomness, is of great significance to autonomous driving technology. In this paper, we propose Multi-PPTP, a novel trajectory prediction model that first utilizes a composite rasterized map to model the complex representations and interactions of road components, including dynamic obstacles (e.g., pedestrians, vehicles, and cyclists) and static road information (e.g., lanes, traffic lights, and sidewalks). Then it uses MapNet and AgentNet to extract spatio-temporal features by deep convolutional networks and LSTM to automatically derive relevant features, and next exploits Interaction-AttNet aggregate features to learn the high interactions among all components by affine transformation and multi-head attention mechanism. Additionally, we also propose a series of unique loss functions to predict multiple possible trajectories of each pedestrian while estimating their probabilities. Following extensive offline evaluation and comparison to the state-of-the-art models, our approach outperforms significantly the state-of-the-art models on our large scale in-house and public nuScenes dataset.

Exploring a multisource-data framework for assessing ecological environment conditions in the Yellow River Basin, China

Ecological environment conditions (EEC) assessment plays an important role in watershed management. However, due to insufficient field data, EEC assessment in large-scale watersheds faces challenges. Our study was conducted to develop an effective EEC assessment method framework that was capable of reducing the use of field data. Three indicators were developed from multisource data, including landscape ecological risk index (LERI), road network density (RND), and industry density (ID). The knowledge-based raster mapping approach integrated the three indicators into an overall score of the EEC. Then model validation was conducted with principal components of water quality from field sampling data by Pearson correlation analysis methods. Finally, we applied and demonstrated the constructed method framework in the EEC assessment of the YRB.The results showed that bad EEC (0.5326 < Overall score ≤ 0.7679) areas were mainly distributed in the northern part of the YRB, showing a circular distribution pattern. The areas with bad EEC were 15.84 million km2, accounting for 19.87 % of the YRB. The area of the highest LERI (0.157 < LERI≤0.246), the highest RND (4.4435 < RND ≤ 8.5574), and the highest ID (0.1403 < ID≤0.2597) finally converted to bad EEC was 7.22 million km2, 0.78 million km2, and 0.91 million km2, respectively. The results indicated that the ecological risk factors were the primary challenges for improving EEC, followed by industrial agglomeration and road network factors. The primary factors affecting EEC varied between the provinces in the YRB, suggesting that provinces take the management strategies and measures should be adaptive. The correlation coefficients between EEC and the principal components of water quality characteristics were between 0.022 and 0.241, P < 0.05. These findings validated that our method framework could distinguish the spatial variation of EEC in detail and further provide effective support for watershed management.

Industrial agglomeration and haze pollution: Evidence from China

The rapid industrialization has contributed to the miracle of economic growth in China, while also caused a series of environmental problems. As one of the most concerning urban disease in China, the aggravation of haze pollution is closely related to the accelerating urbanization and industrialization process in recent years, and the strong spatial diffusion makes the negative externality of haze pollution more harmful. This characteristic of haze pollution is closely related to the spatial structure of industrial sectors, especially the agglomeration of industrial sectors. This paper established a spatial economic framework to investigate the spillover effect of industrial agglomeration on haze pollution based on satellite raster map data of PM2.5 and location entropy index, and further discuss the question that whether industrial agglomeration can achieve a balanced development for both economic growth and environmental quality. The results indicated that there is an inverted U-shaped relationship between industrial agglomeration and haze pollution, and the indirect effect from spatial spillover dominated this impact. Industrial agglomeration has a single threshold effect on the knowledge spillover and the utilization of pollution disposal infrastructure of industrial sectors. Only when the agglomeration level reaches the threshold value, the scale effect can be realized to promote the air quality. The resource allocation efficiency and knowledge spillovers of industrial agglomeration need to be promoted in order to reach the inflection point and realize the win-win situation.