Grid Map Research Articles

Advanced 3D Navigation System for AGV in Complex Smart Factory Environments

The advancement of Industry 4.0 has significantly propelled the widespread application of automated guided vehicle (AGV) systems within smart factories. As the structural diversity and complexity of smart factories escalate, the conventional two-dimensional plan-based navigation systems with fixed routes have become inadequate. Addressing this challenge, we devised a novel mobile robot navigation system encompassing foundational control, map construction positioning, and autonomous navigation functionalities. Initially, employing point cloud matching algorithms facilitated the construction of a three-dimensional point cloud map within indoor environments, subsequently converted into a navigational two-dimensional grid map. Simultaneously, the utilization of a multi-threaded normal distribution transform (NDT) algorithm enabled precise robot localization in three-dimensional settings. Leveraging grid maps and the robot’s inherent localization data, the A* algorithm was utilized for global path planning. Moreover, building upon the global path, the timed elastic band (TEB) algorithm was employed to establish a kinematic model, crucial for local obstacle avoidance planning. This research substantiated its findings through simulated experiments and real vehicle deployments: Mobile robots scanned environmental data via laser radar and constructing point clouds and grid maps. This facilitated centimeter-level localization and successful circumvention of static obstacles, while simultaneously charting optimal paths to bypass dynamic hindrances. The devised navigation system demonstrated commendable autonomous navigation capabilities. Experimental evidence showcased satisfactory accuracy in practical applications, with positioning errors of 3.6 cm along the x-axis, 3.3 cm along the y-axis, and 4.3° in orientation. This innovation stands to substantially alleviate the low navigation precision and sluggishness encountered by AGV vehicles within intricate smart factory environments, promising a favorable prospect for practical applications.

Assessing two decades of change across the Eco-Regions of West Africa using remote sensing technology

The landscapes and Eco-Regions of West Africa have a unique biodiversity that reflects climatic regime and human interaction. Changes and trends in the Eco-Region land cover and its productivity was assessed using Aqua MODIS NDVI C6 satellite images on a geographical mapping grid at approximately 250m from between 2003 and 2021. Using cell statistics and geoprocessing tools within a GIS environment, the satellite images were processed and analysed. The processed and analyzed satellite images document changes across the region over 19 years. The results show that; Guinea Savanna Eco-Region records highest changes, with an increase in green productivity between 2007 and 2012 by 9.24 km2; and decrease in green productivity in green productivity between 2007 and 2012 by 9.24km of 4.37 km2 from 2012 to 2017 and by 9.30 km2 from 2017 to 2021. The Grassland/Sudan Savanna fluctuated over the last 19 years with an overall reduction in green productivity by 3.43 km2 while the Guinea Savanna increased by 1.0 km2 over the same period. The Tropical Rainforest Eco-Region had the highest increase in productivity from 2003 to 2007 by 11.25 km2 and less than a km2 increase from 2007 to 2012. This study concluded that there was fluctuation in area coverages among the Eco-Regions across West Africa which varies seasonally and periodically. These Eco-Regions are vital for the development and economic suitability of West Africa, thus the need for continuous monitoring and study. Also, the fluctuation in area coverages detected in the study suggest that certain interventions by policy makers and stakeholders have brought about these changes, of which reforestation and land use policies are inclusive. Policymakers and government agencies are therefore apprised to maintain policies that support green productivity in the region.

Correlative Scan Matching Position Estimation Method by Fusing Visual and Radar Line Features

Millimeter-wave radar and optical cameras are one of the primary sensing combinations for autonomous platforms such as self-driving vehicles and disaster monitoring robots. The millimeter-wave radar odometry can perform self-pose estimation and environmental mapping. However, cumulative errors can arise during extended measurement periods. In particular scenes where loop closure conditions are absent and visual geometric features are discontinuous, existing loop detection methods based on back-end optimization face challenges. To address this issue, this study introduces a correlative scan matching (CSM) pose estimation method that integrates visual and radar line features (VRL-SLAM). By making use of the pose output and the occupied grid map generated by the front end of the millimeter-wave radar’s simultaneous localization and mapping (SLAM), it compensates for accumulated errors by matching discontinuous visual line features and radar line features. Firstly, a pose estimation framework that integrates visual and radar line features was proposed to reduce the accumulated errors generated by the odometer. Secondly, an adaptive Hough transform line detection method (A-Hough) based on the projection of the prior radar grid map was introduced, eliminating interference from non-matching lines, enhancing the accuracy of line feature matching, and establishing a collection of visual line features. Furthermore, a Gaussian mixture model clustering method based on radar cross-section (RCS) was proposed, reducing the impact of radar clutter points online feature matching. Lastly, actual data from two scenes were collected to compare the algorithm proposed in this study with the CSM algorithm and RI-SLAM.. The results demonstrated a reduction in long-term accumulated errors, verifying the effectiveness of the method.

ERA*: Enhanced Relaxed A* algorithm for solving the shortest path problem in regular grid maps

This paper introduces a novel algorithm for solving the point-to-point shortest path problem in a static regular 8-neighbor connectivity (G8) grid. This algorithm can be seen as a generalization of Hadlock algorithm to G8 grids, and is shown to be theoretically equivalent to the relaxed A⁎ (RA⁎) algorithm in terms of the provided solution's path length, but with substantial time and memory savings, due to a completely different computation strategy, based on defining a set of lookup matrices. Through an experimental study on grid maps of various types and sizes (1290 runs on 43 maps), it is proven to be 2.25 times faster than RA⁎ and 17 times faster than the original A⁎, in average. Moreover, it is more memory-efficient, since it does not need to store a G score matrix.

Intelligent Vehicle Decision-Making and Trajectory Planning Method Based on Deep Reinforcement Learning in the Frenet Space.

The complexity inherent in navigating intricate traffic environments poses substantial hurdles for intelligent driving technology. The continual progress in mapping and sensor technologies has equipped vehicles with the capability to intricately perceive their exact position and the intricate interplay among surrounding traffic elements. Building upon this foundation, this paper introduces a deep reinforcement learning method to solve the decision-making and trajectory planning problem of intelligent vehicles. The method employs a deep learning framework for feature extraction, utilizing a grid map generated from a blend of static environmental markers such as road centerlines and lane demarcations, in addition to dynamic environmental cues including vehicle positions across varied lanes, all harmonized within the Frenet coordinate system. The grid map serves as the input for the state space, and the input for the action space comprises a vector encompassing lane change timing, velocity, and vertical displacement at the lane change endpoint. To optimize the action strategy, a reinforcement learning approach is employed. The feasibility, stability, and efficiency of the proposed method are substantiated via experiments conducted in the CARLA simulator across diverse driving scenarios, and the proposed method can increase the average success rate of lane change by 6.8% and 13.1% compared with the traditional planning control algorithm and the simple reinforcement learning method.

Mental search of concepts is supported by egocentric vector representations and restructured grid maps

The human hippocampal-entorhinal system is known to represent both spatial locations and abstract concepts in memory in the form of allocentric cognitive maps. Using fMRI, we show that the human parietal cortex evokes complementary egocentric representations in conceptual spaces during goal-directed mental search, akin to those observable during physical navigation to determine where a goal is located relative to oneself (e.g., to our left or to our right). Concurrently, the strength of the grid-like signal, a neural signature of allocentric cognitive maps in entorhinal, prefrontal, and parietal cortices, is modulated as a function of goal proximity in conceptual space. These brain mechanisms might support flexible and parallel readout of where target conceptual information is stored in memory, capitalizing on complementary reference frames.

Agro-morphological Characterization and Digitization of Job’s Tears (Coix lacryma-jobi L.) Germplasm: A Minor Cereal Crop of Northeastern India

Thirty-seven Job’s tears accessions of India’s northeastern hill region (NEH) were characterized for nine quantitative and eight qualitative traits. Prominent furrow and elliptic shape of grain in soft-shelled types are supposed to be the most important trait that indicates the domestication trend of the crop in NEH region. Principal component analysis showed that agro-morphological traits, namely time for first heading, days to 80% maturity, seed yield/plant (g), number of culms, and length of culm (cm) have significantly contributed to the diversity. Correlation analysis has shown significant correlations between different yield-contributing traits. Diversity and potential traits in each cluster were also identified through cluster analysis. Grid mapping was done to identify the areas for collection of trait-specific germplasm like higher number of culms, length of culm and hundred seed weight. The germplasm accessions IC0332644, IC0416831 and IC0540173 were identified as promising genotypes

An Underwater Distributed SLAM Approach Based on Improved GMRBnB Framework

Multi-vehicle collaborative mapping proves more efficient in constructing maps in unfamiliar underwater environments in comparison to single-vehicle methods. One of the pivotal hurdles of Simultaneous Localization and Mapping (SLAM) with multiple underwater vehicles is map registration. Due to the inadequate characteristics of the underwater grid maps, matching map features poses a challenge, and outliers between maps add to the complexity. We propose an algorithm to solve this problem. This approach employs the Gaussian Mixture Robust Branch and Bound (GMRBnB) algorithm with an interior point filtering technique. Feature point extraction, registration using the GMRBnB algorithm, inlier extraction based on density, and registration of the inlier are performed to obtain a more precise transformation matrix. The results of the simulation and experiments demonstrate that this technique heightens outlier tolerance and reinforces map registration accuracy. The proposed approach surpasses Iterative Closest Point (ICP) and Normal Distributions Transform (NDT) methods with respect to map registration quality.

LSMCL: Long-term Static Mapping and Cloning Localization for autonomous robot navigation using 3D LiDAR in dynamic environments

One of the challenges in autonomous robot navigation applications is recognizing the exact location in a dynamic environment in which the location of surrounding objects changes frequently. This study proposes a long-term static mapping and cloning localization (LSMCL) method for estimating real-time accurate location using only natural landmarks even in a dynamic environment using a 3D LiDAR sensor. LSMCL comprises long-term static mapping (LSM) and cloning localization (CL). A LSM creates a 2D grid map and a 3D geometric feature map for objects whose positions do not change in space. A CL uses the generated 2D grid map and particle filter to estimate the 2D global position at the initial stage. After cloning the 2D global location to 3D space, the location is tracked through a 3D feature map and map matching. To verify the LSMCL’s usability in a real dynamic environment, a robot navigation experiment was conducted in a highly dynamic parking lot. The experimental results, analyzed in terms of initial localization success rate, location estimation accuracy and precision, processing time, and congested space application confirmed the LSMCL’s real-world applicability.

Comparison of log-derivative and renormalized Numerov methods in low-energy two-body collisions

The log-derivative (LOGD) and renormalized Numerov (RN) methods are popularly used in inelastic and reactive scattering calculations. The computational precision of two methods and their improved versions are compared in two-body diatomic and bimolecular collisions at low temperatures. In diatomic 40K-133Cs collision example the single-channel calculations show the solution-improved RN method has the advantage over others when large fixed propagating interval used. The relative error of local scattering length in different propagating sectors are explored. In multi-channel 40K-133Cs collisions the predicted positions of Feshbach resonances by using mapping grid points indicate strong potential-following character in LOGD methods, which is consistent with the analysis on local scattering length in single-channel calculations. In multiple open-channel calculations the elastic and inelastic scatterings and their dependence on collision energy are compared as well. In bimolecular 23Na87Rb-23Na87Rb long-range reactive scattering example the elastic and reactive rate coefficients are investigated. The original LOGD method displays surprisingly relatively high accuracy with less mapping grid points and a wide range of mapping parameters. By increasing the number of mapping grid points the improved LOGD exhibits definitely superiority on precision with proper mapping parameters. We demonstrate our consequent is applicable in other colliding systems.

Average monthly recharge, surface runoff, and actual evapotranspiration estimation using WetSpass-M model in Low Folded Zone, Iraq

Abstract The evaluation of the spatial and temporal distribution of groundwater recharge is required to develop the regional groundwater model for more precise simulations of various management scenarios. WetSpass-M (Water and Energy Transfer between Soil, Plants, and Atmosphere under Steady-State Conditions), which is a GIS-based spatially distributed water balance model, is deployed to evaluate monthly groundwater recharge, surface runoff, and actual evapotranspiration in the Low Folded Zone from 2000 to 2019. ArcGIS software prepares the essential relevant input data for the Wetspass-M model as grid maps. They include monthly climatological measurements (precipitation, temperature, and wind speed), land cover distribution, soil map, groundwater depth, topography, and slope. The mean annual groundwater recharge, evapotranspiration, and runoff were found to be 128, 131, and 72 mm, respectively. Accordingly, recharge accounts for 39% of the precipitation, while the rest, 40 and 21%, become evapotranspiration and surface runoff, respectively. WetSpass-M model results are meant to enable integrated groundwater modeling. The study of simulation data demonstrates that the WetSpass-M model accurately simulates the hydrological water budget components of the Low Folded Zone. In addition, a better understanding of the simulated spatial and temporal distribution of water balance components is beneficial for managing and planning the available water resources of the Low Folded Zone in Iraq, which faces water scarcity threats.

Autonomous hierarchy creation for computationally feasible near-optimal path planning in large environments

Mobile robots have increasingly been used in warehouses, factories, open spaces, on roads, and urban areas to transport goods and people. Path planning in complex and large environments using classical graph-based search is computationally too intensive. One of the main challenges of mobile robotics is describing the large robot’s workspaces with a reduction of graph complexity and improvement of path planning efficiency. A representation by a hierarchical graph (H-graph) facilitates graph creation and reduces the complexity of path planning. In this paper, we present an algorithm for autonomously generating a hierarchical graph of the environment from floor plans. The hierarchical abstraction depicts the environment in levels, from the most detailed to the most abstract representation of the environment, where pre-computed partial paths at the most detailed level are graph edges at a higher level. We use the E* algorithm to find partial paths in the most detailed abstraction level, and we propose the extraction of higher levels automatically from lower levels. We verified the proposed H-graph creation on three cases: our University premises as an example of a multi-building environment, a large warehouse with a lot of shelves, and an outdoor urban environment which is represented by a city map. All cases result in five abstract levels. The result shows that the path planning algorithm searches about 50 to 800 times fewer nodes when using the H-graph as the environment representation than when using a plain graph representing the entire environment as an occupancy grid map.

Improved coverage path planning for indoor robots based on BIM and robotic configurations

Robotics holds great potential to improve productivity in construction, and coverage path planning (CPP) is an essential capability crucial to various applications, including floor cleaning and environmental monitoring. However, there is still a lack of a comprehensive CPP system that can handle complex indoor conditions and various robotic properties. An improved CPP (ICCP) system that leverages building information modeling (BIM) and robotic configurations for indoor robots is proposed in this study. Firstly, BIM is semantically enriched to generate semantic trapezoidal grid maps (TGMs); Next, a novel concept called “coverage bonus” is incorporated into coverage pattern analysis to enable farsighted decision making; Finally, the coverage sequence is optimized by solving the cluster generalized traveling salesman problem, resulting in routes that minimize both coverage distances and disruptions in indoor activities. Experimental validation shows that the ICPP system can not only attain optimal coverage performance with the highest coverage ratio (97.6%) but also ensure the adherence to indoor coverage rules. Future research will focus on enhancing coverage ratio through tunable hyperparameters, optimizing computation time in ICCP, and expanding the study to multi-robot scenarios.

A Comparative Investigation of Duplex and Super Duplex Stainless Steels Processed through Laser Powder Bed Fusion

The aim of this paper was to compare duplex (DSS) and super duplex stainless steel processed by laser powder bed fusion (LPBF) based on the process parameters and microstructure–nanomechanical property relationships. Each alloy was investigated with respect to its feedstock powder characteristics. Optimum process parameters including scanning speed, laser power, beam diameter, laser energy density, and layer thickness were defined for each alloy, and near-fully dense parts (>99.9%) were produced. Microstructural analysis was performed via optical (OM), scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD). The samples were subjected to stress relief and high-temperature annealing. EBSD revealed the crystallographic orientation and quantified the phases in the as-built and annealed sample conditions. The as-built samples revealed a fully ferritic microstructure with a small amount of grain boundary austenite in the SDSS microstructure. High-temperature solution annealing resulted in the desired duplex microstructure for both alloys. There were no secondary phases present in the microstructure after both heat treatments. Nanoindentation generated nanomechanical (modulus) mapping grids and quantified the nanomechanical (both hardness and modulus) response; plasticity and stress relief were also assessed in all three conditions (as-built, stress-relieved, and annealed) in both DSS and SDSS. Austenite formation in the annealed condition contributed to lower hardness levels (~4.3–4.8 Gpa) and higher plastic deformation compared to the as-built (~5.7–6.3 Gpa) and stress-relieved conditions (~4.8–5.8 Gpa) for both alloys. SDSS featured a ~60% austenite volume fraction in its annealed and quenched microstructure, attributed to its higher nickel and nitrogen contents compared to DSS, which exhibited a ~30% austenite volume fraction.

The omnipolar voltages as an ablation indicator for pulmonary vein isolation in patients with atrial fibrillation

Abstract Background Lesion Index (LSI) without considering myocardial voltage is well-known to guide pulmonary vein isolation (PVI) ablation using the TactiCath™ Ablation Catheter in paroxysmal atrial fibrillation (PAF) patients. However, some patients show gaps after LSI-guided PVI ablation. Meanwhile, the omnipolar voltages detected using EnSite™ Omnipolar Technology (OT) have been reported to improve the underestimation of voltage. Purpose We aimed to clarify their differences by comparing the omnipolar voltages with conventional bipolar voltages and to verify whether it could be an additional indicator of LSI. Methods [Study1] We retrospectively studied 48 PAF patients (32 male, 69±8yrs) who received catheter ablation (CA). The left atrial voltage mapping was performed with Advisor™ HD Grid Mapping Catheter under right atrial pacing. The voltages within a 1cm diameter circle were measured at each site (Roof, Anterior, Posterior, Septal, Lateral; 238 sites) and evaluated the omnipolar and conventional bipolar voltages. [Study2] Twelve patients (8 males, 71±9yrs) with PAF who underwent CA were prospectively studied. The left atrial voltage mapping was performed before PVI ablation. The pre-ablation omnipolar voltages / bipolar voltages and LSI on each ablation site (558 points) of the PVI line were measured and compared in gapped and non-gapped regions. The gapped areas were defined as sites that required additional ablation after the first-round ablation of PVI. Results [Study1] The omnipolar voltages were higher than the bipolar voltages (total 238 sites, 2.84±2.05 vs. 2.67±1.81 mV, P<0.0001) in the entire left atrium. There was no feature in the difference between the omnipolar voltage and the bipolar voltage, according to the site and the voltage height (Figure). [Study2] Five gaps (14 ablation points) were confirmed after the first round of LSI-guided PVI (LSI 4.5±0.4, minimum 3.8 - maximum 5.8) in 4 of 12 patients. The omnipolar voltage in the gapped areas was higher than the non-gapped areas (544 ablation points) (5.64±1.93 vs. 3.73±2.82mV, P=0.0118, cut-off value 3.56mV, the area under the curve 0.74678). Meanwhile, there was no difference between the gapped and non-gapped areas in LSI and the conventional bipolar voltage. Conclusion Although the omnipolar voltage obtained by OT is detected only slightly higher than the bipolar potential, the higher omnipolar voltage could be an indicator of additional ablation to the LSI.

Improving omnipolar electrogram reconstruction: an animal model study

Abstract Background Cardiac arrhythmias are a significant cause of morbidity and mortality worldwide. The characterization of cardiac tissue's electrophysiological substrate is crucial for the diagnosis and treatment of these conditions. High-density (HD) electrode arrays, such as the Advisor™ HD Grid Mapping Catheter and Sensor Enabled™ (SE), are commonly used to examine cardiac tissue locally and create activation maps. However, the current method for reconstructing omnipolar EGMs, a novel technique claimed to be orientation-independent, has limitations. The enhancement of this reconstruction methodology has the potential to improve the accuracy and reliability of electrophysiological mapping. This, in turn, can have a positive impact on patients suffering from cardiac arrhythmias, ultimately leading to better clinical outcomes. Purpose We propose a novel reconstruction method to estimate omnipolar EGM that overcomes the angle dependence weakness of the current method to enhance the reconstruction of electrophysiological signals. Methodology Prior experiments were conducted on isolated rabbit hearts using a 128-electrode array with electrodes spaced 1mm apart over the ventricular epicardium. Four-electrode cliques were designed from these experiments, with different inter-electrode distances as depicted in Figure 1A, to reconstruct the current and novel omnipolar configurations. Metrics were designed to assess the quality of the electrogram signals, including the bipolar loop area and the ratio of the bipolar components of the omnipole (see fig. 2). The bipoles were calculated from unipolar electrograms, and the signal was corrected with a 45º offset in the cross. The propagation angle was then detected, and the local activation time (LAT) was estimated. Results In the bipole plots, it was observed that the morphology of the bipolar loop becomes distorted with an increase in the inter-electrode distance, becoming thinner for distances less than 2mm. There is a direct relation between the areas in the bipolar loop and the inter-electrode distance (see Figure 2A). Furthermore, the values of the area in the cross are always comparable to the best-performing triangular clique. For the ratio of bipoles, the relation is inversely related to distance (see Figure 2B). Similarly, the performance of the cross is comparable to the best-performing triangular omnipole. Conclusion This study highlights the importance of accurate reconstruction of omnipolar EGMs for the diagnosis and treatment of cardiac arrhythmias. To achieve precise reconstruction, inter-electrode distances smaller than 2mm are recommended. The proposed reconstruction method based on the cross configuration is more accurate and robust to wavefront propagation, provided that the recommended distance criterion is met. This study opens up new possibilities for the design of new devices and practices in the clinic.Figure 1Figure 2

Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution.

A spanning tree algorithm with adaptive pruning with low redundancy coverage rate

In reality, when UAV is used in region reconnaissance, the shape of region is usually irregular. Selecting an algorithm to find a complete coverage path with smaller coverage redundancy rate, lower path motion complexity and theoretical energy consumption has certain research significance for the complete coverage path planning (CCPP). In existing algorithms, there is always a high redundant coverage rate under irregularly shaped regions. The motion complexity and the theoretical energy consumption of the path also increase. In this paper, a kind of spanning tree with adaptive pruning (STAP) algorithm based on the grid map is proposed to solve the above problems: on the one hand, it uses a greedy growth strategy to determine the branching growth direction, and proposes an adaptive pruning method for self-optimization of the initial path; on the other hand, a kind of nearest neighbor sub-region connection rule of adjacent paths is proposed to reduce the complexity and the coverage redundancy rate. The STAP algorithm searches for all grid centers, grows, and optimizes the spanning tree by creating paths between the centers. At the end of the optimization, it creates a complete coverage path based on the distribution of the spanning tree. Experiments show that when the STAP algorithm is applied to unobstructed regions, it plans the path with the lowest redundant coverage rate, which reaches 2.6296% and 4.9402%; when it is applied to the regions include obstacles, the advantages of the algorithm are more significant: On the one hand, the path planned by STAP has the least total length, the lowest number of turning and turning back. On the other hand, the redundant coverage rate of the path is still the lowest, reaching 4.1362% and 2.7765%, respectively. The STAP is primarily suitable for situations about CCPP under grid maps formed by irregularly shaped regions, and it holds certain significance for ground reconnaissance of UAVs.

A novel path planning algorithm for ships in dynamic current environments

Ship meteorological path planning has received increasing attention, especially in the dynamic wind, wave, and current environments. Determining a safe and efficient voyage path is one of the most important issues for ships. In order to reduce the navigation costs in terms of distance, energy, and time under multiple constraints, a novel ship path planning method is proposed in a spatially-temporally variant environment, which can find the optimum paths under different task requirements. An improved A* algorithm in a dynamic current environment (A*-DCE) is proposed, which introduces the weights of distance, energy, and time to generate paths with significantly different costs. The attractive/repulsive field replaces the distance cost estimation of the conventional A* algorithm. The grid map is segmented based on velocity data to determine energy and time cost estimations. Under the varied spatial-temporal current environment in the Nw-European Shelf region, the B-spline curve is used to improve the smoothness and to reduce the yaw cost. In addition, the paths in the candidate set that conform to the Pareto front are obtained using the NSGA-II algorithm. The smallest distance path has the same distance-optimal capability as the path from the conventional A* algorithm. The energy and time cost of the remaining optimal paths are also reduced.

Simultaneous Localization and Mapping and Tag-Based Navigation for Unmanned Aerial Vehicles

This paper presents navigation techniques for an Unmanned Aerial Vehicle (UAV) in a virtual simulation of an indoor environment using Simultaneous Localization and Mapping (SLAM) and April Tag markers to reach a target destination. In many cases, UAVs can access locations that are inaccessible to people or regular vehicles in indoor environments, making them valuable for surveillance purposes. This study employs the Robot Operating System (ROS) to simulate SLAM techniques using LIDAR and GMapping packages for UAV navigation in two different environments. In the Tag-based simulation, the input topic for April Tag in ROS is camera images, and the calibration of position with a tag is done through assigning a message to each ID and its marker image. On the other hand, navigation in SLAM was achieved using a global and local planner algorithm. For localization, an Adaptive Monte-Carlo Localization (AMCL) technique has been used to identify factors contributing to inconsistent mapping results, such as heavy computational load, grid mapping accuracy, and inadequate UAV localization. Furthermore, this study analyzed the April Tag-based navigation algorithm, which showed satisfactory outcomes due to its lighter computing requirements. It can be ascertained that by using ROS packages, the simulation of SLAM and Tag-based UAV navigation inside a building can be achieved.