Grid-based Map Research Articles

GRID-FAST: A Grid-based Intersection Detection for Fast Semantic Topometric Mapping

This article introduces a novel approach to constructing a topometric map that allows for efficient navigation and decision-making in mobile robotics applications. The method generates the topometric map from a 2D grid-based map. The topometric map segments areas of the input map into different structural-semantic classes: intersections, pathways, dead ends, and pathways leading to unexplored areas. This method is grounded in a new technique for intersection detection that identifies the area and the openings of intersections in a semantically meaningful way. The framework introduces two levels of pre-filtering with minimal computational cost to eliminate small openings and objects from the map which are unimportant in the context of high-level map segmentation and decision making. The topological map generated by GRID-FAST enables fast navigation in large-scale environments, and the structural semantics can aid in mission planning, autonomous exploration, and human-to-robot cooperation. The efficacy of the proposed method is demonstrated through validation on real maps gathered from robotic experiments: 1) a structured indoor environment, 2) an unstructured cave-like subterranean environment, and 3) a large-scale outdoor environment, which comprises pathways, buildings, and scattered objects. Additionally, the proposed framework has been compared with state-of-the-art topological mapping solutions and is able to produce a topometric and topological map with up to 92% fewer nodes than the next best solution. The method proposed in this article has been implemented in the robotics framework ROS and is open-sourced. The code is available at: https://github.com/LTU-RAI/GRID-FAST.

Construction and assessment of a fire risk index system for typical grasslands in Xinjiang, China

BackgroundFire hazards have a substantial impact on grassland ecosystems, and they are becoming more frequent and widespread because of global changes and human activities. However, there is still a lack of a widely accepted or practical method to evaluate grassland fire risk. In our study of typical grasslands in northern Xinjiang, we selected 18 evaluation indicators for grassland fires from three aspects of hazard, exposure, and vulnerability. Employing the analytic hierarchy process, weighted comprehensive evaluation method, and standard deviation classification, we determined the fire risk level thresholds, aiming to develop efficient and precise methods for assessing grassland fire risks, and ultimately created a grid-based map of grassland fire risk levels.ResultsThe risk level of grassland fires is determined by the combined spatial heterogeneity of fire-causing factors’ hazard and fire hazard-bearing bodies’ vulnerability and exposure. The hazard of grassland fire and fire hazard-bearing bodies’ vulnerability and exposure are dominated by medium level and medium–low level. Most areas of grassland fire risk levels are medium–low, medium, or medium–high risk, with few areas being high risk or low risk. The grassland fire risk exhibits a spatial distribution characterized by higher risks in the western and lower in the eastern; high and medium–high risk areas are primarily distributed in the western and some northeastern regions of the study area. The simulate result effectively represents the spatial distribution of grassland fire in the research area.ConclusionWe established a grassland fire risk index system and model, creating a spatial distribution map of grassland fire risk levels based on grid. Few grassland areas have fire risks and show a patchy distribution. The results generally reflect the spatial distribution pattern of grassland fire risks in the study area. This research provides technical support for scientifically formulating local grassland fire disaster prevention and relief strategies.

Data Mining Virtual Contaminated Sites to Develop an Educational Learning Tool supported by a k-NN Machine Learning Predictive Algorithm for use in Environmental Engineering Education

Recent work by Mumford et al. used high-resolution numerical simulations of contaminated sites to evaluate the state of practice for contaminated site investigation1. These simulations were originally developed in collaboration with academic and industry partners, including the U.S. Department of Defense's Environmental Security Technology Certification Program (ESTCP), as a training tool for environmental monitoring and performance optimization. This project focuses on the development of an algorithm to determine available borehole information in those simulations based on user-input, to help leverage this work to create an educational tool. The algorithm begins by validating user-specified coordinates to ensure they fall within an acceptable range. Leveraging principles from linear algebra and a grid-based mapping system, it identifies the nearest existing borehole on a predefined coordinate grid. To optimize efficiency, the algorithm utilizes a 2-D array to store and retrieve coordinate data. It calculates distances from the input coordinates to all available borehole locations on the grid, to identify the shortest distance and select the closest borehole, enabling students to map contamination within virtual sites. In addition to boreholes, the application incorporates data for Membrane Interface Probes (MIPs), monitoring wells, groundwater samples, and soil samples that are used to investigate contaminated sites, providing a comprehensive tool for training environmental professionals. The k-nearest neighbors (k-NN) algorithm predicts Membrane Interface Probe (MIP) Photoionization Detector (ECD) readings at future locations, which indicate chlorinated contaminant levels. Two models were evaluated: one using spatial data and ECD values, and another incorporating electrical conductivity (EC) and hydraulic conductivity (K), which are also measured by MIP. By making use of additional parameters, variations in soil properties were better represented, leading to more accurate predictions. Through iterative testing and refinement, the tool’s accuracy and user-interface have been enhanced, ensuring robust reliability for classroom applications. These tools are slated for integration into 4th-year subsurface contamination courses at Queen's University, Carleton, and the University of Iowa. By automating borehole selection, instructors can interact more with students and dedicate less time to data processing tasks. The k-NN application will enable the use of machine learning for site assessment to optimize MIP instrument placement to reduce bias. Assistance from Cole Van De Ven (Carleton University) and Jessica Meyer (University of Iowa) in collecting virtual contaminated site data is gratefully acknowledged.

Investigating the Bone Bruise Patterns in Pediatric Patients With Contact and Noncontact Acute Anterior Cruciate Ligament Tears: A Multicenter Study

Background: In adults with anterior cruciate ligament (ACL) tears, bone bruises on magnetic resonance imaging (MRI) scans provide insight into the underlying mechanism of injury. There is a paucity of literature that has investigated these relationships in children with ACL tears. Purpose: To examine and compare the number and location of bone bruises between contact and noncontact ACL tears in pediatric patients. Study Design: Cohort study; Level of evidence, 3. Methods: Boys ≤14 years and girls ≤12 years of age who underwent primary ACL reconstruction surgery between 2018 and 2022 were identified at 3 separate institutions. Eligibility criteria required detailed documentation of the mechanism of injury and MRI performed within 30 days of the initial ACL tear. Patients with congenital lower extremity abnormalities, concomitant fractures, injuries to the posterolateral corner and/or posterior cruciate ligament, previous ipsilateral knee injuries or surgeries, or closed physes evident on MRI scans were excluded. Patients were stratified into 2 groups based on a contact or noncontact mechanism of injury. Preoperative MRI scans were retrospectively reviewed for the presence of bone bruises in the coronal and sagittal planes using fat-suppressed T2-weighted images and a grid-based mapping technique of the tibiofemoral joint. Results: A total of 109 patients were included, with 76 (69.7%) patients sustaining noncontact injuries and 33 (30.3%) patients sustaining contact injuries. There were no significant differences between the contact and noncontact groups in terms of age (11.8 ± 2.0 vs 12.4 ± 1.3 years; P = .12), male sex (90.9% vs 88.2%; P > .99), time from initial injury to MRI (10.3 ± 8.1 vs 10.4 ± 8.9 days; P = .84), the presence of a concomitant medial meniscus tear (18.2% vs 14.5%; P = .62) or lateral meniscus tear (69.7% vs 52.6%; P = .097), and sport-related injuries (82.9% vs 81.8%; P = .89). No significant differences were observed in the frequency of combined lateral tibiofemoral (lateral femoral condyle + lateral tibial plateau) bone bruises (87.9% contact vs 78.9% noncontact; P = .41) or combined medial tibiofemoral (medial femoral condyle [MFC] + medial tibial plateau) bone bruises (54.5% contact vs 35.5% noncontact; P = .064). Patients with contact ACL tears were significantly more likely to have centrally located MFC bruising (odds ratio, 4.3; 95% CI, 1.6-11; P = .0038) and less likely to have bruising on the anterior aspect of the lateral tibial plateau (odds ratio, 0.27; 95% CI, 0.097-0.76; P = .013). Conclusion: Children with contact ACL tears were 4 times more likely to present with centrally located MFC bone bruises on preoperative MRI scans compared with children who sustained noncontact ACL tears. Future studies should investigate the relationship between these bone bruise patterns and the potential risk of articular cartilage damage in pediatric patients with contact ACL tears.

A Path Planning System for Orchard Mower Based on Improved A* Algorithm

The application of intelligent mobile robots in agriculture has emerged as a new research frontier, with the integration of autonomous navigation technology and intelligent agricultural robots being the key to the widespread adoption of smart agricultural machinery. This paper investigates comprehensive coverage path planning for tracked lawnmowers within orchard environments and addresses challenges related to task allocation and battery life. Firstly, in this study, the motion model of the tracked lawnmower was initially simplified based on assumptions about the orchard environment. Force analyses were conducted on each of its motion mechanisms. For the known orchard environment, a grid-based mapping technique was employed to model the orchard environment. Then, in order to improve the algorithm speed and reduce the number of turns during the lawnmower’s traversal, the A* search algorithm was enhanced by combining the method of robot cluster traversal in the orchard environment. Finally, the improved method was simulated and verified in the MATLAB platform to investigate the influence of the number of lawnmower clusters on the path planning in the connected and non-connected orchards. Furthermore, two sets of on-site field trials were meticulously designed to validate the reliability, practicality, and efficacy of the simulation experiments.

Multi-UAV Path Planning and Following Based on Multi-Agent Reinforcement Learning

Dedicated to meeting the growing demand for multi-agent collaboration in complex scenarios, this paper introduces a parameter-sharing off-policy multi-agent path planning and the following approach. Current multi-agent path planning predominantly relies on grid-based maps, whereas our proposed approach utilizes laser scan data as input, providing a closer simulation of real-world applications. In this approach, the unmanned aerial vehicle (UAV) uses the soft actor–critic (SAC) algorithm as a planner and trains its policy to converge. This policy enables end-to-end processing of laser scan data, guiding the UAV to avoid obstacles and reach the goal. At the same time, the planner incorporates paths generated by a sampling-based method as following points. The following points are continuously updated as the UAV progresses. Multi-UAV path planning tasks are facilitated, and policy convergence is accelerated through sharing experiences among agents. To address the challenge of UAVs that are initially stationary and overly cautious near the goal, a reward function is designed to encourage UAV movement. Additionally, a multi-UAV simulation environment is established to simulate real-world UAV scenarios to support training and validation of the proposed approach. The simulation results highlight the effectiveness of the presented approach in both the training process and task performance. The presented algorithm achieves an 80% success rate to guarantee that three UAVs reach the goal points.

Transcranial magnetic stimulation mapping of the motor cortex: comparison of five estimation algorithms.

There are currently five different kinds of transcranial magnetic stimulation (TMS) motor mapping algorithms available, from ordinary point-based algorithms to advanced field-based algorithms. However, there have been only a limited number of comparison studies conducted, and they have not yet examined all of the currently available algorithms. This deficiency impedes the judicious selection of algorithms for application in both clinical and basic neuroscience, and hinders the potential promotion of a potential superior algorithm. Considering the influence of algorithm complexity, further investigation is needed to examine the differences between fMRI peaks and TMS cortical hotspots that were identified previously. Twelve healthy participants underwent TMS motor mapping and a finger-tapping task during fMRI. The motor cortex TMS mapping results were estimated by five algorithms, and fMRI activation results were obtained. For each algorithm, the prediction error was defined as the distance between the measured scalp hotspot and optimized coil position, which was determined by the maximum electric field strength in the estimated motor cortex. Additionally, the study identified the minimum number of stimuli required for stable mapping. Finally, the location difference between the TMS mapping cortical hotspot and the fMRI activation peak was analyzed. The projection yielded the lowest prediction error (5.27 ± 4.24 mm) among the point-based algorithms and the association algorithm yielded the lowest (6.66 ± 3.48 mm) among field-based estimation algorithms. The projection algorithm required fewer stimuli, possibly resulting from its suitability for the grid-based mapping data collection method. The TMS cortical hotspots from all algorithms consistently deviated from the fMRI activation peak (20.52 ± 8.46 mm for five algorithms). The association algorithm might be a superior choice for clinical applications and basic neuroscience research, due to its lower prediction error and higher estimation sensitivity in the deep cortical structure, especially for the sulcus. It also has potential applicability in various other TMS domains, including language area mapping and more. Otherwise, our results provide further evidence that TMS motor mapping intrinsically differs from fMRI motor mapping.

Graph-based robot optimal path planning with bio-inspired algorithms

Recently, bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps. However, these approaches endure performance degradation as problem complexity increases, often resulting in lengthy search times to find an optimal solution. This limitation is particularly critical for real-world applications like autonomous off-road vehicles, where high-quality path computation is essential for energy efficiency. To address these challenges, this paper proposes a new graph-based optimal path planning approach that leverages a sort of bio-inspired algorithm, improved seagull optimization algorithm (iSOA) for rapid path planning of autonomous robots. A modified Douglas–Peucker (mDP) algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough terrains. The resulting mDP-derived graph is then modeled using a Maklink graph theory. By applying the iSOA approach, the trajectory of an autonomous robot in the workspace is optimized. Additionally, a Bezier-curve-based smoothing approach is developed to generate safer and smoother trajectories while adhering to curvature constraints. The proposed model is validated through simulated experiments undertaken in various real-world settings, and its performance is compared with state-of-the-art algorithms. The experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.

Augmenting progress monitoring in soil-foundation construction utilizing SOLOv2-based instance segmentation and visual BIM representation

Efficient management of construction progress requires regular progress tracking, but monitoring progress on a daily basis can be both time-consuming and labor-intensive since the meticulous manual data processing is involved. Soil-foundation construction entails multiple uncertain underground safety risks, the elimination of which requires significant time and effort, thereby increasing the likelihood of schedule overruns. Automation and augmentation of progress monitoring are necessary for soil-foundation construction. There are two primary research challenges in the context of soil-foundation construction: the precise differentiation of objects with similar apparent characteristics, and the accurate detection of partially obscured objects. The existing approaches primarily address such issues by image post-processing rather than augmenting image feature inference. To enhance the efficiency of progress monitoring and facilitate unmanned inspection, an integrated computer vision-based framework for the tracking of soil-foundation construction progress was proposed. An improved SOLOv2 was utilized to qualitatively and quantitatively recognize the construction progress. Subsequently, the actual and differential progress could be integrated into streamlined BIM based on a self-adaptive grid-based mapping method. The approach was applied to a campus building project in China, resulting in a high segmentation accuracy (mAP = 90.9%). The improved SOLOv2 was found to surpass other state-of-the-art segmentation algorithms. Further, the impact of grid size on the mapping accuracy of construction progress was explored. The present study promotes automated schedule tracking and provides a feasible approach for developing a digital twin of soil-foundation construction.

Automatic Generation of 3D Indoor Navigation Networks from Building Information Modeling Data Using Image Thinning

Navigation networks are a common form of indoor map that provide the basis for a wide range of indoor location-based services, intelligent tasks for indoor robots, and three-dimensional (3D) geographic information systems. The majority of current indoor navigation networks are manually modeled, resulting in a laborious and fallible process. Building Information Modeling (BIM) captures design information, allowing for the automated generation of indoor maps. Most existing BIM-based navigation systems for floor-level wayfinding rely on well-defined spatial semantics, and do not adapt well to buildings with irregular 3D shapes, which can make cross-floor path generation difficult. This research introduces an innovative approach to generating 3D indoor navigation networks automatically from BIM data using image thinning, which is referred to as GINIT. Firstly, GINIT extracts grid-based maps for floors from BIM data using only two types of semantics, i.e., slabs and doors. Secondly, GINIT captures cross-floor paths from building components by projecting 3D forms onto a 2D image, thinning the 2D image to capture the 2D projection path, and crossing over the 2D routes with 3D routes to restore the 3D path. Finally, to demonstrate the effectiveness of GINIT, experiments were conducted on three real-world multi-floor buildings, evaluating its performance across eight types of cross-layer architectural component. GINIT overcomes the dependency of space definitions in current BIM-based navigation network generation schemes by introducing image thinning. Due to the adaptability of navigation image thinning to any binary image, GINIT is capable of generating navigation networks from building components with diverse 3D shapes. Moreover, the current studies on indoor navigation network extraction mainly use geometry theory, while this study is the first to generate 3D indoor navigation networks automatically using image thinning theory. The results of this study will offer a unique perspective and foster the exploration of imaging theory applications of BIM.

Optimizing groundwater level monitoring networks with hydrogeological complexity and grid-based mapping methods

Optimizing groundwater level monitoring networks with hydrogeological complexity and grid-based mapping methods

Mapping Productivity-related Spatial Characteristics in Rice-based Cropping Systems in Sri Lanka

Accurate and timely information on the productivity-related spatial characteristics in paddy fields will facilitate accurate decision-making to achieve food security needs. The aim of the present study was to map rice-cultivating regions in Sri Lanka based on crop productivity and related agronomic decisions made. A grid-based map coupled with a paddy land map of the country was used for the sampling protocol, and the generated data were mapped using ArcGIS software. The study revealed the spatial heterogeneity in the productivity of lowland paddy lands, rice varieties cultivated according to their age classes and breeding stations, water sources used for cultivation, and crops grown in lowlands during the water-limited cropping seasons. The constructed maps carrying the productivity-related spatial characteristics of rice across the country could facilitate agricultural decision support systems. The maps could be used to develop novel approaches for the management of rice productivity in rice-based cropping systems in the country.

UAV-Supported Route Planning for UGVs in Semi-Deterministic Agricultural Environments

Automated agricultural operations must be planned and organized to reduce risk and failure potential while optimizing productivity and efficiency. However, the diversity of natural outdoor environments and the varied data types and volumes required to represent an agricultural setting comprise critical challenges for the deployment of fully automated agricultural operations. In this regard, this study develops an integrated system for enabling an unmanned aerial vehicle (UAV) supported route planning system for unmanned ground vehicles (UGVs) in the semi-structured environment of orchards. The research focus is on the underpinning planning system components (i.e., world representation or map generation or perception and path planning). In particular, the system comprises a digital platform that receives as input a geotagged depiction of an orchard, which is obtained by a UAV. The pre-processed data define the agri-field’s tracks that are transformed into a grid-based map capturing accessible areas. The grid map is then used to generate a topological path planning solution. Subsequently, the solution is translated into a sequence of coordinates that define the calculated optimal path for the UGV to traverse. The applicability of the developed system was validated in routing scenarios in a walnuts’ orchard using a UGV. The contribution of the proposed system entails noise reduction techniques for the accurate representation of a semi-deterministic agricultural environment for enabling accuracy in the route planning of utilized automated machinery.

Homodimeric complexes of the 90–231 human prion: a multilayered computational study based on FMO/GRID-DRY approach

The molecular interaction properties and aggregation capabilities disclosed by PrP-E200K, a pathogenic mutant of the human prion protein, were investigated in detail using multilayered computational approaches. In a previous work, we reported that the electrostatic complementarity between region1 (negative) and region3 (positive) has been assumed to lead to a head-to tail interaction between 120 and 231 PrP-E200K units and to initiation of the aggregation process. In this work, we extended the PrP-E200K structure by including the unstructured 90–120 segment which was found to assume different conformations. Plausible models of 90–231 PrP-E200K dimers were calculated and analyzed in depth to identify the nature of the involved protein–protein interactions. The unstructured 90–120 segment was found to extend the positively charged region3 involved in the association of PrP-E200K units which resulted to be driven by hydrophobic interactions. The combination of molecular dynamics, protein–protein docking, grid-based mapping, and fragment molecular orbital approaches allowed us to provide a plausible mechanism of the early state of 90–231 PrP-E200K aggregation, considered a preliminary step of amyloid conversion.

Assessing the Diversity and Distribution Pattern of the Speciose Genus Lycocerus (Coleoptera: Cantharidae) by the Global-Scale Data

Species richness patterns and endemism on the large-scale play a significant role in biogeography and biodiversity conservation. This study aimed to explore the diversity centers and endemic areas of a large cantharid genus Lycocerus, so as to test whether the hypothesis of montane and island systems biodiversity in previous studies was supported. In this study, a comprehensive species’ geographical database on the global scale consisting of 4,227 records for 324 Lycocerus species was compiled and analyzed. Species richness pattern was mapped into a grid-based map with a spatial resolution of 1° × 1° fishnet. An unbalanced pattern was identified, and it showed that the centers of species richness of Lycocerus were situated in Eastern Himalayas, Hengduan Mountains, Eastern Sichuan Mountains, Taiwan, and Japan. Further analyses based on two approaches, including parsimony analysis of endemicity (PAE) and endemicity analysis (EA), were applied to detect areas of endemism (AOEs) at three different grid sizes (1°, 1.5°, and 2°). Finally, a total of nine AOEs were detected, including five montane areas (Himalayan areas, Hengduan Mountains, South edge of China, Eastern China Mountains, and Eastern Sichuan Mountains), three islands (Taiwan Island, Japan, and Korean Peninsula), and one plateau (Shan Plateau), which were generally consistent with the species richness pattern. The results verify that montane and island systems have an essential role in promoting the formation of diversity centers and AOEs because of their complex topography, varied habitat and geological events.

Vision-Aided Path Planning Using Low-Cost Gene Encoding for a Mobile Robot

Path planning is intrinsically regarded as a multi-objective optimization problem (MOOP) that simultaneously optimizes the shortest path and the least collision-free distance to obstacles. This work develops a novel optimized approach using the genetic algorithm (GA) to drive the multi-objective evolutionary algorithm (MOEA) for the path planning of a mobile robot in a given finite environment. To represent the positions of a mobile robot as integer-type genes in a chromosome of the GA, a grid-based method is also introduced to relax the complex environment to a simple grid-based map. The system architecture is composed of a mobile robot, embedded with the robot operating system (ROS), the ArUco system and a laptop, executing the algorithms of path planning and image processing. Both simulations and experimental results are presented to verify the feasibility of the proposed method. In applications, this work can be employed in a commercial ball-collecting or an object-carrying robot.

Intelligent Path Planning Approach for Autonomous Mobile Robot

This paper presents a new path planning algorithm for an autonomous mobile robot. It is desired that the robot reaches its goal in a known or partially known environment (e.g., a warehouse or an urban environment) and avoids collisions with walls and other obstacles. To this end, a new, efficient, simple, and flexible path finder strategy for the robot is proposed in this paper. With the proposed strategy, the optimal path from the robot’s current position to the goal position is guaranteed. The environment is represented as a grid-based map, which is then divided into a predefined number of subfields to reduce the number of required computations. This leads to a reduction in the load on the controller and allows a real-time response. To evaluate the flexibility and efficiency of the proposed strategy, several tests were simulated with environments of different sizes and obstacle distributions. The experimental results demonstrate the reliability and efficiency of the proposed algorithm.

Experimental study of path planning problem using EMCOA for a holonomic mobile robot

In this paper, a comparative study of the path planning problem using evolutionary algorithms, in comparison with classical methods such as the A∗ algorithm, is presented for a holonomic mobile robot. The configured navigation system, which consists of the integration of sensors sources, map formatting, global and local path planners, and the base controller, aims to enable the robot to follow the shortest smooth path delicately. Grid-based mapping is used for scoring paths efficiently, allowing the determination of collision-free trajectories from the initial to the target position. This work considers the evolutionary algorithms, the mutated cuckoo optimization algorithm (MCOA) and the genetic algorithm (GA), as a global planner to find the shortest safe path among others. A non-uniform motion coefficient is introduced for MCOA in order to increase the performance of this algorithm. A series of experiments are accomplished and analyzed to confirm the performance of the global planner implemented on a holonomic mobile robot. The results of the experiments show the capacity of the planner framework with respect to the path planning problem under various obstacle layouts.

Extracting 3D Indoor Maps with Any Shape Accurately Using Building Information Modeling Data

Indoor maps lay the foundation for most indoor location-based services (LBS). Building Information Modeling (BIM) data contains multiple dimensional computer-aided design information. Some studies have utilized BIM data to automatically extract 3D indoor maps. A complete 3D indoor map consists of both floor-level maps and cross-floor paths. Currently, the floor-level indoor maps are mainly either grid-based maps or topological maps, and the cross-floor path generation schemes are not adaptive to building elements with irregular 3D shapes. To address these issues, this study proposes a novel scheme to extract an accurate 3D indoor map with any shape using BIM data. Firstly, this study extracts grid-based maps from BIM data and generates the topological maps directly through the grid-based maps using image thinning. A novel hybrid indoor map, termed Grid-Topological map, is then formed by the grid-based maps and topological maps jointly. Secondly, this study obtains the cross-floor paths from cross-floor building elements by a four-step process, namely X-Z projection, boundary extraction, X-Z topological path generation, and path-BIM intersection. Finally, experiments on eight typical types of cross-floor building elements and three multi-floor real-world buildings were conducted to prove the effectiveness of the proposed scheme, the average accuracy rates of the evaluated paths are higher than 88%. This study will advance the 3D indoor maps generation and inspire the application of indoor maps in indoor LBS, indoor robots, and 3D geographic information systems.

Multicapacitor Approach to Interfacial Proton-Coupled Electron Transfer Thermodynamics at Constant Potential

Theoretical calculations of interfacial thermodynamics at constant potential enhance understanding of heterogeneous electrocatalytic reactions. Herein, a strategy is devised for computing reaction thermodynamics for electrochemical proton-coupled electron transfer (PCET), a key elementary step in a wide range of electrocatalytic processes. In this approach, Gibbs free energies obtained from constant charge periodic density functional theory calculations are transformed to grand potentials by using a grid-based mapping procedure in conjunction with a multicapacitor model that accounts for constantly charged surface adsorbates. The energetic contribution of the adsorbate capacitor to the grand potential is found to be essential for computing proton-coupled redox potentials at constant potential. This strategy is applied to graphite-conjugated catalysts, wherein organic acids are attached to carbon surfaces through a conductive phenazine bridge. In these systems, PCET occurs at both the phenazine bridges and the organic acids, which can be negatively or positively charged. For a given graphite-conjugated catalyst active site, the charge of the organic acid adsorbate remains virtually constant for the relevant range of electrode potentials. Moreover, the potential-dependent graphite surface charge density, which excludes this adsorbate charge, is consistent across all systems studied. Within this framework, the potential-dependent PCET reaction free energies are independent of cell size, thereby avoiding the need for computationally expensive cell size extrapolation techniques. The proton-coupled redox potentials computed with this strategy are in agreement with experimental data. This computational strategy, as well as the conceptual insights about the impact of charged adsorbates on electrochemical interfaces, is applicable to other materials and processes.