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This study examines the effectiveness of various spatial data sources and pathfinding algorithms for route determination in forested environments, focusing on the Mazovia region of Poland. Accurate and efficient forest route planning is critical for both military operations and crisis management, highlighting the need for reliable data and robust algorithms. The analysis centers on three primary spatial data sources that can support forest routing: the civilian Topographic Objects Database (TOD) and OpenStreetMap (OSM), along with the military-specific Vector Map Level 2 (VML2). Two commonly used pathfinding algorithms, Dijkstra and A* (the latter with six heuristic variations), were tested to assess their suitability and performance in these contexts. This study was conducted across ten of the largest forested areas in Mazovia, with route determinations performed between selected pairs of start and end points within each forest area. The findings indicate that the TOD database yielded the most stable and consistent routes, while the A* algorithm with Euclidean distance heuristics proved to be the fastest among the tested variants. In contrast, OSM data presented challenges due to inconsistencies, resulting in some routes being undeterminable, where connections between start and end points were lacking. These results underscore the importance of data quality and algorithm selection in effective forest route planning. [ABSTRACT FROM AUTHOR]

This paper introduces iTEVAC, an advanced model of the Trusted Evacuation System (TEVAC), designed to optimize emergency evacuation procedures within commercial centers. Building upon the principles of TEVAC, iTEVAC incorporates additional features such as isolation, firefighting, and rescue intervention strategies to enhance the system's performance and efficiency. iTEVAC leverages the power of Fog computing and Internet of Things (IoT) devices to provide real-time guidance for individuals during emergencies. The system operates by executing alerts for isolated nodes and those unable to find an exit route, providing detailed guidance for nodes that need to move within the commercial center's network. A key advantage of iTEVAC is its decision-making speed, which is less than 1 second, crucial in high-stakes, time-sensitive situations. The iTEVAC model also addresses the limitations of older buildings, traditional markets, and crowded areas that often lack efficient and integrated evacuation systems. It allows users to rely not only on smart navigation guide panel but also on their smartphones to find the safest direction using local Wi-Fi and broadband cellular networks, supported by Fog computing. The iTEVAC model represents a significant advancement in emergency response strategies, contributing to the development of more effective and intelligent evacuation strategies. [ABSTRACT FROM AUTHOR]

Post-earthquake scenarios have brought connectivity challenges to the forefront of research in recent years. Particularly, the randomness and large-scale of road and telecom network infrastructure damage within the aftermath hinders communications coverage restoration during the most critical hours when lives are at stake. This paper proposes a seismic-based post-earthquake city and cellular network model to statistically predict the status of road closures and base station failures based on fundamental earthquake measurements. The presented model considers a generic Manhattan grid-based city model, with buildings featuring random heights. In addition, it quantifies the probability of building collapse and the consequent probability of road closure which accounts for the random debris nature. Moreover, the model accounts for the dependencies between the debris width, height, and the relative location with respect to the earthquake epicenter. Furthermore, a routing algorithm for movable and deployable resource units (MDRUs) that exploits the derived statistical model is proposed to ensure that MDRUs are efficiently deployed and connectivity is restored swiftly. The proposed routing algorithm is extensively tested over a large set of simulation scenarios depicting different earthquake magnitudes and was shown to provide up to 31% traveling time reduction compared to a blind distance-based approach. Finally, the conducted simulations showed the effectiveness of the proposed MDRUs deployment approach in restoring the communications coverage from a signal-to-interference plus noise ratio perspective in the majority of the considered locations.

This paper introduces iTEVAC, an advanced model of the Trusted Evacuation System (TEVAC), designed to optimize emergency evacuation procedures within commercial centers. Building upon the principles of TEVAC, iTEVAC incorporates additional features such as isolation, firefighting, and rescue intervention strategies to enhance the system's performance and efficiency. iTEVAC leverages the power of Fog computing and Internet of Things (IoT) devices to provide real-time guidance for individuals during emergencies. The system operates by executing alerts for isolated nodes and those unable to find an exit route, providing detailed guidance for nodes that need to move within the commercial center's network. A key advantage of iTEVAC is its decision-making speed, which is less than 1 second, crucial in high-stakes, time-sensitive situations. The iTEVAC model also addresses the limitations of older buildings, traditional markets, and crowded areas that often lack efficient and integrated evacuation systems. It allows users to rely not only on smart navigation guide panel but also on their smartphones to find the safest direction using local Wi-Fi and broadband cellular networks, supported by Fog computing. The iTEVAC model represents a significant advancement in emergency response strategies, contributing to the development of more effective and intelligent evacuation strategies.

Penelitian ini bertujuan untuk menyelesaikan permasalahan di bidang transportasi terkait penentuan rute perjalanan menggunakan transportasi umum dengan mengembangkan sistem dan aplikasi perangkat bergerak yang mampu merekomendasikan rute perjalanan angkutan umum di kota Malang. Rekomendasi rute perjalanan dihasilkan dari penerapan algoritme Dijkstra dari rute transportasi angkutan umum di Kota Malang yang dimodelkan dalam struktur data graf. Bagian yang akan dinilai pada penelitian ini adalah perbandingan efektivitas penggunaan format JSON terutama pada penghematan bandwidth. Teknik kompresi yang digunakan pada penelitian ini adalah Zlib yang dikombinasikan dengan encoding Base64. Analisis juga dilakukan terhadap beban tambahan dan penggunaan memori akibat proses kompresi serta analisis waktu komputasi dan penggunaan memori akibat penyederhanaan struktur data graf melalui pendekatan visual dengan algoritme Douglas-Peucker. Penggunaan dokumen dalam format JSON terbukti efektif digunakan sebagai media komunikasi, penggunaan kompresi data dapat menghemat penggunaan bandwidth untuk mendistribusikan data di Internet sebesar 64,61%, dan memberikan beban tambahan pada memori yang diperlukan oleh server untuk kompresi data sebesar 0,44%. Selain itu, dengan penyederhanaan graf secara visual, waktu proses yang diperlukan untuk merekomendasikan rute transportasi menjadi lebih singkat 81,24% serta berkurangnya penggunaan memori hingga 72,99%. Abstract This research aims to solve problems in the field of transportation related to determining travel plans using public transportation by developing systems and mobile applications based on Google Android that recommend public transport plans in Malang City. The plans are generated from the implementation of Dijkstra's algorithm, which is modeled in the graph data structure. This study determines the effectiveness of using JSON format, the bandwidth saved for distributing data on the Internet from Zlib data compression and Base64 encoding, and analyzes additional memory usage due to compression and the impact of data simplification through a visual approach on transport network graphs with the Douglas-Peucker algorithm. This study suggested that the use of JSON format is effective as a communication medium, and the use of data compression allows clients to save bandwidth usage by up to 64.61% with an additional 0.44% of memory load for data compression. Additionally, visually simplifying the graph data improved the system's performance; it reduced the average processing time to recommend transportation routes by 81.24% and reduced memory usage by up to 72.99%.

This study introduces an optimization approach for calculating the shortest path in mobile robot route planning. The proposed solution targets real-time processing requirements by offering a high-performance alternative. This is achieved by embedding in the dedicated hardware an architecture which emphasizes parallelism. Through improvements in parallel exploration techniques, our solution aims to present not only a boost in performance but also a dynamic adaptation to graph changes, accommodating randomly occurring edge insertions or deletions as environmental conditions fluctuate. We present the developed architecture alongside its results. Our method efficiently updates obstacle matrices, resulting in a remarkable 120-fold improvement for 1024-node graphs. When utilizing a cost-effective device like the Cyclone IV E, it achieves approximately 12 times the performance of software applications. [ABSTRACT FROM AUTHOR]

Several algorithms such as A*, Dijkstra, SLAM (Simultaneous Localisation and Mapping) and APF (Artificial Potential Field) were used in this study to create local maps, plan the shortest path, and localize mobile robots. In fact, when compared to the SLAM/APF method, these algorithms achieved a reduction in road length by 1.18 meters. Nevertheless, the SLAM/APF method outperformed the Dijkstra algorithm by reducing navigation time by 7.62 seconds and surpassed the A* method by reducing navigation time by 5.76 seconds.

The aviation industry has proposed multiple solutions to reduce fuel consumption, air pollution, and noise at airports, one of which involves deploying electric trucks for aircraft towing between the stand and the runway. However, the introduction of tow trucks results in increased surface traffic, posing challenges from the perspective of air traffic controllers (ATCOs). Various solutions involving automated planning and execution have been proposed, but many are constrained by their inability to manage multiple active runways simultaneously, and their failure to account for the tow truck battery state of charge during assignments. This paper presents a novel system for taxi operations that employs autonomous tow trucks to enhance ground operations and address deficiencies in existing approaches. The system focuses on identifying conflict-free solutions that minimise taxi-related delays and route length while maximising the efficient use of the tow trucks. The algorithm operates at a strategic level and uses a centralised approach. It has the capacity to cater for multiple active runways and considers factors such as the tow truck battery state of charge and availability of charging stations. Furthermore, the proposed algorithm is capable of scheduling and routing tow trucks for aircraft taxiing without generating traffic conflicts. [ABSTRACT FROM AUTHOR]

An optimized robot path-planning algorithm is required for various aspects of robot movements in applications. The efficacy of the robot path-planning model is vulnerable to the number of search nodes, path cost, and time complexity. The conventional A-star (A*) algorithm outperforms other grid-based algorithms because of its heuristic approach. However, the performance of the conventional A* algorithm is suboptimal for the time, space, and number of search nodes, depending on the robot motion block (RMB). To address these challenges, this paper proposes an optimal RMB with an adaptive cost function to improve performance. The proposed adaptive cost function keeps track of the goal node and adaptively calculates the movement costs for quickly arriving at the goal node. Incorporating the adaptive cost function with a selected optimal RMB significantly reduces the searches of less impactful and redundant nodes, which improves the performance of the A* algorithm in terms of the number of search nodes and time complexity. To validate the performance and robustness of the proposed model, an extensive experiment was conducted. In the experiment, an open-source dataset featuring various types of grid maps was customized to incorporate the multiple map sizes and sets of source-to-destination nodes. According to the experiments, the proposed method demonstrated a remarkable improvement of 93.98% in the number of search nodes and 98.94% in time complexity compared to the conventional A* algorithm. The proposed model outperforms other state-of-the-art algorithms by keeping the path cost largely comparable. Additionally, an ROS experiment using a robot and lidar sensor data shows the improvement of the proposed method in a simulated laboratory environment. [ABSTRACT FROM AUTHOR]

throughput for high-resolution remote video surveillance. 5G cellular network as today's most advanced wireless technology will be the perfect match for Agriculture 4.0 requirements. In its maturity process, the 5G network requires various optimizations, one of which is by making route algorithm calculation modifications in terms of determining the best route for a data packet from a data source to a data destination. To achieve this goal, it requires research in the form of experiments using network simulator. Software Define Network (SDN) as network programmability is used to modify route in Dijkstra algorithm calculation, and run several use case that simulate 5G network characteristic. By adding bandwidth utilization and latency parameters into the routing algorithm calculations, 5G requirements such as packet loss below 1% and latency below 5ms are successfully achieved. These positive results may be further tested on real 5G networks, if in the future this research also gets positive results in testing on a real 5G network, then cellular network customers will be able to experience an increase in service quality.

On numerous occasions, we often have very little information or must make a decision considering qualitative aspects that are difficult to evaluate. This study focuses on obtaining objective criteria to assist in decision-making in the design phase of pressurized water pipes in collective irrigation networks. In the layout of these networks, various types of paths and roads for laying pipes can be encountered, and it is not always a simple task to obtain the least costly layout or the one with the fewest issues during construction. In order to obtain objective results, different layout alternatives are evaluated using the Analytic Hierarchy Process (AHP) Multicriteria Analysis Methodology and the Dijkstra algorithm to obtain optimal solutions. This is applied to twelve case studies where the types of available layout paths are identified as alternatives, and four criteria are established for their evaluation. Recognized experts in irrigation modernization conduct the evaluation to derive weighting coefficients for selecting the optimal layout. The coefficients or resistances obtained weigh the lengths of the pipes, allowing the selection of the most suitable alternative based on the defined criteria. The results are compared with a network designed by an expert using classical methodologies, revealing cost improvements in the design phase and a reduction in conflict points, thus leading to faster execution of the works. [ABSTRACT FROM AUTHOR]

Path planning is an essential concern in robotic systems, and it refers to the process of determining a safe and optimal path starting from the source state to the goal one within dynamic environments. We proposed an improved path planning method in this article, which merges the Dijkstra algorithm for path planning with Potential Field (PF) collision avoidance. In real-time, the method attempts to produce multiple paths as well as determine the suitable path that’s both short and reliable (safe). The Dijkstra method is employed to produce multiple paths, whereas the Potential Field is utilized to assess the safety of each route and choose the best one. The proposed method creates links between the routes, enabling the robot to swap between them if it discovers a dynamic obstacle on its current route. Relating to path length and safety, the simulation results illustrate that Dynamic Dijkstra-Potential Field (Dynamic D-PF) achieves better performance than the Dijkstra and Potential Field each separately, and going to make it a promising solution for the application of robotic automation within dynamic environments. [ABSTRACT FROM AUTHOR]

The increasing amount of air pollution on a global scale is a major source of worry. The elevated levels of pollution are affecting the health of urban dwellers and motorists to a great extent. Since the situation has become so dire, it has become an absolute necessity to find effective ways to reduce air pollution and mitigate the negative effects of air pollution on human health. In this paper, the task of determining the least polluted route is addressed and how this can be used to curb pollution is discussed. The proposed work uses Dijkstra's algorithm, suggests a weight selection metric for air pollution parameters and analyzes the efficacy of the proposed algorithm in determining the least polluted route. The algorithm is tested on real-time data extracted from the Central Pollution Control Board, a statutory organization under the Ministry of Environment, Forest and Climate Change, Government of India. From the results obtained, it is found that the least polluted routes between a given source location and other different locations of a city can be determined using Dijkstra's algorithm accurately and the algorithm has a time complexity of O(V2). Further, using a least polluted route map will help motorists take less polluted routes, diverting air pollution off congested streets and enabling national governments to create policies like constructing flyovers or widening roads in increasingly polluted areas. [ABSTRACT FROM AUTHOR]

Bottlenecks and imbalance in parallel programs can significantly affect performance of parallel execution. Finding these bottlenecks is a key issue in performance analysis of MPI programs especially on a large scale. One of the ways to discover bottlenecks is to analyze the critical path of the parallel program: the longest execution path in the program activity graph. There are a number of methods of finding the critical path; however, most of them suffer a performance drop when scaled. In this paper, we analyze several methods of critical path finding based on classical Dijkstra and Delta-stepping algorithms along with the proposed algorithm based on topological sorting. Corresponding algorithms for each approach are presented including additional enhancements for increasing performance. The implementation of the algorithms and resulting performance for several benchmark applications (NAS Parallel Benchmarks, CP2K, OpenFOAM, LAMMPS, and MiniFE) are analyzed and discussed. [ABSTRACT FROM AUTHOR]

Introduction: In today's dynamic logistics landscape, the role of intelligent robots is paramount for enhancing effciency, reducing costs, and ensuring safety. Traditional path planning methods often struggle to adapt to changing environments, resulting in issues like collisions and conflicts. This research addresses the challenge of path planning and control for logistics robots operating in complex environments. The proposed method aims to integrate information from various perception sources to enhance path planning and obstacle avoidance, thereby increasing the autonomy and reliability of logistics robots. Methods: The method presented in this paper begins by employing a 3D Convolutional Neural Network (CNN) to learn feature representations of objects within the environment, enabling object recognition. Subsequently, Long Short-Term Memory (LSTM) models are utilized to capture spatio-temporal features and predict the behavior and trajectories of dynamic obstacles. This predictive capability empowers robots to more accurately anticipate the future positions of obstacles in intricate settings, thereby mitigating potential collision risks. Finally, the Dijkstra algorithm is employed for path planning and control decisions to ensure the selection of optimal paths across diverse scenarios. Results: In a series of rigorous experiments, the proposed method outperforms traditional approaches in terms of both path planning accuracy and obstacle avoidance performance. These substantial improvements underscore the effcacy of the intelligent path planning and control scheme. Discussion: This research contributes to enhancing the practicality of logistics robots in complex environments, thereby fostering increased effciency and safety within the logistics industry. By combining object recognition, spatio-temporal modeling, and optimized path planning, the proposed method enables logistics robots to navigate intricate scenarios with higher precision and reliability, ultimately advancing the capabilities of autonomous logistics operations. [ABSTRACT FROM AUTHOR]

BTS (Base Transceiver Station) is a telecommunications infrastructure in the form of a tower with a transmitting antenna that facilitates wireless communication between communication devices and operator networks. BTS as a signal receiver and transmitter, its existence must be known to a user, such as maintenance staff and BTS tower operation staff to deal with existing problems. So a special system is needed for users to use in determining the closest path between the user and the location of the BTS tower. The purpose of this study is to make it easier for the user to determine the shortest path to the intended BTS tower location and to analyze the Dijkstra algorithm and the A* algorithm in determining the shortest route between the user's location and the location of the Telkomsel BTS tower in Semarang, Central Java. The results obtained from the 82 test data tested with the information system created show that Dijkstra's algorithm is more efficient than the A* algorithm. Validation is carried out by calculating with the system between the user's location and 82 test data or the location of BTS towers in the city of Semarang. The results of the validation carried out explained that the system was running according to the functions made and the results of the calculations carried out by the system were appropriate. [ABSTRACT FROM AUTHOR]

Many infrastructure facilities such as bridges, communication towers, and power transmission lines were constructed more than 30 years ago, and their inspection work is significant for disaster prevention and mitigation. Infrastructure inspections using unmanned aerial vehicles (UAVs) have been introduced and are expected to improve inspection efficiency significantly. However, infrastructure facilities are located in various places, and the cost of dispatching pilots and UAVs to each infrastructure is increasing costs. This paper proposes a migratory UAV (MiUAV) system in which a UAV automatically moves to infrastructure facilities and a pilot inspects remotely. The proposed MiUAV consists of multiple ports that can accommodate and charge UAVs, and the UAVs can travel to remote infrastructure by flying between the ports. Assuming an environment where multiple UAVs move simultaneously, setting up a route through a port with high utilization or a bad weather area will increase the travel time to the destination. The proposed route decision scheme uses weights considering port utilization ratio and inclement weather probability and autonomously sets a route to the destination for each UAV using the Dijkstra method. Computer simulations evaluate that the proposed scheme can reduce travel time by avoiding high-utilization ports and bad weather areas.

Planning flight paths for unmanned aerial vehicles in urban areas requires consideration of safety, legal, and economic aspects as well as attention to social factors for gaining public acceptance. To solve this many-objective path planning problem in the three-dimensional space, we propose a hybrid framework combining an exact Dijkstra search and a metaheuristic evolutionary optimization. Given a start and an endpoint, we optimize a path regarding the risk in case of a system failure, the radio signal disturbance between the aerial vehicle and a ground station, the energy consumption, and the noise immission on city residents. The optimization includes constraints for static obstacle collision avoidance and compliance with the minimum flight altitude. The result is a set of smooth and three-dimensional paths that realize different trade-offs between the defined objectives. As an example, we consider an urban transportation application for aerial vehicles in San Francisco. For all tests, we use real-world data from OpenStreetMap. In a statistical evaluation, we test the efficiency of our framework against different state-of-the-art optimizers. Moreover, we extend the framework with two features that allow the user to integrate arbitrary objectives and unknown scenarios into the path planning framework.

The agriculture sector has witnessed a transformation with the advent of smart sensing devices, leading to improved crop yield and quality. However, the management of data collection from numerous sensors across vast agricultural areas, as well as the associated charging requirements, presents significant challenges. This paper addresses the major research problem by proposing an innovative solution for charging agricultural sensors. The introduction of an energy-constrained device (ECD) enables wireless charging and transmission of soil data to a centralized server. The proposed ECDs will enable enhanced data collection, precision agriculture, optimized resource allocation, timely decision-making, and remote monitoring and control. A bidirectional wireless charging drone is employed to efficiently charge the ECDs. To optimize energy usage, a prioritized Dijkstra algorithm determines the ECDs to be charged and plans the shortest route for the drone. The wireless charging drone landing-charging station achieves an efficiency of 91.3%, delivering 72 W of power within a 5 mm range. Furthermore, the ECD possesses a data transmission range of 100 m and incorporates deep sleep functionality, allowing for a remarkable 30-day battery life.

In the paper we discuss and compare two commonly used methods of finding the shortest paths in networks, namely Dijkstra's and A∗ algorithms. We compare their effectiveness in terms of traversing road network in circumstances that require swift decision making in the event of dynamically changing road conditions on the basis of studies conducted for evacuation plans. To build a proper model of such a network, a method of appropriate edge-weighting is introduced, based on empirical data collected by other researchers. Then, we use the basics of the theory of quasimetric spaces to introduce a heuristic to such graphs, which is easy to calculate metric. The heuristic we obtain is both admissible and consistent, which allows us to use it efficiently in A∗ search algorithms. The developed application can be used in studies into evacuation from hazardous areas. In this case, optimum calculative efficiency is achievable with a simultaneous reduction of calculation time (when compared to Dijkstra's algorithm). Our application can be applied during the first stage, i.e., prior to the occurrence of a disaster, since this is an appropriate time for preparation by planning, drilling, early warning, and designating the rescue services that are to participate in the following stages.

Urban transport systems play a major role in the development of today's societies, but they require technological changes to reduce their environmental impact. The problem lies in their level of autonomy, which is why electrical energy production systems are proposed for self-consumption, efficiently feeding their accumulators. As the energy provided by photovoltaic installations has lower recharge speeds, conventional systems with high transfer amperage and higher voltage are required. For this reason, solar installations are used for additional services and to support their autonomy. The present work tries to find the best solution for both constant voltage and peak current systems. Once found, these solutions will be applied in real time for the dynamic recharging of battery packs, trying to achieve vehicles that are progressively more energetically autonomous. To solve these situations, a new computational method for calculating voltage and amperage has been developed in this work, based on Dijkstra's minimum path search algorithm on graph theory, adapted to electrical circuits. Once this algorithm has been established, the panel performance analysis sensors, developed at the University of Malaga, are combined with different electronic solutions described in this article (Wi-Fi relay devices using esp8266 chips or feeding these relays through panels and establishing the voltage drop to switch the connection), achieving precise and sufficiently fast solutions at very low cost. Both series and parallel transitions are possible, depending on the type of energy generation required. The theoretical solutions using Minkowski paths, analyzed in the past, have been simulated and subsequently constructed in this paper, indicating the diagrams necessary for their realization. [ABSTRACT FROM AUTHOR]

The aviation industry has proposed multiple solutions to reduce fuel consumption, air pollution, and noise at airports, one of which involves deploying electric trucks for aircraft towing between the stand and the runway. However, the introduction of tow trucks results in increased surface traffic, posing challenges from the perspective of air traffic controllers (ATCOs). Various solutions involving automated planning and execution have been proposed, but many are constrained by their inability to manage multiple active runways simultaneously, and their failure to account for the tow truck battery state of charge during assignments. This paper presents a novel system for taxi operations that employs autonomous tow trucks to enhance ground operations and address deficiencies in existing approaches. The system focuses on identifying conflict-free solutions that minimise taxi-related delays and route length while maximising the efficient use of the tow trucks. The algorithm operates at a strategic level and uses a centralised approach. It has the capacity to cater for multiple active runways and considers factors such as the tow truck battery state of charge and availability of charging stations. Furthermore, the proposed algorithm is capable of scheduling and routing tow trucks for aircraft taxiing without generating traffic conflicts.

In the paper we discuss and compare two commonly used methods of finding the shortest paths in networks, namely Dijkstra's and A* algorithms. We compare their effectiveness in terms of traversing road network in circumstances that require swift decision making in the event of dynamically changing road conditions on the basis of studies conducted for evacuation plans. To build a proper model of such a network, a method of appropriate edge-weighting is introduced, based on empirical data collected by other researchers. Then, we use the basics of the theory of quasimetric spaces to introduce a heuristic to such graphs, which is easy to calculate metric. The heuristic we obtain is both admissible and consistent, which allows us to use it efficiently in A* search algorithms. The developed application can be used in studies into evacuation from hazardous areas. In this case, optimum calculative efficiency is achievable with a simultaneous reduction of calculation time (when compared to Dijkstra's algorithm). Our application can be applied during the first stage, i.e., prior to the occurrence of a disaster, since this is an appropriate time for preparation by planning, drilling, early warning, and designating the rescue services that are to participate in the following stages. [ABSTRACT FROM AUTHOR]

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PT. X is a company domiciled in Gedebage, Bandung which is engaged in the manufacturing industry by producing precision parts using CNC machines. PT. X is a sub-contracting company that usually serves project work from other companies. PT. Y and PT. Z is a regular customer who often works with PT. X. So that PT. X often sends finished products directly to PT. Y who is domiciled on Jl. Gatot Subroto, Bandung and also PT. Z who is domiciled on Jl. Pajajaran, Bandung. To minimize the cost of distribution of goods, PT. X must determine an adequate path taking into account the optimization of transportation costs. One of the variables that affect transportation costs is distance. It is assumed that the optimal path for transportation costs is the shortest distance using the Dijkstra method. This test uses data from Google Maps to find out the distance to each destination, making it easier to get the shortest path. Obtained the shortest path from PT. X to PT.Y is 12.3 Km via West Java Police then Carefour, while the shortest route is from PT. Y to PT. Z is 10.7 Km via Simpang Lima then Vie Hotel Westhoff. So that the optimal total mileage for distributing goods is 23 Km.

Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths from an origin to a destination. Choosing an appropriate path planning algorithm helps to ensure safe and effective point-to-point navigation, and the optimal algorithm depends on the robot geometry as well as the computing constraints, including static/holonomic and dynamic/non-holonomically-constrained systems, and requires a comprehensive understanding of contemporary solutions. The goal of this paper is to help novice practitioners gain an awareness of the classes of path planning algorithms used today and to understand their potential use cases—particularly within automated or unmanned systems. To that end, we provide broad, rather than deep, coverage of key and foundational algorithms, with popular algorithms and variants considered in the context of different robotic systems. The definitions, summaries, and comparisons are relevant to novice robotics engineers and embedded system developers seeking a primer of available algorithms.

The use of computerized formative assessments in K–12 classrooms has yielded valuable data that can be utilized by learning analytics (LA) systems to produce actionable insights for teachers and other school-based professionals. For example, LA systems utilizing computerized formative assessments can be used for monitoring students' progress in reading and identifying struggling readers. Using such LA systems, teachers can also determine whether progress is adequate as the student works towards their instructional goal. However, due to the lack of guidelines on the timing, number, and frequency of computerized formative assessments, teachers often follow a one-size-fits-all approach by testing all students together on pre-determined dates. This approach leads to a rigid test scheduling that ignores the pace at which students improve their reading skills. In some cases, the consequence is testing that yields little to no useful data, while increasing the amount of instructional time that students miss. In this study, we propose an intelligent recommender system (IRS) based on Dijkstra's shortest path algorithm that can produce an optimal assessment schedule for each student based on their reading progress throughout the school year. We demonstrated the feasibility of the IRS using real data from a large sample of students in grade two (n = 668,324) and grade four (n = 727,147) who participated in a series of computerized reading assessments. Also, we conducted a Monte Carlo simulation study to evaluate the performance of the IRS in the presence of unusual growth trajectories in reading (e.g., negative growth, no growth, and plateau). Our results showed that the IRS could reduce the number of test administrations required at both grade levels by eliminating test administrations in which students' reading growth did not change substantially. In addition, the simulation results indicated that the IRS could yield robust results with meaningful recommendations under relatively extreme growth trajectories. Implications for the use of recommender systems in K–12 education and recommendations for future research are discussed. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on the path planning of mobile robots in complex two-dimensional terrain. It proposes a fuzzy rule-based path planning algorithm for multiple guide points by changing the spatial point-taking method and combining Dijkstra's and fuzzy logic algorithms. In the process of path planning, the existing algorithms have completed the smoothing process of turning point, but when analyzing the angular acceleration and linear acceleration of mobile robot in the process of movement, it is found that these paths are still difficult to meet the motion law of mobile robot. Through the mobile robot Angle range, velocity, acceleration range such as constraints, can guarantee the absolute path is an excellent way to satisfy the requirement of the mobile robot motion, combined with the Dijkstra algorithm, by using a fuzzy logic system based on considering the environment status of path planning, motion is more suitable for mobile robot path. The simulation results show that this algorithm can solve the complex environment that traditional fuzzy inference algorithms cannot plan. In subsequent studies, this algorithm will extend to group path planning and dynamic environment planning. [ABSTRACT FROM AUTHOR]