Path Optimization Algorithm Research Articles

Research on Automatic Vertical Parking Path-Planning Algorithms for Narrow Parking Spaces

Narrow parking spaces pose difficulties in path planning and spot turning caused by sudden changes and discontinuities in path curvature. To address these problems, this paper investigates the performance of three path-planning algorithms and proposes a path-optimization algorithm. First, a narrow parking space is defined based on single-step parking using the arc-line combination parking algorithm. Second, to compare the arc-line combination algorithm, Hybrid A* algorithm, and particle swarm optimization parking algorithm with respect to different narrow parking spaces, a multi-objective evaluation function is proposed, including three evaluation indicators, namely, the path length, the number of positive and negative conversions of vehicle speed, and the smoothness of the path. Their performance is compared using a simulation conducted in MATLAB. With the same starting point and different parking space widths, the three algorithms are simulated to generate different planned paths. Then, the evaluation indices are obtained to compare the performance of the algorithms based on the multi-objective function, the values of which indicate the fitness of the algorithm in a narrow parking environment. The results show that the Hybrid A* algorithm is better than the others for narrow parking spaces. Third, to smooth the planning path, a path optimization algorithm based on the cubic B-spline curve and gradient descent is proposed. Finally, the results of a simulation conducted on the proposed algorithm and the Hybrid A* algorithm are provided: the average minimum curvature of the path was reduced by 0.005 m−1, and the path meets the requirements of the minimum turning radius constraint of the analyzed vehicle. The results show that the proposed algorithm can effectively eliminate the curvature mutation point and constrain the path curvature to meet the requirements of a smooth path.

An intelligent power grid emergency allocation technology considering secondary disaster and public opinion under typhoon disaster

Typhoon disaster may not only cause power outage physically, but also breed negative sentiments in public opinion and affect social stability. Therefore, an intelligent power grid allocation technology considering secondary disaster and public opinion under typhoon disaster is proposed. Firstly, an Extra-Tree (ET) method is used to predict the power outage in time sequence (e.g., landing period, 6 h later, and 12 h later) after typhoon lands. Secondly, a typhoon secondary disaster potential assessment model is established based on disaster intensity and geographical environment. Since power outages may also cause public opinion polarization, a public opinion analysis model is proposed to mine Micro-blog (similar to Twitter) tweet information and analyze the demand for power restoration. Then, according to the results of secondary disaster potential assessment and public opinion analysis, the allocation strategy optimization is described as a Mixed Integer Nonlinear Programming (MINLP) problem. It is solved by the Stud Genetic Algorithm (Stud GA) and A-star path optimization algorithm. Finally, Yangjiang city in China under Typhoon “Chaba” (2022) is selected as the study area to verify the effectiveness and feasibility. It shows the proposed method can respond to public electricity demand in time and eliminate public negative sentiments, while the repair process is not disturbed by secondary disasters.

Applying Intelligent Reflector Surfaces for Detecting Violent Expiratory Aerosol Cloud Using Terahertz Signals

The recent COVID-19 pandemic has driven researchers from different spectrum to develop novel solutions that can improve detection and understanding of SARS-CoV- 2 virus. In this article we propose the use of Intelligent Reflector Surface (IRS) and terahertz communication systems to detect violent expiratory aerosol cloud that are secreted from people. Our proposed approach makes use of future IRS infrastructure to extend beyond communication functionality by adding environmental scanning for aerosol clouds. Simulations have also been conducted to analyze the accuracy of aerosol cloud detection based on a signal scanning and path optimization algorithm. Utilizing IRS for detecting violent expiratory aerosol cloud can lead to new added value of telecommunication infrastructures for sensor monitoring data that can be used for public health.

Method of Achieving the Goal on a Graph Model with Two Quality Criteria

The features of the construction and application of graph models for solving applied problems are considered. A graph model with two quality criteria is proposed, on which the search for optimal paths between given graph vertices is performed. Each edge of the graph has a weighting factor that determines the number of time unitsrequired to pass this edge. Each vertex can be in one of two states: “open” or “locked”. Initially, all vertices are open, but their states may change in the process of problem solving. The search for a solution is limited by a given time. If during the movement along the chosen route the graph vertices become blocked, it is necessary to look for alternative ways to achieve the goal. A method for constructing a Pareto set from which admissible paths are selected is proposed. The notion of an admissible path on a graph is defined. A procedure for choosing a path from the Pareto set has been developed. Upon completion of the choice, the path is considered optimal for follo wing it from the initial vertex to the target. Situations that can occur in the process of choosing a path and passing along it are presented. Based on the selection procedure, an algorithm for finding optimal paths bet ween given vertices on a graph model has been developed.

Optimizing Cutting Sequences and Paths for Common-Edge Nested Parts

Different from the normal nesting algorithm, common-edge nesting reduces the spacing between parts, improves the utilization rate of the sheet, and shortens the processing time. Although common-edge nested parts may be useful in theory, they are not practical for large-scale production due to their cutting limitations. Existing research either did not consider all common cutting constraints or used graph theory methods for global common cutting path planning. However, due to cutting thermal effects and sheet deformation, it is difficult to use global common cutting path planning in actual processing. Therefore, this paper proposes a cutting sequence optimization algorithm for common-edge nested parts that consider all cutting constraints and proposes an algorithm for generating and optimizing common cutting paths. Experiments show that the common cutting path optimization algorithm proposed in this paper can reduce about 70% of the air path compared with commercial software. Furthermore, comparison by optical microscopy shows that the quality of the actual machined part is not inferior to that of the part machined according to the cut path generated by the commercial software.

Green Energy Consumption Path Selection and Optimization Algorithms in the Era of Low Carbon and Environmental Protection Digital Trade

In order to better study the chosen path of the consumption model of public green energy and more accurately predict consumers’ green energy consumer behavior, we take new energy vehicles as an example to explore the driving mechanism and internal mechanism of the public green energy consumption model from the perspective of motivation. We propose an ensemble learning model based on a stacking strategy. The model uses XGBoost, random forest and gradient lifting decision trees as primary learners to transform features, and uses logistic regression as a meta-learner to predict users’ consumer behavior. The experimental results show that this feature engineering method can significantly improve the accuracy rate in multiple model algorithms, and the prediction effect of the ensemble learning model is better than that of a single model, with the accuracy rate of 82.81%. In conclusion, the ensemble learning model based on a stacking strategy can effectively predict the public’s consumer behavior. This provides a theoretical basis and policy recommendations for promoting green energy products represented by new energy vehicles, thereby improving the practical path for proposing green energy consumption.

A Review of Path Planning for Unmanned Surface Vehicles

With the continued development of artificial intelligence technology, unmanned surface vehicles (USVs) have attracted the attention of countless domestic and international specialists and academics. In particular, path planning is a core technique for the autonomy and intelligence process of USVs. The current literature reviews on USV path planning focus on the latest global and local path optimization algorithms. Almost all algorithms are optimized by concerning metrics such as path length, smoothness, and convergence speed. However, they also simulate environmental conditions at sea and do not consider the effects of sea factors, such as wind, waves, and currents. Therefore, this paper reviews the current algorithms and latest research results of USV path planning in terms of global path planning, local path planning, hazard avoidance with an approximate response, and path planning under clustering. Then, by classifying USV path planning, the advantages and disadvantages of different research methods and the entry points for improving various algorithms are summarized. Among them, the papers which use kinematic and dynamical equations to consider the ship’s trajectory motion planning for actual sea environments are reviewed. Faced with multiple moving obstacles, the literature related to multi-objective task assignment methods for path planning of USV swarms is reviewed. Therefore, the main contribution of this work is that it broadens the horizon of USV path planning and proposes future directions and research priorities for USV path planning based on existing technologies and trends.

Diffusion Nonlinear Estimation and Distributed UAV Path Optimization for Target Tracking with Intermittent Measurements and Unknown Cross-Correlations

This paper focuses on distributed state estimation (DSE) and unmanned aerial vehicle (UAV) path optimization for target tracking. First, a diffusion cubature Kalman filter with intermittent measurements based on covariance intersection (DCKFI-CI) is proposed, to address state estimation with the existence of detection failure and unknown cross-correlations in the network. Furthermore, an alternative transformation of DCKFI-CI based on the information form is developed utilizing a pseudo measurement matrix. The performance of the proposed DSE algorithm is analyzed using the consistency and the bounded error covariance of the estimate. Additionally, the condition of the bounded error covariance is derived. In order to further improve the tracking performance, a UAV path optimization algorithm is developed by minimizing the sum of the trace of fused error covariance, based on the distributed optimization method. Finally, simulations were conducted to verify the effectiveness of the proposed algorithm.

Arctic Route Planning and Navigation Strategy: The Perspective of Ship Fuel Costs and Carbon Emissions

Arctic navigation research will be more urgent as it is predicted that 4.7% of the shipping trade will be deployed in the Arctic region by 2030. This paper presents a multi-objective path optimization algorithm based on the field theory to investigate Arctic shipping route design and navigation strategies that take safety, economy and environmental protection as factors. The algorithm considers the ice conditions in Arctic waters throughout the year, the energy consumption of ships with different ice classes and carbon emissions. The sea environment is initially modeled using the potential field method, incorporating the ice condition field, fuel consumption, emission field and directional field. The navigable area is defined based on the ship navigability, and path optimization is conducted using the principle of gradient descent between the departure point and destination. The experimental results show that the cost of carbon emissions and fuel consumption varies for each class of ship on the planned path in different months. Therefore, by combining different classes of ships and months, we obtain an Arctic navigation strategy with a consideration of the best cost and environmental protection for each month. The path planning and navigation strategy decision method proposed in this paper can provide a reference for an Arctic route design, navigation month and vessel selection, which is beneficial to the sustainable development of Arctic shipping.

Study of Optimization of Tourists' Travel Paths by Several Algorithms

The purpose of this paper is to optimize the tourism path to make the distance shorter. The article first constructed a model for tourism route planning and then used particle swarm optimization (PSO), genetic algorithm (GA), and ant colony algorithms to solve the model separately. Finally, a simulation experiment was conducted on tourist attractions in the suburbs of Taiyuan City to compare the path optimization performance of the three algorithms. The three path optimization algorithms all converged during the process of finding the optimal path. Among them, the ant colony algorithm exhibited the fastest and most stable convergence, resulting in the smallest model fitness value. The travel route obtained through the ant colony algorithm had the shortest distance, and this algorithm required minimal time for optimization. The novelty of this article lies in the enumeration and description of various algorithms used for optimizing travel paths, as well as the comparison of three different travel route optimization algorithms through simulation experiments. Doi: 10.28991/HIJ-2023-04-02-012 Full Text: PDF

Cooperative Navigation for Heterogeneous Air-Ground Vehicles Based on Interoperation Strategy

This paper focuses on the cooperative navigation of heterogeneous air-ground vehicle formations in a Global Navigation Satellite System (GNSS) challenged environment and proposes a cooperative navigation method based on motion estimation and a regionally optimal path planning strategy. In air-ground vehicle formations, unmanned ground vehicles (UGVs) are equipped with low-precision inertial navigation measurement units and wireless range sensors, which interact with unmanned aerial vehicles (UAVs) equipped with high-precision navigation equipment for cooperative measurement information and use the UAVs as aerial benchmarks for cooperative navigation. Firstly, the Interacting Multiple Model (IMM) algorithm is used to predict the next moment’s motion position of the UGVs. Then regional real-time path optimization algorithms are introduced to design the motion position of the high-precision UAVs so as to improve the formation’s configuration and reduce the geometric dilution of precision (GDOP) of the configuration. Simulation results show that the Dynamic Optimal Configuration Cooperative Navigation (DOC-CN) algorithm can reduce the GDOP of heterogeneous air-ground vehicle formations and effectively improve the overall navigation accuracy of the whole formation. The method is suitable for the cooperative navigation environment of heterogeneous air-ground vehicle formations under GNSS-challenged conditions.

An effective approach for non-singular trajectory generation of a 5-DOF hybrid machining robot

By taking a newly developed 5-DOF (degree of freedom) hybrid robot as an exemplar, this paper presents a novel and effective approach for non-singular tool trajectory generation. Based upon singularity analysis via inverse kinematics, an algorithm for the C-axis path optimization is developed using the weighted S-curve. Incorporated singular domain detection with the C-axis angle correction, the algorithm can easily be programmed and embedded into the CNC (computer numerical control) system as a postprocessing module and works in real-time. Then, an offline feedrate scheduling method is proposed by considering the drive and geometric accuracy constraints, allowing the rapid yet smooth movement in the neighborhood of singularity to be achieved. Additionally, it is found that the orientation accuracy can be improved by setting adequate cone angle and feedrate value. The results of both simulations and experiments on a prototype machine show that the C-axis can follow the non-singular tool trajectory well with relatively high rotary speed to pass through the singular domain, thereby verifying the effectiveness of the proposed approach.

A Sampling-Based Distributed Exploration Method for UAV Cluster in Unknown Environments

Rapidly completing the exploration and construction of unknown environments is an important task of a UAV cluster. However, the formulation of an online autonomous exploration strategy based on a real-time detection map is still a problem that needs to be discussed and optimized. In this paper, we propose a distributed unknown environment exploration framework for a UAV cluster that comprehensively considers the path and terminal state gain, which is called the Distributed Next-Best-Path and Terminal (DNBPT) method. This method calculates the gain by comprehensively calculating the new exploration grid brought by the exploration path and the guidance of the terminal state to the unexplored area to guide the UAV’s next decision. We propose a suitable multistep selective sampling method and an improved Discrete Binary Particle Swarm Optimization algorithm for path optimization. The simulation results show that the DNBPT can realize rapid exploration under high coverage conditions in multiple scenes.

Characteristics and Optimization Strategies of A* Algorithm and Ant Colony Optimization in Global Path Planning Algorithm

A* algorithm and ant colony optimization (ACO) are more widely used in path planning among global path planning algorithms. The optimization process is analyzed and summarized from the principles and characteristics of the two algorithms, A* algorithm is mainly optimized in terms of point selection and improvement of heuristic function; and ACO is mainly investigated in terms of transfer probability and pheromone positive feedback for improvement and optimization. Taking a single algorithm solving complex optimization problems difficulties into consideration, a splitting strategy can be used. So that local path or intelligent path optimization algorithms are incorporated in global path planning to improve search efficiency and optimization quality.

A Toolpath Optimization Algorithm for Layered 3D Printings based on Solving the TSP

In order to optimize the tool path of 3D printing such that the efficiency is improved, by summerizing the pros and cons of existing methods, we proposed a noval tool path optimization algorithm for layered 3D printings based on solving the Traveling Salesman Problem. Our algorithm first adjusts the major printing direction using Principal Components Analysis, and then applies the greedy strategy and generates multiple printing paths by interleavingly appending the filling segments and contour segments along the major printing direction. Thereafter, by considering the multiple printing paths as “cities”, and elaborately defining the distances between them, we successfully model the problem of minimizing the flying distances as a kind of Traveling Salesman Problem. Af-ter converting the accurate solution of TSP to our problem, we are able to get the global optimized tool path with the given multiple printing paths. Experiments demonstrate that the proposed algorithm not only miminizes the flying distances, but also reduces the switches between printing status and flying status, and boosts the percentage of long segments in the whole tool path as well, thus the printing efficiency is significently imporved without sac-rifying the printing quality.

Multi-UAV WRSN charging path planning based on improved heed and IA-DRL

Aiming at the problem of how to improve the energy utilization rate of UAV (Unmanned Aerial Vehicle) in the charging process of wireless rechargeable sensor network, a charging path planning scheme for multi-UAV wireless rechargeable sensor network based on deep reinforcement learning is proposed. Firstly, the multi-UAV path planning problem is described and a network model is established, and then the network model is optimized by using the improved dynamic clustering algorithm of HEED, and then an intelligent path optimization algorithm based on the intelligent algorithm and deep reinforcement learning IA-DRL is proposed for the problem model. According to this algorithm, the optimal charging path of multiple drones is obtained, and finally the drones charge each node to be charged in the network. The experimental results show, compared with other traditional heuristic methods, IA-DRL has more advantages in solving small and medium-scale UAV path planning problems, and compared with the neural network model without PSO, the minimum AVG performance of IA-DRL is improved by about 3.8% and has a faster convergence speed about 550 episodes.

Emergency Escape Route Design and Application

This paper uses k-means clustering algorithm, single objective optimization model and multisource multi-sink problem shortest path algorithm to obtain the optimal path algorithm for emergency evacuation and the shortest time model.Firstly, a code of conduct for the division of terrorist attacks has been established. The number of casualties in various incidents and the scope of destructive power to public areas are counted. A three-dimensional cluster core is selected. All terrorist at- tacks are classified into three levels: level 1-3 by the k-means clustering algorithm, and the degree of harm is gradually increasing.Secondly, the optimal path from each venue to the stairway is calculated. The re- lationship between the speed of flow and the number of evacuees is given in the case of three situations. The multi-source multi-sink problem is transformed into a single- source shortest path problem by adding a virtual node to all venues on the second floor of the Louvre, and the Dijkstra algorithm is called once to obtain the optimal path from the venue to all stairs.The Final step is to establish an emergency evacuation optimization model with the minimum evacuation time as the objective function. The overall evacuation time is divided into the personnel response time, the horizontal road walking time, the time down the stairs, and the waiting time when congestion occurs. the open number of emergency exits can be judging by different levels of terrorist events, and the optimal evacuation path and the shortest time for different emergency events are given. At the same time, we also analyzed the changing trend of velocity in different cases, which can be adjusted according to the actual conditions.

Computation of conformational transitions and activation of Src tyrosine kinase.

A protein molecule can adapt different structural forms depending on solution conditions or ligation state. The stable forms can be defined experimentally, but the transition pathways and metastable states between them cannot. A computational method to explore equilibrium conformational transition pathways is the adaptive bias path optimization (ABPO) algorithm. ABPO, implemented in the CHARMM program, differs from other path methods by using enhanced sampling along a path between two known states without restraining the system to the reduced coordinates of the evolving path. ABPO converges to a physically reasonable path even in the case of a highly rugged energy landscape and is amenable to assessing the power of the reduced variables chosen to explore the transition. ABPO was applied to three cases where a conformational transition is localized to one part of the polypeptide chain. With proper selection of the reduced coordinate set, sampling without restraints converged to a physically reasonable path. We also applied ABPO to a more complex transition of Src tyrosine kinase catalytic domain that undergoes a more complex transition between enzymatically down‑regulated and activated conformations. The results using coarse-grained and all‑atom models identified a switched electrostatic network (SEN) that could confer an entropic advantage to reduce energetic barriers by limiting the search space of the activation loop. The richness in atomic detail provided by the ABPO path reveals how the SEN couples the dynamics of the key αC‑helix and the activation loop, which provided a new rationale for the highly conserved HRD sequence of kinases. Further, the results related to energetics of the rotation of the αC‑helix provide a rationale for the interactions observed in a number of kinase regulatory complexes, some for which the basis for regulation from inspection of the structure along is puzzling.

Retracted: Logistics Distribution Path Optimization Algorithm Based on Intelligent Management System.

[This retracts the article DOI: 10.1155/2022/3699990.].

Research on off-road path optimization algorithm based on Bekker theory improved genetic algorithm

Research on off-road path optimization algorithm based on Bekker theory improved genetic algorithm