Dijkstra Algorithm Research Articles

Ship voyage optimisation considering environmental forces using the iterative Dijkstra's algorithm

ABSTRACT The original Dijkstra's algorithm requires the weight map for the network to find the optimum path; however, the change of weights due to temporal change in the met-ocean parameters makes it difficult to use this algorithm for ship navigation; hence, the path was discretised into a limited number of segments and an iterative scheme was developed in which the weight of network was determined based on the environmental condition at the time of ship navigation. A container trip from the Chabahar port in the Gulf of Oman to the Mumbai port in eastern India was studied. The objective was to minimize the fuel consumption. The results showed a decrease of ∼6 tons in the amount of fuel consumption and the total costs in the optimal path were 4.76% less than the path of the great circle arc. The proposed model is simple and effective to be run for operational route optimization.

Targeted multiobjective Dijkstra algorithm

We introduce the Targeted Multiobjective Dijkstra Algorithm (T‐MDA), a label setting algorithm for the One‐to‐One Multiobjective Shortest Path (MOSP) Problem. It is based on the recently published Multiobjective Dijkstra Algorithm (MDA) and equips it with A*‐like techniques. For any explored subpath, a label setting MOSP algorithm decides whether the subpath can be discarded or must be stored as part of the output. A major design choice is how to store subpaths from the moment they are first explored until the mentioned final decision can be made. The T‐MDA combines the polynomially bounded size of the priority queue used in the MDA and a lazy management of paths that are not in the queue. The running time bounds from the MDA remain valid. In practice, the T‐MDA outperforms known algorithms from the literature and the increased memory consumption is negligible. In this paper, we benchmark the T‐MDA against an improved version of the state of the art One‐to‐One MOSP algorithm from the literature on a standard testbed.

Transmission Line-Planning Method Based on Adaptive Resolution Grid and Improved Dijkstra Algorithm.

An improved Dijkstra algorithm based on adaptive resolution grid (ARG) is proposed to assist manual transmission line planning, shorten the construction period and achieve lower cost and higher efficiency of line selection. Firstly, the semantic segmentation network is used to change the remote sensing image into a ground object-identification image and the grayscale image of the ground object-identification image is rasterized. The ARG map model is introduced to greatly reduce the number of redundant grids, which can effectively reduce the time required to traverse the grids. Then, the Dijkstra algorithm is combined with the ARG and the neighborhood structure of the grid is a multi-center neighborhood. An improved method of bidirectional search mechanism based on ARG and inflection point-correction is adopted to greatly increase the running speed. The inflection point-correction reduces the number of inflection points and reduces the cost. Finally, according to the results of the search, the lowest-cost transmission line is determined. The experimental results show that this method aids manual planning by providing a route for reference, improving planning efficiency while shortening the duration, and reducing the time spent on algorithm debugging. Compared with the comparison algorithm, this method is faster in running speed and better in cost saving and has a broader application prospect.

An Efficient Path Planning in Uncertainty Environments using Dynamic Grid-Based and Potential Field Methods

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.

SLAM-based Navigation Technology for Rescue Robots in Post-disaster Scenarios

In order to solve the problems of difficulty and high-risk factor in implementing rescue after disasters, this paper designs an intelligent rescue robot autonomous navigation system based on LIDAR synchronous positioning and map building, with a view to achieving autonomous navigation of robots in complex post-disaster scenarios to complete rescue tasks. Firstly, the autonomous navigation system senses the scene by LiDAR and uses gmapping algorithms to construct a map of the post-disaster environment. Secondly, adaptive Monte Carlo localization algorithm is used to achieve robot localization based on radar and odometer data. Then the robot rescue work path is planned to use the Dijkstra algorithm. And TEB local planning path algorithm is used to control the robot. Finally, to verify the reliability of the autonomous navigation system designed in this paper, the ROS system software framework is used as the basis. The SLAM map construction, global path planning, and local real-time obstacle avoidance are tested practically under the scenario to ensure that the autonomous navigation of the mobile robot meets the requirements.

Development of IoT-Based Automated Dynamic Emergency Response System Against Fire Incidents in Academic Building

The ongoing advancement of architectural and structural designs, high-ceiling spaces, special spaces have made fire disasters increasingly diverse and difficult to predict. It is demanding the need for improved firefighting systems. This study aims to address the need for improved firefighting systems in academic building by proposing the development of an IoT-based automated emergency response website. The proposed system leverages IoT technology, wireless and bluetooth sensor networks to gather real-time data from various sensors and devices installed in the site and uses machine learning algorithms to predict and prevent potential fire incidents. The system also includes an emergency response website that allows users to access real-time information about the fire incident, location, severity, and evacuation instructions. Additionally, the proposed system incorporates Building Information Modelling (BIM) to optimize evacuation and rescue routes, providing early detection and accurate alarm capabilities, evacuation guidance for endangered individuals, and guidance for firefighters. The integration of BIM allows the system to provide a three-dimensional visualization of the site, enabling a more efficient and effective response to fire incidents. Overall, the proposed system aims to improve the safety and security through real-time monitoring and response capabilities. By leveraging the power of IoT technology, machine learning algorithms and BIM, the proposed system aims to reduce the impact of fire disasters by providing accurate and timely information, route optimization and facilitating effective evacuation and rescue efforts.Keywords: Building Information Modeling, IoT, Sensor, Dijkstra Algorithm, Simulation

A REVIEW OF METHODOLOGIES FOR PATH PLANNING AND OPTIMIZATION OF MOBILE ROBOTS

Abstract: This research paper provides a comprehensive review of methodologies for path planning and optimization of mobile robots. With the rapid development of robotics technology, path planning and optimization have become fundamental areas of research for achieving efficient and safe autonomous robot navigation. In this paper, we review the classic and state-of-the-art techniques of path planning and optimization, including artificial potential fields, A* algorithm, Dijkstra's algorithm, genetic algorithm, swarm intelligence, and machine learning-based methods. We analyze the strengths and weaknesses of each approach and discuss their application scenarios. Moreover, we identify the challenges and open problems in this field, such as dealing with dynamic environments and real-time constraints. This paper serves as a comprehensive reference for researchers and practitioners in the robotics community, providing insights into the latest trends and developments in path planning and optimization for mobile robots.

Low Latency Extended Dijkstra Algorithm with Multiple Linear Regression for Optimal Path Planning of Multiple AGVs Network

Dijkstra algorithms are typically used to find the shortest path from a source node to a destination node. It is widely used in various applications due to its reliability and less complexity. This paper presents the extended Dijkstra Algorithm with lower latency and consumes less computing memory intended for implementation in many AGVs networks for effective decentralized task distribution path planning. This paper proposed linear regression normalization across the node network in Dijkstra architecture to reduce computing time and memory consumption. The issue addressed through this optimization focused on reducing the possibilities of collision between AGVs and deadlock. The extended Dijkstra algorithm significantly reduces computing time compared to the traditional Dijkstra algorithm. In addition, the proposed solutions suggest better AGV routing for collision avoidance and deadlock prevention possibilities.

Global path planning with lifetime constraint model-based offline optimized loading strategy for vehicle fuel cell system

To promote the commercialization of fuel cell vehicles, simultaneously improving the dynamic response and durability of fuel cells under loading conditions are extremely important. Since it can seriously affect the system design and lifespan of the fuel cell system. Unfortunately, fast and reliable loading is a pair of contradictory relationships and it needs an optimal loading slope under variable loading conditions. Therefore, this paper proposes a global path planning (GPP) model by considering lifetime limitations to solve the problem. Firstly, the initial GPP model is established according to the initial power and target power, which is exploited to determine exploratory and calibration tests. Subsequently, the trade-off relationship between fast and reliable loading is addressed by the multi-objective cost function based on the dynamic weight coefficient adjustment method to construct a complete GPP model. Eventually, the optimal slopes solution is calculated by the Dijkstra algorithm. The superiority, robustness, and feasibility of the presented method are successfully verified under a 90 kW fuel cell system test bench. The verification test results show that compared with the reference solution, the utilization of optimal solution loading can significantly reduce the dynamic response time by 22.22% and improve the loading reliability by 40.93%. Moreover, the number of calibration tests determined based on the GPP model is 85.19% less than that of the traditional method. Thus, the proposed loading strategy can load quickly and reliably.

Application of Odometry and Dijkstra Algorithm as Navigation and Shortest Path Determination System of Warehouse Mobile Robot

One of the technologies in the industrial world that utilizes robots is the delivery of goods in warehouses, especially in the goods distribution process. This is very useful, especially in terms of resource efficiency and reducing human error. The existing system in this process usually uses the line follower concept on the robot's path with a camera sensor to determine the destination location. If the line and destination are not detected by the sensor or camera, the robot's navigation system will experience an error. it can happen if the sensor is dirty or the track is faded. The aim of this research is to develop a robot navigation system for efficient goods delivery in warehouses by integrating odometry and Dijkstra's algorithm for path planning. Holonomic robot is a robot that moves freely without changing direction to produce motion with high mobility. Dijkstra's algorithm is added to the holonomic robot to obtain the fastest trajectory. by calculating the distance of the node that has not been passed from the initial position, if in the calculation the algorithm finds a shorter distance it will be stored as a new route replacing the previously recorded route. the distance traversed by the djikstra algorithm is 780 mm while a distance of 1100 mm obtains the other routes. The time for using the Djikstra method is proven to be 5.3 seconds faster than the track without the Djikstra method with the same speed. Uneven track terrain can result in a shift in the robot's position so that it can affect the travel data. The conclusion is that odometry and Dijkstra's algorithm as a planning system and finding the shortest path are very efficient for warehouse robots to deliver goods than ordinary line followers without Dijkstra, both in terms of distance and travel time.

Digital-Twin-Driven AGV Scheduling and Routing in Automated Container Terminals

Automated guided vehicle (AGV) scheduling and routing are critical factors affecting the operation efficiency and transportation cost of the automated container terminal (ACT). Searching for the optimal AGV scheduling and routing plan are effective and efficient ways to improve its efficiency and reduce its cost. However, uncertainties in the physical environment of ACT can make it challenging to determine the optimal scheduling and routing plan. This paper presents the digital-twin-driven AGV scheduling and routing framework, aiming to deal with uncertainties in ACT. By introducing the digital twin, uncertain factors can be detected and handled through the interaction and fusion of physical and virtual spaces. The improved artificial fish swarm algorithm Dijkstra (IAFSA-Dijkstra) is proposed for the optimal AGV scheduling and routing solution, which will be verified in the virtual space and further fed back to the real world to guide actual AGV transport. Then, a twin-data-driven conflict prediction method is proposed to predict potential conflicts by constantly comparing the differences between physical and virtual ACT. Further, a conflict resolution method based on the Yen algorithm is explored to resolve predicted conflicts and drive the evolution of the scheme. Case study examples show that the proposed method can effectively improve efficiency and reduce the cost of AGV scheduling and routing in ACT.

A REVIEW OF METHODOLOGIES FOR PATH PLANNING AND OPTIMIZATION OF MOBILE ROBOTS

This research paper provides a comprehensive review of methodologies for path planning and optimization of mobile robots. With the rapid development of robotics technology, path planning and optimization have become fundamental areas of research for achieving efficient and safe autonomous robot navigation. In this paper, we review the classic and state-of-the-art techniques of path planning and optimization, including artificial potential fields, A* algorithm, Dijkstra's algorithm, genetic algorithm, swarm intelligence, and machine learning-based methods. We analyze the strengths and weaknesses of each approach and discuss their application scenarios. Moreover, we identify the challenges and open problems in this field, such as dealing with dynamic environments and real-time constraints. This paper serves as a comprehensive reference for researchers and practitioners in the robotics community, providing insights into the latest trends and developments in path planning and optimization for mobile robots.

Study on tortuosity from 3D images of nuclear graphite grades IG-110 by Dijkstra's algorithm and fast marching algorithm

The nuclear-grade graphite is a kind of granular material, and tortuosity is a key parameter to predict its performance. However, there are few research about analyzing the tortuosity of nuclear graphite materials. In this study, two methods were employed to calculate the tortuosity of graphite grades IG-110. In the first method, the X-ray computed tomography (X-CT) images were transferred into binary ones using three different threshold segmentation algorithms separately, and then the tortuosity was calculated by the Dijkstra's algorithm. In the second method, the tortuosity was calculated by fast marching algorithm based on the grayscale images, in which some characters are intensified. The results indicate that the Dijkstra's algorithm and fast marching algorithm, which are frequently used for tortuosity in other fields, can also be applied to graphite grades IG-110. Overall, it is expected that this work may provide insight on the tortuosity analysis of similar particulate materials.

Algoritma Dijkstra dan Algoritma Greedy Untuk Optimasi Rute Pengiriman Barang Pada Kantor Pos Gorontalo

Delivery of goods is one of the services used in the online buying and selling process at the Post Office company. Companies need a performance in the distribution of goods that is more time-consuming and cost-efficient, so it requires determination in selecting the shortest path. Many algorithms have been used to help find the shortest path for distributing goods, including the Dijkstra Algorithm and the Greedy Algorithm. In this study, the application of the two algorithms is to find routes between sub-districts that are the destination of goods delivery, then compare to see which algorithm is more optimal. Based on the calculation results, the Dijkstra Algorithm can provide a more optimal solution with a total mileage obtained of 304.90 Km, while the Greedy Algorithm produces a total distance of 441.60 Km.

Development of the Gadingsari Village Tourism Geographic Information System Application

Gadingsari Village is a city located in Jogjakarta, has cultural diversity as an asset of regional wealth, and must be developed as optimally as possible. Many tourists visit tourist attractions in Gadingsari Village, but there are difficulties in choosing the tourist attractions visited due to limited information about tourist attractions in Gadingsari Village. The other problems are congestion problems, cost problems, time problems,s, and labor problems. The purpose of this study is to create a geographic information system for tourism in Gadingsari village. The system is built using PHP and MySQL programming languages as database management as well as the application of the Dijkstra algorithm used to find the shortest path based on the smallest weight from one point of departure to another. The tests carried out in this study used the User Acceptance Test, aiming to determine the user's understanding of the application created. The resulting geographic information system consists of several features, namely the information feature of tourist places, and the feature of finding the fastest route to the tourist spot. In the fastest route search feature, the Dijkstra algorithm works optimally so that it can quickly find the fastest route to tourist places in Gadingsari Village.

Visualising Path Finding Algorithms Application Development and Implementing

Abstract: The Dijkstra algorithm, A* Search, Greedy Best-first Search, Swarm Search, Breadth-first, and Depth-first Search are some of the common algorithms used today. The pathfinding algorithm is used in this study to provide an overview of algorithms and how they are implemented. The user will also learn more about how different algorithms and programming in general work. Knowing these tactics will give them a fundamental understanding of how to create different navigational tools. The visualizer is a grid page containing a "start node" and a "end node." In order to enhance the overall picture and better understand how these pathfinding algorithms deal with our everyday problems, the spectator can add new characteristics like a maze, walls, and weights. To build a visualizer, a programmer needs a solid grasp of front-end programming languages and pathfinding techniques.

Research on Optimization Method of Integrated Energy System Network Planning

The development of an integrated energy system (IES) is conducive to promoting the transformation of the energy system and helping to achieve the ‘double carbon’ goal in China. The IES integrates cooling, heating, electricity, gas, and other energy resources, which is significantly more difficult than single energy network planning. This paper systematically sorts out the process of IES network planning and proposes an improved methodology. Firstly, the bottom-up dynamic multiple-load forecasting method of 8760 h a year is proposed as the basis of system configuration and capacity selection. Subsequently, a planning method for energy station location and route optimization using the Dijkstra algorithm is constructed by applying the P-median optimization model. Finally, when optimizing the capacity allocation of the IES, the complementary characteristics of natural gas, electricity and heat, as well as the corresponding energy demand characteristic, have been fully considered, so that the optimization objectives can be reasonably determined. Through the actual calculation, it is found that the optimization method proposed in this paper can reduce the construction cost of the network by 41%. The work combines the process of energy network planning and capacity configuration of IES, which provides a simple, easy and economical solution for IES planning in new areas.

Sustainable hierarchical multi-modal hub network design problem: bi-objective formulations and solution algorithms

This paper presents a bi-objective model for the design and optimization of a sustainable hierarchical multi-modal hub network. The proposed model focuses on sustainability by considering economic, environmental, and social aspects of the decisions in a hierarchical network. A case of Turkish network for freight transportation is used to validate the proposed model. To solve the small-sized problems, the augmented epsilon constraint method version 2 (AUGMECON2) is applied. It can be inferred from the Pareto-optimal set obtained by AUGMECON2 that the effect of increasing the number of hubs after a threshold is marginal. The current contribution proposes two multi-objective genetic algorithms (NSGA-II and NRGA), which incorporate LP solving and Dijkstra algorithm. The results show the superiority of NRGA compared to NSGA-II in terms of solution time. Also, we present an alternative, more efficient formulation to the problem. Based on the alternative formulation, in addition to AUGMECON2, we use two exact methods, including Torabi and Hassini (TH) method and augmented weighted Tchebycheff procedure (AWTP), to find Pareto-optimal solutions for small, medium, and large-sized problems (including the case study). The performance of the proposed solution methods is measured using some multi-objective indicators. The results show the superiority of AUGMECON2.

Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks.

Fast convergence routing is a critical issue for Low Earth Orbit (LEO) constellation networks because these networks have dynamic topology changes, and transmission requirements can vary over time. However, most of the previous research has focused on the Open Shortest Path First (OSPF) routing algorithm, which is not well-suited to handle the frequent changes in the link state of the LEO satellite network. In this regard, we propose a Fast-Convergence Reinforcement Learning Satellite Routing Algorithm (FRL-SR) for LEO satellite networks, where the satellite can quickly obtain the network link status and adjust its routing strategy accordingly. In FRL-SR, each satellite node is considered an agent, and the agent selects the appropriate port for packet forwarding based on its routing policy. Whenever the satellite network state changes, the agent sends "hello" packets to the neighboring nodes to update their routing policy. Compared to traditional reinforcement learning algorithms, FRL-SR can perceive network information faster and converge faster. Additionally, FRL-SR can mask the dynamics of the satellite network topology and adaptively adjust the forwarding strategy based on the link state. The experimental results demonstrate that the proposed FRL-SR algorithm outperforms the Dijkstra algorithm in the performance of average delay, packet arriving ratio, and network load balance.

Application of Ant Colony Optimization Algorithm Based on Triangle Inequality Principle and Partition Method Strategy in Robot Path Planning

Path planning is an important area of mobile robot research, and the ant colony optimization algorithm is essential for analyzing path planning. However, the current ant colony optimization algorithm applied to the path planning of mobile robots still has some limitations, including early blind search, slow convergence speed, and more turns. To overcome these problems, an improved ant colony optimization algorithm is proposed in this paper. In the improved algorithm, we introduce the idea of triangle inequality and a pseudo-random state transfer strategy to enhance the guidance of target points and improve the search efficiency and quality of the algorithm. In addition, we propose a pheromone update strategy based on the partition method with upper and lower limits on the pheromone concentration. This can not only improve the global search capability and convergence speed of the algorithm but also avoid the premature and stagnation phenomenon of the algorithm during the search. To prevent the ants from getting into a deadlock state, we introduce a backtracking mechanism to enable the ants to explore the solution space better. Finally, to verify the effectiveness of the proposed algorithm, the algorithm is compared with 11 existing methods for solving the robot path planning problem, including several ACO variants and two commonly used algorithms (A* algorithm and Dijkstra algorithm), and the experimental results show that the improved ACO algorithm can plan paths with faster convergence, shorter path lengths, and higher smoothness. Specifically, the algorithm produces the shortest path length with a standard deviation of zero while ensuring the most rapid convergence and the highest smoothness in the case of the shortest path in four different grid environments. These experimental results demonstrate the effectiveness of the proposed algorithm in path planning.