Optimal Path Search Research Articles

GIS-Data-Driven Efficient and Safe Path Planning for Autonomous Ships in Maritime Transportation

Maritime transportation is vital to the global economy. With the increased operating and labor costs of maritime transportation, autonomous shipping has attracted much attention in both industry and academia. Autonomous shipping can not only reduce the marine accidents caused by human factors but also save labor costs. Path planning is one of the key technologies to enable the autonomy of ships. However, mainstream ship path planning focuses on searching for the shortest path and controlling the vehicle in order to track it. Such path planning methods may lead to a dynamically infeasible trajectory that fails to avoid obstacles or reduces fuel efficiency. This paper presents a data-driven, efficient, and safe path planning (ESP) method that considers ship dynamics to provide a real-time optimal trajectory generation. The optimization objectives include fuel consumption and trajectory smoothness. Furthermore, ESP is capable of fast replanning when encountering obstacles. ESP consists of three components: (1) A path search method that finds an optimal search path with the minimum number of sharp turns from the geographic data collected by the geographic information system (GIS); (2) a minimum-snap trajectory optimization formulation with dynamic ship constraints to provide a smooth and collision-free trajectory with minimal fuel consumption; (3) a local trajectory replanner based on B-spline to avoid unexpected obstacles in real time. We evaluate the performance of ESP by data-driven simulations. The geographical data have been collected and updated from GIS. The results show that ESP can plan a global trajectory with safety, minimal turning points, and minimal fuel consumption based on the maritime information provided by nautical charts. With the long-range perception of onboard radars, the ship can avoid unexpected obstacles in real time on the planned global course.

Optimal search path planning of UUV in battlefeld ambush scene

Aiming at the practical application of Unmanned Underwater Vehicle (UUV) in underwater combat, this paper proposes a battlefield ambush scene with UUV considering ocean current. Firstly, by establishing these mathematical models of ocean current environment, target movement, and sonar detection, the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively. Then, based on the Hybrid Quantum-behaved Particle Swarm Optimization (HQPSO) algorithm, the path with the highest target search probability is found. Finally, through simulation calculations, the influence of different UUV parameters and target parameters on the target search probability is analyzed, and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated, and the optimal search path scheme is obtained. The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.

Improving Accuracy and Transferability of Machine Learning Chemical Activation Energies by Adding Electronic Structure Information.

Predicting chemical activation energies is one of the longstanding and important challenges in computational chemistry. Recent advances have shown that machine learning can be used to create tools to predict them. Such tools can significantly decrease the computational cost for these predictions compared to traditional methods, which require an optimal path search along a high-dimensional potential energy surface. To enable this new route, we need both large and accurate datasets and a compact yet complete description of the reactions. Although data for chemical reactions is becoming increasingly available, the key step of encoding the reaction as an efficient descriptor remains a big challenge. In this paper, we demonstrate that including electronic energy levels in the description of the reaction significantly improves the prediction accuracy and transferability. Feature importance analysis further demonstrates that electronic energy levels have a higher importance than some structural information and typically require less space in the reaction encoding vector. In general, we observe that the results of the feature importance analysis relate well to the domain knowledge of fundamental chemical principles. This work can help to build better chemical reaction encodings for machine learning and thus improve the predictions of machine learning models for reaction activation energies. These models could ultimately be used to recognize reaction limiting steps in large reaction systems, allowing to account for bottlenecks at the design stage.

Receding Horizon-Based Infotaxis With Random Sampling for Source Search and Estimation in Complex Environments

This article proposes a receding horizon-based information-theoretic source search and estimation strategy for a mobile sensor in an urban environment in which an invisible harmful substance is released into the atmosphere. The mobile sensor estimates the source term including its location and release rate by using sensor observations based on Bayesian inference. The sampling-based sequential Monte Carlo method, particle filter, is employed to estimate the source term state in a highly nonlinear and stochastic system. Infotaxis, the information-theoretic gradient-free search strategy is modified to find the optimal search path that maximizes the reduction of the entropy of the source term distribution. In particular, receding horizon Infotaxis (RHI) is introduced to avoid falling into the local optima and to find more successful information gathering paths in obstacle-rich urban environments. Besides, a random sampling method is introduced to reduce the computational load of the RHI for real-time computation. The random sampling method samples the predicted future measurements based on current estimation of the source term and computes the optimal search path using sampled measurements rather than considering all possible future measurements. To demonstrate the benefit of the proposed approach, comprehensive numerical simulations are performed for various conditions. The proposed algorithm increases the success rate by about 30% and reduces the mean search time by about 40% compared with the existing information-theoretic search strategy.

A Sampling-Based Algorithm with the Metropolis Acceptance Criterion for Robot Motion Planning

Motion planning is one of the important research topics of robotics. As an improvement of Rapidly exploring Random Tree (RRT), the RRT* motion planning algorithm is widely used because of its asymptotic optimality. However, the running time of RRT* increases rapidly with the number of potential path vertices, resulting in slow convergence or even an inability to converge, which seriously reduces the performance and practical value of RRT*. To solve this issue, this paper proposes a two-phase motion planning algorithm named Metropolis RRT* (M-RRT*) based on the Metropolis acceptance criterion. First, to efficiently obtain the initial path and start the optimal path search phase earlier, an asymptotic vertex acceptance criterion is defined in the initial path estimation phase of M-RRT*. Second, to improve the convergence rate of the algorithm, a nonlinear dynamic vertex acceptance criterion is defined in the optimal path search phase, which preferentially accepts vertices that may improve the current path. The effectiveness of M-RRT* is verified by comparing it with existing algorithms through the simulation results in three test environments.

Optimizing Searchers that can Transport and Deploy another Searcher Using an Agent Based Model and Nonlinear Optimization Methods in a Maritime Domain

This paper introduces a new methodology, nicknamed Pathfinder, for finding optimal search paths for searchers that can transport and deploy other searchers. The methodology applies an Agent-Based Model to model target movement, then uses nonlinear optimization methods to find optimal search plans. This methodology can optimize these search teams effectively and quickly. Pathfinder significantly increase probability of detection and decreases travel distance. In addition to advancing Search Theory, this methodology also has the potential to enhance current search and rescue (SAR) and anti-submarine warfare (ASW) operations.

Sistem Pendukung Keputusan Cerdas Menggunakan Metode Ant Colont Optimization (ACO) untuk Pencarian Jalur Optimum Rantai Pasok Bioenergi Berbasis Kelapa Sawit

The concept of supply chain management in business activities is very necessary. This concept involves more than one type of company. One of the businesses that requires this concept is palm oil-based bioenergy management. The optimal path in the supply chain network is very difficult, because the product supply chain network is perishable. For that we need an intelligent decision support system model in the search for the optimal path. The purpose of this study is to determine the optimum value and to find the optimum path of the palm oil-based bioenergy supply chain. The method used in the development of the model is the ant colony optimization method, namely the shortest path method. The data processed is the distance and time of supply. The optimal path search result is 0.035. So that this research becomes a reference in determining the optimal path.

Research on Security Anomaly Detection for Big Data Platforms Based on Quantum Optimization Clustering

Due to the explosive growth of data in the Internet, more and more applications are being deployed on Big Data platforms. However, as the scale of data continues to increase, the probability of anomalies in the platform is also increasing. However, traditional anomaly detection techniques cannot effectively handle the massive amount of historical data and can hardly meet the security requirements of big data platforms. In order to solve the above problems, this paper proposes a security anomaly detection method for big data platforms based on quantum optimization clustering. Firstly, a framework of big data platform anomaly detection system is designed based on distributed software architecture through Hadoop and Spark big data open source technology. The system achieves effective detection of network anomalies by collecting and analyzing big data platform server log data. Secondly, an offline anomaly detection algorithm based on quantum ant colony optimized affinity propagation clustering is designed for various anomalies mined from historical data. The bias parameters of the affinity propagation clustering are treated as individual ants to construct an ant colony, and the clustering accuracy is set as fitness. Finally, in order to improve the accuracy of the optimal path search of the ant colony, quantum bit encoding is applied to the ant colony position to refine the granularity of the individual ant colony position update. The experimental results show that the proposed method can effectively complete the anomaly clustering detection of massive data. With a reasonable threshold, the quantum ant colony–based affinity propagation clustering has high detection accuracy.

Optimizing the Path of Plug Tray Seedling Transplanting by Using the Improved A* Algorithm

In greenhouse nurseries, one of the important tasks of the automatic transplanter is replanting missing or bad seedling holes with healthy seedlings. This requires the transplanter to spend significant time moving between the supply trays and target trays during replanting. The diversity and complexity of the transplanting routes affect transplanter efficiency. Path planning method can find a better path for the manipulator and improve the efficiency of transplantation. The A* algorithm (A*), which is one of the optimal path search algorithms, is often used in practical applications of path planning. In this paper, the heuristic function of the A* is optimized by the ant colony algorithm (ACA), and an improved A* algorithm (Imp-A*) is obtained. Simulation tests and transplanting trials of Imp-A*, A*, ACA, Dijkstra (DA), and common sequence method (CSM) were carried out using 32-, 50-, 72-, and 128-hole plug trays. The results show that Imp-A* inherits the advantages of A* and ACA in terms of path planning length and computation time. Compared to A*, ACA, DA, and CSM, the transplanting time for Imp-A* was reduced by 2.4%, 12.84%, 11.63%, and 14.27%, respectively. In just six trays of transplanting tasks, Imp-A* saves 60.91 s compared to CSM, with an average time saving of 10.15 s per tray. The combination optimization algorithm has similar application prospects in agriculture.

Ant colony optimization for path planning in search and rescue operations

In search and rescue operations, an efficient search path, colloquially understood as a path maximizing the probability of finding survivors, is more than a path planning problem. Maximizing the objective adequately, i.e., quickly enough and with sufficient realism, can have substantial positive impact in terms of human lives saved. In this paper, we address the problem of efficiently optimizing search paths in the context of the NP-hard optimal search path problem with visibility, based on search theory. To that end, we evaluate and develop ant colony optimization algorithm variants where the goal is to maximize the probability of finding a moving search object with Markovian motion, given a finite time horizon and finite resources (scans) to allocate to visible regions. Our empirical results, based on evaluating 96 variants of the metaheuristic with standard components tailored to the problem and using realistic size search environments, provide valuable insights regarding the best algorithm configurations. Furthermore, our best variants compare favorably, especially on the larger and more realistic instances, with a standard greedy heuristic and a state-of-the-art mixed-integer linear program solver. With this research, we add to the empirical body of evidence on an ant colony optimization algorithms configuration and applications, and pave the way to the implementation of search path optimization in operational decision support systems for search and rescue.

Optimal Search for Underwater Targets for Ships Equipped with Hull Mounted Sonar Using Genetic Algorithm

When a surface ship utilizes a hull-mounted sonar (HMS) to search for a target located in the water at sea, the maneuver path of the surface ship depends on the maximum detection range of the sonar. Because sonar utilizes sound waves, it is significantly influenced by the marine environment and the detection probability depends on the location and sound wave incident angle of the target. This study aims to calculate the detection probability by applying the actual marine environment, and proposes an optimal search path calculation method that maximizes the detection probability based on a genetic algorithm. The proposed optimal search path calculation method was proven to be effective via simulations in determining the optimal search.

A Sampling-Based Unfixed Orientation Search Method for Dual Manipulator Cooperative Manufacturing.

The case of dual manipulators with shared workspace, asynchronous manufacturing tasks, and independent objects is named a dual manipulator cooperative manufacturing system, which requires collision-free path planning as a vital issue in terms of safety and efficiency. This paper combines the mathematical modeling method with the time sampling method in the classification of robot path-planning algorithms. Through this attempt we can achieve an optimal local search path during each sampling period interval. Our strategy is to build the corresponding non-linear optimization functions set based on the motion characteristics of the dual manipulator system. In this way, the path-planning problem can be turned into a purely mathematical problem of solving the non-linear optimization programming equations set. The spatial geometric analysis is used to linearize the predicted dual-manipulator minimum distance equation, thus linearizing the non-linear optimization equations set. Finally, this system of linear optimization equations will be mapped directly into a virtual Euclidean space and then solved intuitively using the spatial geometry theory. By simulation and comparing with the previous strategies, we find that the planning results of the newly proposed planning strategy are smoother and have shorter deviations as well as a higher algorithmic efficiency in terms of spatial geometric properties.

Full-field strain measurement using optimal path search method with error control

Digital image correlation (DIC) is one of the main means of optical measurement of the strain field. However, there has been little discussion on the calculation errors at positions with abrupt change. This study seeks to address how to reduce the full-field strain calculation errors and improve the strain accuracy. An optimal path search method (OPSM) with error control for a full-field strain solution is proposed by optimizing the algorithm of the traditional DIC method, using the material displacement field with abrupt change. The strain calculation considering the calculation zone boundary, hole, and shock wave front is discussed. A comparison of the results using the proposed method with the theoretical results shows that this method can effectively obtain full-field strain information, especially at positions with abrupt change in displacement. The application of OPSM to the cellular structure in a quasi-static compression experiment again verifies the validity. This method not only is comparable with the original method in the noise suppression effect but also ensures the approximation of the results to the true values in the displacement zone with abrupt change.

Profitability of Prosumers According to Various Business Models—An Analysis in the Light of the COVID-19 Effect

The European Union has set itself ambitious emission-reduction targets—becoming carbon neutral by 2050. The member states’ energy systems are increasingly switching to renewable and decentralized configurations, in line with the EU guidelines. This is accompanied by the support for “prosumers”—entities that both consume and generate renewable energy. In parallel, a number of prosumer support schemes are emerging as a result of the search for optimal development paths. At the same time, the COVID-19 pandemic has significantly affected the power market, causing huge anomalies mainly in demand and billing. The objective of the study was to assess the impact of the proposed changes in the prosumer schemes on the level of benefits prosumers obtained, including the performance of sensitivity analyses reflected in different levels and stability of electricity consumption resulting from extreme situations caused by the COVID-19 pandemic. Numerical computer simulations for five predefined prosumer support schemes were carried out in the R-project environment. The basic conclusion is that the prosumer benefits most from participating in the applicable discount mechanism, in which the unused energy is fed into a network storage, from which the prosumer can take 70% or 80% of the stored volume. The research also allows us to conclude that in Poland the COVID-19 pandemic has had a very significant impact on the level and profile of energy demand due to the introduction of restrictions on selected areas and economic sectors. The reduced demand is particularly visible in services (−58%), tourism and sport (−39%), and education (−19%). The analysis is an important contribution to the search for an optimal model of prosumer market development in Europe.

A Node Localization Algorithm for Wireless Sensor Networks Based on Virtual Partition and Distance Correction

The coordinates of nodes are very important in the application of wireless sensor networks (WSN). The range-free localization algorithm is the best method to obtain the coordinates of sensor nodes at present. Range-free localization algorithm can be divided into two stages: distance estimation and coordinate calculation. For reduce the error in the distance estimation stage, a node localization algorithm for WSN based on virtual partition and distance correction (VP-DC) is proposed in this paper. In the distance estimation stage, firstly, the distance of each hop on the shortest communication path between the unknown node and the beacon node is calculated with the employment of virtual partition algorithm; then, the length of the shortest communication path is obtained by summing the distance of each hop; finally, the unknown distance between nodes is obtained according to the optimal path search algorithm and the distance correction formula. This paper innovative proposes the virtual partition algorithm and the optimal path search algorithm, which effectively avoids the distance estimation error caused by hop number and hop distance, and improves the localization accuracy of unknown nodes.

A global neural network learning machine: Coupled integer and fractional calculus operator with an adaptive learning scheme

Find the global optimal solution of the model is one promising research topic in computational intelligent community. Dependent on analogies to natural processes, the evolutionary swarm intelligent algorithms are widely used for solving global optimization problems which directed by the fitness values. In this paper, we propose one efficient fractional global learning machine (Fragmachine) which includes two stages (descending and ascending) to determine the optimal search path. The neural network model is used to approach the given fitness value. Specifically, for the descending stage, the integer gradient of the network output with respect the current location is employed to find the next descending point, while for the ascending stage, the fractional gradient is implemented to climb and escape from the local optimal point. We further propose one adaptive learning rate during training which relies on both the current gradient (integer or fractional) information and the fitness value. Finally, a series of numerical experiments verify the effectiveness of the proposed algorithm, Fragmachine.

Research on dynamic route selection method of D2D communication under multi hop transmission

In order to find the best path in wireless sensor networks, a dynamic energy-saving routing strategy based on multi hop transmission is proposed. The dynamic state transition optimization rules are designed to increase the search probability of new nodes reasonably, so as to find the global optimal solution quickly and effectively, so as to save the search time, increase the optimal path search probability and prolong the network survival time. Simulation results and analysis show that the dynamic route selection method of D2D communication under multi hop transmission greatly increases the search probability of the global optimal solution, achieves the global optimal solution quickly and effectively, saves the energy consumption of nodes, and is conducive to extending the network survival time.

Research on Optimal Path Selection Based on WebGIS

With the continuous development of geographic information system technology, the ability to analyze and process various spatial information has been greatly enriched and strengthened. The optimal path analysis is the current research hotspot in spatial analysis. In this paper, Changsha City in Hunan Province was taken as an example to build an optimal path selection platform based on WebGIS. With the help of ArcGIS network analysis tool, network analysis data set was established for vector road data, and road length was selected as impedance factor to realize optimal path search.

Automatic Detection of Fractures Based on Optimal Path Search in Well Logging Images

Reservoir fractures are essential locations to gather oil and gas. Recently, imaging logging technology has become a mainstream method for obtaining stratigraphic information. This paper proposed a combined optimal path search strategy to effectively identify and extract the fracture information in well logging images. Specifically, the threshold segmentation of logging images is used to obtain the binary image. In addition, the identification of connected fractures in the logging image is transformed into the optimal path search, and the identification and extraction of reservoir fractures are realized by constructing the optimal path between the two ends of fractures. Finally, an improved ant colony algorithm is applied to filter irrelevant information and extract fractures automatically by recording all the ants’ exploration trajectories in the ant colony. Compared with previous approaches, the proposed method can eliminate irrelevant background features and merely reserve pixels corresponding to fractures. Simultaneously, relative to the conventional strategy, the time consumption is reduced by more than 98%. The findings of this study can help for better extracting fractures automatically and reducing manual workload.

Dynamic Metric Accelerated Method for Fuzzy Clustering

Features in data samples usually need a unified dimension by a standardization process before clustering. However, there still exists a nonstandardized metric in which the distance between samples is greater than 1 after features are standardized. It is difficult to find the optimal search path if the data sample metrics are not standardized. To address this problem, we develop a dynamic-metric accelerated method for fuzzy clustering by introducing a metric matrix, whose diagonal elements consist of infinite norms of the metric matrix into the Fuzzy C-Means (FCM) clustering algorithm and its derived algorithms. More specifically, we focus on constructing a dynamic metric matrix that is used to unify the metric between data samples and updating cluster centers to optimize the search path of the cluster center. In addition, we propose a new evaluation index named the Coefficient of Variation Metric (CVM) to evaluate metric effectiveness. The dynamic metric accelerated method, whose complexity remains unchanged, can effectively accelerate the iteration speed of fuzzy clustering. The comparisons between the algorithm using the dynamic metric accelerated method and the corresponding algorithm on UCI, business district and COVID-19 CT image datasets show the superiority of the dynamic metric accelerated method in accelerating effect and clustering performance.