Grid Map Research Articles

A Novel Robotic Path Planning Method in Grid Map Context Based on D* Lite Algorithm and Deep Learning

Mobile robot path planning has received more and more attention as an important technology in robotics. Based on the D* Lite algorithm, this paper constructs a robot path planning model in a grid map environment, and proposes a deep learning fusion algorithm for path planning under complex large maps. The D* Lite algorithm with excellent performance adopts the enhanced neural network algorithm with environment self-learning ability for local parts. The model introduces the gentle update method of the Q value in the D* Lite algorithm into the optimization target calculation, calculates the loss function and updates the network parameters, thereby solving the overestimation problem of deep reinforcement learning in the application of mobile robot path planning. In the simulation process, the idea of averaging is introduced into the e-greedy strategy, and the value function output by the previous generation parameter network is used to obtain the average result to determine the next action direction of the mobile robot. The experimental results show that in the simple environment and the complex environment, the planned path lengths in the environment are 44.21, 43.63 and 43.61[Formula: see text]m, respectively, reducing the number of collisions with obstacles during the training process of the mobile robot, and improving the superiority and effectiveness of the algorithm.

Integrative data system for monitoring biota and natural habitats in the Israeli Eastern Mediterranean marine environment.

An integrative data system for monitoring the biota of the Mediterranean waters of Israel as well as selected records from adjacent Levantine basin regions is presented here, aimed at providing data and research tools for long-term bio-geographic and ecological studies and more important, providing background data for assisting governmental regulators to establish educated habitat-oriented environmental policy. The system relies on the geographic information system (GIS) online map-based platform and contains at present the following components: biotic database of ~ 170,000 recorded sampling events; uniform habitat maps of 63 benthic habitats and 2 pelagic ones, constructed using relevant bathymetric features and biotic community compositions; bathymetric hill-shade map; depth contours; raster depth grid and human interference map. Other informative auxiliary maps are planned to be added (e.g., map of potential pockmark sites, detailed maps of tiny carbonate crust nolls and more). A number of 883 cited documents were listed by us for potential extraction of sampling efforts, most of them areavailable to us as PDFs andare available also to the users, excluding copyright-protected ones. Forty-three major projects were depicted in addition to a variety of small studies (e.g., university theses). Thirty-five sampling devices were documented and described, and 3187 species-level identifications were already recorded. In addition, the system provides access to description of sampling devices and pictures of species and seascapes. New data is continuously deposited to the system and the system is flexible, allowing future addition of new types of information. The system site is accessible through the link: https://experience.arcgis.com/experience/40e86605ff4d4e5096ed2c901fec2a2f .

Optimization of an Autonomous Mobile Robot Path Planning Based on Improved Genetic Algorithms

Mobile robots are intended to operate in a variety of environments, and they need to be able to navigate and travel around obstacles, such as objects and barriers. In order to guarantee that the robot will not come into contact with any obstacles or other objects during its movement, algorithms for path planning have been demonstrated. The basic goal while constructing a route is to find the fastest and smoothest route between the starting point and the destination. This article describes route planning using the improvised genetic algorithm with the Bezier Curve (GA-BZ). This study carried out two main experiments, each using a 20x20 random grid map model with varying percentages of obstacles (5%, 15%, and 30% in the first experiment, and 25% and 50% in the second). In the initial experiments, the population (PN), generation (GN), and mutation rate (MR) of genetic algorithms (GA) will be altered to the following values: (PN = 100, 125, 150, or 200; GN = 100, 125, 150; and MR = 0.1, 0.3, 0.5, 0.7) respectively. The goal is to evaluate the effectiveness of AMR in terms of travel distance (m), total time (s), and total cost (RM) in comparison to traditional GA and GA-BZ. The second experiment examined robot performance utilising GA, GA-BZ, Simulated Annealing (SA), A-Star (A*), and Dijkstra's Algorithms (DA) for path distance (m), time travel (s), and fare trip (RM). The simulation results are analysed, compared, and explained. In conclusion, the project is summarised.

Geospatial mapping of distribution grid with machine learning and publicly-accessible multi-modal data

Detailed and location-aware distribution grid information is a prerequisite for various power system applications such as renewable energy integration, wildfire risk assessment, and infrastructure planning. However, a generalizable and scalable approach to obtain such information is still lacking. In this work, we develop a machine-learning-based framework to map both overhead and underground distribution grids using widely-available multi-modal data including street view images, road networks, and building maps. Benchmarked against the utility-owned distribution grid map in California, our framework achieves > 80% precision and recall on average in the geospatial mapping of grids. The framework developed with the California data can be transferred to Sub-Saharan Africa and maintain the same level of precision without fine-tuning, demonstrating its generalizability. Furthermore, our framework achieves a R2 of 0.63 in measuring the fraction of underground power lines at the aggregate level for estimating grid exposure to wildfires. We offer the framework as an open tool for mapping and analyzing distribution grids solely based on publicly-accessible data to support the construction and maintenance of reliable and clean energy systems around the world.

Application of Improved Butterfly Optimization Algorithm in Mobile Robot Path Planning

An improved butterfly optimization algorithm (IBOA) is proposed to overcome the disadvantages, including slow convergence, generation of local optimum solutions, and deadlock phenomenon, of the optimization algorithm in the path planning of mobile robots. A path-planning grid model is established based on an improved obstacle model. First, the population diversity is improved by introducing kent mapping during population position renewal in the normal butterfly optimization algorithm (BOA) to enhance the global search ability of the butterfly population. Second, an adaptive weight coefficient is introduced in the renewal process of each generation to increase the convergence speed and accuracy. An opposition-based learning strategy based on convex lens imaging is introduced to help the butterfly population jump out of the local optimum. Finally, a mutation strategy is introduced to solve the path planning problem. On this basis, two path simplification strategies are proposed to make up for the shortcomings of planning paths in grid maps. The shortest path lengths solved by IBOA, BOA, and GA in the 20 × 20 map are 30.97, 31.799, and 31.799, respectively. The numbers of iterations for the shortest paths searched by IBOA, BOA, and GA are 14, 24, and 38 in that order. The shortest path lengths solved by IBOA, BOA and GA in the 40 × 40 map are 63.84, 65.60, and 65.84, respectively. The number of iterations for the shortest paths searched by IBOA, BOA and GA are 32, 40, and 46, respectively. Simulation results show that IBOA has a strong ability to solve robot path planning problems and that the proposed path simplification strategy can effectively reduce the length of the optimal path in the grid map to solve the path planning problem of mobile robots. The shortest paths solved by IBOA in 20 × 20 and 40 × 40 maps are simplified to lengths of 30.2914 and 61.03, respectively.

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.

Research on Public Air Route Network Planning of Urban Low-Altitude Logistics Unmanned Aerial Vehicles

As urban populations continue to grow and road traffic congestion worsens, traditional ground logistics has become less efficient. This has led to longer logistics times and increased costs. Therefore, unmanned aerial vehicle (UAV) logistics has become increasingly popular. However, free-planned routes cannot meet the safety and efficiency requirements of urban airspace mobility. To address this issue, a public air route network for low-altitude logistics UAVs needs to be established in urban areas. This paper proposes a public route network planning method based on the obstacle-based Voronoi diagram and A* algorithm, as follows: Firstly, construct a city airspace grid model in which the characteristics of the airspace are mapped onto the grid map. Introduce an obstacle clustering algorithm based on DBSCAN to generate representative obstacle points as the Voronoi seed nodes. Utilize the Voronoi diagram to establish the initial route network. Then, conduct an improved path planning by employing the A* algorithm for obstacle avoidance in route edges that pass through obstacles. To ensure the safe operation of drones, set constraints on the route safety interval. This process will generate a low-altitude public air route network for urban areas. After considering the flight costs of logistics UAVs at different altitudes, the height for the route network layout is determined. Finally, the route network evaluation indicators are established. The simulation results demonstrate that compared with the city road network planning method and the central radial network planning method, the total route length is shortened by 7.1% and 9%, respectively, the airspace coverage is increased by 9.8% and 35%, respectively, the average network degree is reduced by 52.6% and 212%, respectively, and the average flight time is reduced by 19.4s and 3.7s, respectively. In addition, by solving the network model using the Dijkstra algorithm, when the energy cost and risk cost weights are 0.6 and 0.4, respectively, and the safety interval is taken as 15 m, the total path cost value of the planned trajectory is minimized.

Deca-BDE emissions, validation, and environmental fate in China

Decabromodiphenyl ether (Deca-BDE) was officially listed in Annex A of the Stockholm Convention for persistent organic pollutants (POPs). It is necessary to establish its emission inventory to help reduce Deca-BDE contamination in the environment. We established a comprehensive Deca-BDE emission inventory in China. The results reveal that, from 2015 to 2017, the Deca-BDE emissions in its production source (source I) were less altered but increased annually in flame retarded plastics processing (source II), Deca-BDE-containing products usage (source III), and electronic waste (e-waste) treatment (source IV). We show that Deca-BDE emissions declined significantly in sources I and II but grew in source III and source IV from 2017 to 2018. We set up the provincial emission inventory to a gridded map on a spatial resolution of 0.25°× 0.25° latitude/longitude. The gridded inventory was incorporated into ChnMETOP model to simulate Deca-BDE concentrations in air and soil, and the modeled concentrations were compared to field-sampling data. The results show that the Deca-BDE emission inventory developed in this study agreed well with observed data, demonstrating that the Deca-BDE inventory in China developed in the present study is reliable. The inventory provides a support for quantifying human exposure risk to Deca-BDE and developing effective mitigation measures to mitigate Deca-BDE emissions.

Trajectory planning approach for autonomous electric bus in dynamic environment

Path planning is a challenging task to achieve vehicle autonomy, and it becomes even more difficult in handling dynamic situations and big vehicle sizes. Its performance is affected by the accuracy of mapping and vehicle localization. Many path planning algorithms have been developed to address static environments, however, these do not accurately reflect real-world scenarios which are dynamic in nature. This paper introduces a new planning architecture consisting of a global planner that plans a path in an occupancy grid map generated using LIDAR and odometry data. The local planner uses this global plan and plans a local plan on a section of the map that takes into consideration dynamic obstacles. Both the local and global plans are used to calculate the optimum velocity profile, providing a feasible and comfortable trajectory. The trajectory is then fed to a predictive Stanley controller, and both the calculated steering angle and the optimum velocity are achieved by low-level controllers. The performance of the proposed architecture was tested on a golf bus in a constrained environment and compared to different manual driving attitudes using a key performance indicator. The results of the proposed architecture show that it has a better KPI, with an average of 37% better than manual driving.

Roboat III: An autonomous surface vessel for urban transportation

AbstractIn this paper, we present our novel autonomous surface vessel platform, a full‐scale Roboat for urban transportation. This 4‐m‐long Roboat is designed with six seats and can carry a payload of up to 1000 kg. Roboat has two main thrusters for cruising and two tunnel thrusters to accommodate docking and interconnectivity between Roboats. We build an adaptive nonlinear model predictive controller for trajectory tracking to account for payload changes while transporting passengers. We use a sparse directed graph to represent the canal topological map and then find the most time‐efficient global path in a city‐scale environment using the algorithm. We then employ a multiobjective algorithm's lexicographic search to generate an obstacle‐free path using a point‐cloud projected two‐dimensional occupancy grid map. We also develop a docking mechanism to allow Roboat to “grasp” the docking station. Extensive experiments in Amsterdam waterways demonstrate that Roboat can (1) successfully track the optimal trajectories generated by the planner with varying numbers of passengers on board; (2) autonomously dock to the station without human intervention; (3) execute an autonomous water taxi task where it docks to pick up passengers, drive passengers to the destination while planning its path to avoid obstacles, and finally dock to drop off passengers.

Multitarget Real-Time Path Planning Using Double Adaptive A Algorithm

Multi-target real-time path planning (MTRT) is an extension of path planning that aims to traverse multiple targets in an unknown environment at the lowest cost. The MTRT is gov-erned by the “multi-target” demand leading to a rather extensive search space and the “real-time” requirement that imposes fast planning. However, the traditional path planning algorithm A* is notoriously inefficient in large spaces. Therefore, spurred by the low efficiency of A* for MTRT, this paper proposes the double adaptive A algorithm (DAA), which implements an adaptive multi-target heuristic function and an adaptive node expansion method to accelerate planning. The suggested method is evaluated utilizing two approaches to establishing the search space, i.e., decompose MTRT into traditional path planning problems and involve one target at a time, and consider all unsearched targets while planning. Simulations in discrete grid maps demonstrate that DAA provides at least equivalent searching paths to A* and other algorithms, but at much less running time.

Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction

Through advancement of the Vehicle-to-Everything (V2X) network, road safety, energy consumption, and traffic efficiency can be significantly improved. An accurate vehicle trajectory prediction benefits communication traffic management and network resource allocation for the real-time application of the V2X network. Recurrent neural networks and their variants have been reported in recent research to predict vehicle mobility. However, the spatial attribute of vehicle movement behavior has been overlooked, resulting in incomplete information utilization. To bridge this gap, we put forward for the first time a hierarchical trajectory prediction structure using the capsule neural network (CapsNet) with three sequential components. First, the geographic information is transformed into a grid map presentation, describing vehicle mobility distribution spatially and temporally. Second, CapsNet serves as the core model to embed local temporal and global spatial correlation through hierarchical capsules. Finally, extensive experiments conducted on actual taxi mobility data collected in Porto city (Portugal) and Singapore show that the proposed method outperforms the state-of-the-art methods.

Implementation of Grid Mapping Method for Firefighting Legged Robot

The firefighting robot contest is an event to encourage high education students to explore electronics and robotic skill. The difficulty level of the contest increases from year to year though the arena layout is relatively similar. The field of arena consists of maze, detachable doors and distractorswhich requires smart idea to enable the robot to be well-performed. The combination of home position and random fire spot at each round of the game make each team have to find the most effective method to win the match. This paper presents an implementation the grid map method on the Azmi four legged robot using ultrasonic PING sensor, GPYA021 infrared sensor and GY-955 gyroscope. An ultrasonic sensor is attached on each of four sides of the robot. The data from each sensor will be interpreted as the distance of the front, right, left and back of the robot relative to the wall. Interpretation depends on the robot's current position. Virtual mapping technique was used by assuming that the arena consists of grids which have uniform size. Each grid was given a consecutive index numbering i.e a symbolic number which in practice be manifested in terms of distance unit. The results showed that the grid map method in this study worked well, tested on four randomized configurations, the robot successfully carried out the search task and returned to the home position with an acceptable execution time.

Impact of land-use dynamics and climate change scenarios on Groundwater recharge in the case of Anger watershed, Ethiopia

An assessment of land use dynamics and climate variability impacts on hydrological processes is vital and a prerequisite for effective water resources management. This study aimed to quantify the effect of land-use changes and long-term climate variability on the Anger watershed's annual groundwater recharge, which covers a total drainage area of 7717 km2. The WetSpass (Water and Energy Transfer between Soil, Plants, and Atmosphere under quasi-Steady State) model was used to investigate the impact of land cover and climate variability on groundwater. The Mann–Kendall (MK) test was used to analyze the spatial variations and temporal trends of the climate variables in the watershed. Input data for the model, such as land use, hydro-meteorological data, soil texture, topography, and groundwater elevation parameters, were prepared in the form of gridded maps with a 30 m resolution. The model results indicate that land-use change and climate variability considerably impact distributed groundwater recharges. Groundwater recharge decreased with land use in 2000 and 2019, respectively, as compared to baseline land usage (1985). The study also demonstrates how the anticipated future combination of less precipitation and higher temperatures has a detrimental effect on the watershed's annual average groundwater recharge. Future rising temperatures and reduced precipitation are projected to result in an average annual groundwater recharge showing significant decreases in 2050, 2080, and 2110, respectively, according to scenario-based models. The result has provided valuable information on the management and response of groundwater recharge to climate and land-use changes, particularly for the Anger watershed and for the total country as well.

Fast three-dimensional simulation based on data constraints and methods to improve the realism of knitted sweaters

The purpose of this research is to use the methods of data constraints for parametric design and three-dimensional simulation of knitted sweaters to achieve the purpose of enhancing the simulation effect. First, based on the basic plates of knitted sweaters as a reference, the parameters of the curve parts of the plates were constrained, and the constraint relationships were applied to build the parametric design models. After entering the user parameters, the changed plates can be obtained. On this basis, the transformation of flat plates to three-dimensional surfaces was carried out. The triangular grid mapping relationships between two-dimensional plates and three-dimensional surfaces were constructed, and the physical properties were further specified for the mesh particles. When the surface texture parameters of the two-dimensional plane are designed, the three-dimensional surface will change accordingly. In addition, this research put forward the stitching and matching methods of the seams of knitted sweaters. The joint size was equally divided, and the number of coils was converted into the number of triangular mesh. Point-to-point mesh was used to realize the accurate counterpoint fusion of coils. The feasibility of parametric and virtual design methods was verified by taking an example, which provides ideas for the computer-aided design of knitted sweaters and promotes the improvement of production efficiency without wasting raw materials.

Active Navigation System for a Rubber-Tapping Robot Based on Trunk Detection

To address the practical navigation issues of rubber-tapping robots, this paper proposes an active navigation system guided by trunk detection for a rubber-tapping robot. A tightly coupled sliding-window-based factor graph method is proposed for pose tracking, which introduces normal distribution transform (NDT) measurement factors, inertial measurement unit (IMU) pre-integration factors, and prior factors generated by sliding window marginalization. To actively pursue goals in navigation, a distance-adaptive Euclidean clustering method is utilized in conjunction with cylinder fitting and composite criteria screening to identify tree trunks. Additionally, a hybrid map navigation approach involving 3D point cloud map localization and 2D grid map planning is proposed to apply these methods to the robot. Experiments show that our pose-tracking approach obtains generally better performance in accuracy and robustness compared to existing methods. The precision of our trunk detection method is 93% and the recall is 87%. A practical validation is completed in robot rubber-tapping tasks of a real rubber plantation. The proposed method can guide the rubber-tapping robot in complex forest environments and improve efficiency.

Optimization of Wheelchair-Mounted Robotic Arms’ Base Placement by Fusing Occupied Grid Map and Inverse Reachability Map

In a household setting, a wheelchair-mounted robotic arm (WMRA) can be useful for assisting elderly and disabled individuals. However, the current WMRA can only perform movement and grasping tasks through joystick remote control. This method results in low efficiency due to poor coordination between the mobile platform and the robotic arm as well as the numerous operational steps required. To improve the efficiency and success rate of the robot in task execution, this paper proposes a parking location optimization method that combines the occupied grid map (OGM) and the inverse reachability map (IRM). Firstly, the SLAM algorithm is used to collect environment information, which is then stored in the form of an occupied grid map. The robotic arm workspace is then gridded, and the inverse reachability map is calculated based on the grasping pose of the target object. Finally, the optimal position of the mobile platform is obtained by comparing the optimal location point in the inverse reachability map and the obstacle information in the occupied grid map. This process achieves base placement optimization based on the grasping pose. The experimental results demonstrate that this method reduces the user operation time by 97.31% and overall task completion time by 40.57% when executing household environment tasks compared with the joystick control, increasing the range of executable tasks compared with the algorithm of the EL-E robot and reducing task completion time by 23.48% for the same task. This paper presents a parking location optimization method that can improve the grasping efficiency of the robotic arm and achieve parking location position selection for the WMRA in a household environment.

Eco-geographic Survey, In-situ Characterization and Spatial Analysis of Phenotypic Traits Diversity among the Maize Landraces Collected from Chhattisgarh

Exploration and germplasm collection expedition undertaken in the three districts of Chhattisgarh state in the present study resulted in the collection of 43 diverse local maize genotypes. The collected accessions were characterized in-situ on a farmer’s field for six ear and seed-specific quantitative characters and the recorded data was analyzed for quantification and analysis of the genetic variability among the accessions. Significant variability was recorded in all the studied traits viz. ear length (7.33–26.67 cm), ear girth(8.00–16.33 cm), number of kernel rows per ear (8-18), number of kernels per ear row (12.67–45.33), ear weight (25.45–204.88 g) and 100 kernel weight (9.74–38.66 g). The first three components explained 99.61% of the total variation; the first principal component represented 97.33%. Cluster analysis based on Euclidean distances classified 43 maize genotypes into five clusters with clustering together of 9, 18, 13, 2 and 1 accessions. Geo-referencing analysis of recorded data was done using DIVA-GIS software. The diversity pattern of maize germplasm accessions revealed by the grid maps confirmed the existence of considerable genetic diversity for all the six quantitative traits in the surveyed area also discussed in this study.

Improving the accuracy of computational fluid dynamics simulations of coiled cerebral aneurysms using finite element modeling

Cerebral aneurysms are a serious clinical challenge, with ∼half resulting in death or disability. Treatment via endovascular coiling significantly reduces the chances of rupture, but the techniquehas failure rates of ∼20 %. This presents a pressing need to develop a method fordetermining optimal coildeploymentstrategies. Quantification of the hemodynamics of coiled aneurysms using computational fluid dynamics (CFD) has the potential to predict post-treatment outcomes, but representing the coil mass in CFD simulations remains a challenge. We use the Finite Element Method (FEM) for simulating patient-specific coil deployment for n = 4 ICA aneurysms for which 3D printed in vitro models were also generated, coiled, and scanned using ultra-high resolution synchrotron micro-CT. The physical and virtual coil geometries were voxelized onto a binary structured grid and porosity maps were generated for geometric comparison. The average binary accuracy score is 0.8623 and the average error in porosity map is 4.94 %. We then conduct patient-specific CFD simulations of the aneurysm hemodynamics using virtual coils geometries, micro-CT generated oil geometries, and using the porous medium method to represent the coil mass. Hemodynamic parameters including Neck Inflow Rate (Qneck) and Wall Shear Stress (WSS) were calculated for each of the CFD simulations. The average relative error in Qneck and WSS from CFD using FEM geometry were 6.6 % and 21.8 % respectively, while the error from CFD using a porous media approximation resulted in errors of 55.1 % and 36.3 % respectively; demonstrating a marked improvement in the accuracy of CFD simulations using FEM generated coil geometries.

A computational workflow to study CO2 transport in porous media with irregular grains: Coupling a Fourier series-based approach and CFD

Understanding CO2 transport in porous media is important for enhanced oil recovery and CO2 sequestration. In this study, we aimed to develop a computational workflow to automatically simulate CO2 transport in porous media, including generating digital porous media from a grain morphological perspective, a pipeline, and pore-scale modeling. A method for generating porous media using a Fourier series-based approach was proposed for this workflow. Contact detection introduced the Floyd-Warshall algorithm to improve the computational efficiency and application scope of the method. Furthermore, the simultaneous application of parametric equations and grid mapping makes the output results diverse. A pipeline was built to link the proposed method with pore-scale modeling to automate the workflow. Drawing on the developed computational workflow, we simulated CO2 transport in porous media and discussed the effects of varying the different pore structures, capillary numbers and wettabilities. In different pore structures, the dual-porosity model is easily broken through by CO2 causing a small swept area making its oil recovery lower than the single-porosity model. In the capillary number, the CO2-free oil recovery period decreased with an increasing capillary number in the media. The concomitant imbibition in the low capillary number increased swept area and oil recovery but can lead to economic and time losses. At the wettabilities, the maximum recovery was achieved with weak CO2 wetting. This computational workflow can be used as an effective tool to investigate CO2 transport within porous media, contributing to the design and application of innovative enhanced CO2 sequestration and oil recovery method.