Vehicle Path Research Articles

Design of low-carbon planning model for vehicle path based on adaptive multi-strategy ant colony optimization algorithm

In contemporary transportation systems, the imperatives of route planning and optimization have become increasingly critical due to vehicles’ burgeoning number and complexity. This includes various vehicle types, such as electric and autonomous vehicles, each with specific needs. Additionally, varying speeds and operational requirements further complicate the process, demanding more sophisticated planning solutions. These systems frequently confront myriad challenges, including traffic congestion, intricate routes, and substantial energy consumption, which collectively undermine transportation efficiency, escalate energy usage, and contribute to environmental pollution. Hence, strategically planning and optimizing routes within complex traffic milieus are paramount to enhancing transportation efficacy and achieving low-carbon and environmentally sustainable objectives. This article proposes a vehicle path low-carbon planning model, Adaptive Cooperative Graph Neural Network (ACGNN), predicated on an adaptive multi-strategy ant colony optimization algorithm, addressing the vehicle path low-carbon planning conundrum. The proposed framework initially employs graph data from road networks and historical trajectories as model inputs, generating high-quality graph data through subgraph screening. Subsequently, a graph neural network (GNN) is utilized to optimize nodes and edges computationally. At the same time, the global search capability of the model is augmented via an ant colony optimization algorithm to ascertain the final optimized path. Experimental results demonstrate that ACGNN yields significant path planning outcomes on both public and custom-built datasets, surpassing the traditional Dijkstra’s shortest path algorithm, random graph network (RGN), and conventional GNN methodologies. Moreover, comparative analyses of various optimization methods on the custom-built dataset reveal that the ant colony optimization algorithm markedly outperforms the simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO). The method offers an innovative technical approach to vehicle path planning and is instrumental in advancing low-carbon and environmentally sustainable goals while enhancing transportation efficiency.

Large-Area Coverage Path Planning Method Based on Vehicle–UAV Collaboration

With the widespread application of unmanned aerial vehicles (UAV) in surveying, disaster search and rescue, agricultural spraying, war reconnaissance, and other fields, coverage path planning is one of the most important problems to be explored. In this paper, a large-area coverage path planning (CCP) method based on vehicle–UAV collaboration is proposed. The core idea of the proposed method is adopting a divide-and conquer-strategy to divide a large area into small areas, and then completing efficient coverage scanning tasks through the collaborative cooperation of vehicles and UAVs. The supply points are generated and adjusted based on the construction of regular hexagons and a Voronoi diagram, and the segmentation and adjustment of sub-areas are also achieved during this procedure. The vehicle paths are constructed based on the classical ant colony optimization algorithm, providing an efficient way to traverse all supply points within the coverage area. The classic zigzag CCP method is adopted to fill the contours of each sub-area, and the UAV paths collaborate with vehicle supply points using few switching points. The simulation experiments verify the effectiveness and feasibility of the proposed vehicle–UAV collaboration CCP method, and two comparative experiments demonstrate that the proposed method excels at large-scale CCP scenarios, and achieves a significant improvement in coverage efficiency.

Reinforcement learning-based vehicle travel path reconstruction from sparse automatic licence plate recognition data

ABSTRACT Automatic licence plate recognition (ALPR) data is a vital source for acquiring large-scale vehicle trajectory data in urban transportation research. However, the sparse distribution of ALPR sensors often results in incomplete vehicle trajectories with unobserved travel paths between adjacent ALPR sensors. To address this challenge, this study proposes a novel travel path reconstruction model based on reinforcement learning. First, the maximum entropy inverse reinforcement learning (MaxEnt IRL) method is employed to learn vehicle path selection strategy, i.e. the reward function, from the real data. Then, a comprehensive reward function is formulated by integrating destination rewards. Finally, the Q-learning algorithm is utilized to reconstruct vehicle travel paths between adjacent ALPR sensors based on the comprehensive reward function. Evaluation on a randomly selected set of 100 pairs of adjacent ALPR sensors demonstrates that the proposed model significantly outperforms two baseline methods, achieving reductions of 24.3% and 22.5% in deviation from the real paths.

A spherical vector-based adaptive evolutionary particle swarm optimization for UAV path planning under threat conditions

Unmanned aerial vehicle (UAV) path planning is a constrained multi-objective optimization problem. With the increasing scale of UAV applications, finding an efficient and safe path in complex real-world environments is crucial. However, existing particle swarm optimization (PSO) algorithms struggle with these problems as they fail to consider UAV dynamics, resulting in many infeasible solutions and poor convergence to optimal solutions. To address these challenges, we propose a spherical vector-based adaptive evolutionary particle swarm optimization (SAEPSO) algorithm. This algorithm, based on spherical vectors, directly incorporates UAV dynamic constraints and introduces improved tent map and reverse learning to enhance the diversity and distribution of initial solutions. Additionally, dynamic nonlinear and adaptive factors are integrated to balance exploration and exploitation capabilities. To avoid local optima in highly complex environments, we propose an adaptive acceleration strategy for poor particles, and an evolutionary programming strategy is incorporated to further improve the optimization capability. Finally, we conducted comparative studies and in six benchmark scenarios with varying threat levels, and the results demonstrated that the proposed algorithm outperforms others in the initial solution effectiveness, the final solution accuracy, convergence stability, and scalability.

Dynamic-collaborative path planning based on tradable road priority: an interweaving strategy for emergency vehicle

Connected autonomous vehicles (CAV) and mobile payment technology allow vehicles to engage in peer-to-peer transactions for overtaking priority, creating an opportunity for unprivileged emergency vehicles to pass through road segments faster. This paper presents a novel interweaving strategy, offering a general and thoughtful alternative to the conventional lane-clearing approaches. Our approach is formulated as an integer linear programming (ILP) problem, which determines the optimal path for the emergency vehicle to navigate traffic while coordinating surrounding hindering vehicles to free up space. To address this collaborative path finding problem, we develop a novel A*-based algorithm with a nested structure, and provide a thorough proof of optimality for the proposed Nested A* algorithm. Simulations show the algorithm’s robustness in handling deadlocks, moving obstacles, and congestion, generating globally optimal solutions efficiently. For a 4 × 8 map with 53.125% density, the computation time typically ranges from 0.07 and 0.6 seconds.

An Intelligent 5G Unmanned Aerial Vehicle Path Optimization Algorithm for Offshore Wind Farm Inspection

In recent years, clean energy has gained increasing attention, with offshore wind power playing a crucial role in global energy production. However, the high operating and maintenance costs of offshore wind farms remain a significant challenge. The advent of 5G technology provides a solution for efficiently monitoring and controlling wind power equipment. The use of 5G unmanned aerial vehicles (UAVs) for blade inspections is a promising development. A key challenge is efficiently planning UAV flight paths for fast and effective inspections in complex offshore environments. To address this problem, we conduct an in-depth study of the 5G UAV path optimization method. In this paper, the UAV inspection path problem is modeled as an obstacle avoidance traveling salesman problem (TSP), taking into full account UAV flight constraints and complex sea environment factors, particularly the impact of sea wind on UAV flight speed. We propose a novel Sea Wind-Aware Improved A*-Guided Genetic Algorithm (SWA-IAGA), which integrates an improved A* algorithm to guide the genetic algorithm for efficient path planning, with the assistance of relevant graphical knowledge. This algorithm overcomes the limitations of traditional single-path planning methods, enabling more accurate and efficient path planning.

Multi-Strategy Improved Red-Tailed Hawk Algorithm for Real-Environment Unmanned Aerial Vehicle Path Planning.

In recent years, unmanned aerial vehicle (UAV) technology has advanced significantly, enabling its widespread use in critical applications such as surveillance, search and rescue, and environmental monitoring. However, planning reliable, safe, and economical paths for UAVs in real-world environments remains a significant challenge. In this paper, we propose a multi-strategy improved red-tailed hawk (IRTH) algorithm for UAV path planning in real environments. First, we enhance the quality of the initial population in the algorithm by using a stochastic reverse learning strategy based on Bernoulli mapping. Then, the quality of the initial population is further improved through a dynamic position update optimization strategy based on stochastic mean fusion, which enhances the exploration capabilities of the algorithm and helps it explore promising solution spaces more effectively. Additionally, we proposed an optimization method for frontier position updates based on a trust domain, which better balances exploration and exploitation. To evaluate the effectiveness of the proposed algorithm, we compare it with 11 other algorithms using the IEEE CEC2017 test set and perform statistical analysis to assess differences. The experimental results demonstrate that the IRTH algorithm yields competitive performance. Finally, to validate its applicability in real-world scenarios, we apply the IRTH algorithm to the UAV path-planning problem in practical environments, achieving improved results and successfully performing path planning for UAVs.

Modeling the Optimum Deceleration for the Motion of Asymmetric Rigid Body

PurposeIn this study, we investigate the minimum time (MT) for the spatial motion of a free rigid body (RB) under the influence of viscous friction and gyrostatic moments (GMs). Assuming that the body’s center of mass coinciding with the original point of two Cartesian systems of coordinates.ResultsThe optimal control (OC) law for the slow movements, as a main goal, is established, and the corresponding time and phase pathways are assessed.ConclusionThe resulting innovative outcomes are achieved and displayed for two new scenarios in various graphs to show the gyrostatic moment’s positive impacts. When comparing the obtained results with the previous one when the gyrostatic moment isn’t present, amazing consistency is observed, and the slight deviations between them are discussed. As a result, the acquired results generalize those that were discovered in earlier investigations.ApplicationsThe significance of this study arises from its practical implications in everyday existence, that confine themself to applying gyroscopic theory to preserve the stability and balance of vehicles in the design of which gyroscopes are used, in addition to determining the path of aircraft and marine vehicles.

Multi-AGV multitask collaborative scheduling based on an improved ant colony algorithm

Research on multitask scheduling systems in factory environments is a popular topic in the field of intelligent manufacturing. Existing research mainly focuses on the optimization of automated guided vehicle (AGV) path planning and scheduling, emphasizing on the minimization of conflicts and deadlocks, multi-objective task scheduling, and metaheuristic algorithm optimization, while ignoring path stability and real-time path planning in dynamic environments. Therefore, this paper aims to address these issues to better handle dynamic changes in actual operating environments. This paper establishes a mathematical model with the optimization objective of minimizing the overall running time of material distribution tasks and proposes an improved ant colony algorithm to optimize the model. First, the concept of prior time is introduced to improve the traditional ant colony algorithm. The path of the ongoing task is introduced with a time calculation, and the occupancy time window of each grid point on the path is calculated. Based on this, the initial pheromone distribution on subsequent paths is altered dynamically, which accelerates the convergence of the ants to a collision-free path. Second, in the pheromone update stage, the method of calculating the pheromone increment in the traditional ant colony algorithm is modified. The original distance influence factor is changed to a time influence factor, which ensures that all tasks still have the minimum running time when calculating a collision-free path. Finally, through 30 sets of simulation experiments on material distribution tasks, it is shown that the proposed algorithm shortens the total running time by 15.14%, 12.87%, and 10.59% compared to two ant colony algorithms and one strategic multi-AGV scheduling algorithm, respectively, thus verifying the effectiveness of the proposed method.

Intelligent vehicle path tracking and stability control method based on extension coordinated control of AFS and DYC

Intelligent vehicle path tracking and stability control method based on extension coordinated control of AFS and DYC

Soil Sampling Procedure for Management and Analysis of Legacy Sites in the Korean Peninsula

Background: The Korean Peninsula is split between the South Korea and Democratic People’s Republic of Korea (DPRK, North Korea), with North Korea possessing nuclear weapons. There are rising concerns over legacy sites, areas contaminated primarily by nuclear activities. In North Korea, these sites lack regulatory oversight and are likely neglected even if inactive. Hence, it is vital to devise strategies to manage and assess these sites. Yongbyon, a prominent nuclear site in North Korea, has been proposed for environmental sampling to gauge contamination levels. The goal is to determine contamination by suggesting sample collection points in Yongbyon.Materials and Methods: Using recent data and satellite imagery from the International Atomic Energy Agency’s periodic Safety Measures Report, we selected legacy sites to assess their contamination levels with recent data and satellite imagery. We identified sampling locations using the Visual Sample Plan (VSP) to check nuclear contamination. From these results, we devised a scenario evaluating accessibility and the local environment.Results and Discussion: For Yongbyon in North Korea, this study created two sampling scenarios based on interior access feasibility. Given North Korea’s constraints, we finalized the scenario without interior access. The sampling areas include two steam lines, four vehicle paths, and five green zones outside the facility.Conclusion: Nuclear facility operations worldwide are concerning. When halted, these become legacy sites requiring contamination management. Despite North Korea’s limited recordkeeping and challenges in determining contamination, we devised a sampling scenario using VSP software, factoring in accessibility and movement paths.

Path planning and engineering problems of 3D UAV based on adaptive coati optimization algorithm

In response to the challenges faced by the Coati Optimization Algorithm (COA), including imbalance between exploration and exploitation, slow convergence speed, susceptibility to local optima, and low convergence accuracy, this paper introduces an enhanced variant termed the Adaptive Coati Optimization Algorithm (ACOA). ACOA achieves a balanced exploration–exploitation trade-off through refined exploration strategies and developmental methodologies. It integrates chaos mapping to enhance randomness and global search capabilities and incorporates a dynamic antagonistic learning approach employing random protons to mitigate premature convergence, thereby enhancing algorithmic robustness. Additionally, to prevent entrapment in local optima, ACOA introduces an Adaptive Levy Flight strategy to maintain population diversity, thereby improving convergence accuracy. Furthermore, underperforming individuals are eliminated using a cosine disturbance-based differential evolution strategy to enhance the overall quality of the population. The efficacy of ACOA is assessed across four dimensions: population diversity, exploration–exploitation balance, convergence characteristics, and diverse strategy variations. Ablation experiments further validate the effectiveness of individual strategy modules. Experimental results on CEC-2017 and CEC-2022 benchmarks, along with Wilcoxon rank-sum tests, demonstrate superior performance of ACOA compared to COA and other state-of-the-art optimization algorithms. Finally, ACOA’s applicability and superiority are reaffirmed through experimentation on five real-world engineering challenges and a complex urban three-dimensional unmanned aerial vehicle (UAV) path planning problem.

Electric vehicle path optimization research based on charging and switching methods under V2G

This study presents a novel framework for advancing sustainable urban logistics and distribution systems, with a pivotal focus on fast charging and power exchange modalities as the cornerstone of our research endeavors. Our central contribution encompasses the formulation of an innovative electric vehicle path optimization model, whose paramount objective is to minimize overall operational costs. Integrating V2G technology, we facilitate sophisticated slow charging and discharging management of EVs upon their return to distribution centers, enhancing resource utilization. Moreover, we introduce a robust algorithmic approach for estimating battery degradation costs, meticulously accounting for ambient temperature fluctuations and discharge depth. This methodology, combined with the V2G framework encompassing both charging modes, is effectively solved using a genetic algorithm, ensuring the logistics distribution model’s optimal performance. Simulation outcomes underscore the remarkable capacity of our V2G model to augment operational flexibility in EV logistics distribution, culminating in substantial cost reductions. Simultaneously, it adeptly equilibrates peak and off-peak loads within the distribution grid, fostering a more resilient and efficient energy ecosystem. Through rigorous experimental comparisons, we delve into the intricacies of the charging and swapping mode model, offering profound insights that can inform strategic decision-making within the logistics sector regarding optimal charging and swapping strategies. Furthermore, we explore the ramifications of slow charging and discharging management on the distribution system’s performance, illuminating their potential benefits. A comprehensive sensitivity analysis is conducted to unravel the factors that influence battery loss in EVs, revealing a pronounced positive correlation between elevated temperatures, deeper discharge depths, and accelerated battery degradation. This revelation underscores the importance of considering environmental conditions in EV operation and maintenance strategies.

A novel lightweight YOLOv8-PSS model for obstacle detection on the path of unmanned agricultural vehicles.

The rapid urbanization of rural regions, along with an aging population, has resulted in a substantial manpower scarcity for agricultural output, necessitating the urgent development of highly intelligent and accurate agricultural equipment technologies. This research introduces YOLOv8-PSS, an enhanced lightweight obstacle detection model, to increase the effectiveness and safety of unmanned agricultural robots in intricate field situations. This YOLOv8-based model incorporates a depth camera to precisely identify and locate impediments in the way of autonomous agricultural equipment. Firstly, this work integrates partial convolution (PConv) into the C2f module of the backbone network to improve inference performance and minimize computing load. PConv significantly reduces processing load during convolution operations, enhancing the model's real-time detection performance. Second, a Slim-neck lightweight neck network is introduced, replacing the original neck network's conventional convolution with GSConv, to further improve detection efficiency and accuracy. This adjustment preserves accuracy while reducing the complexity of the model. After optimization, the bounding box loss function is finally upgraded to Shape-IoU (Shape Intersection over Union), which improves both model accuracy and generalization. The experimental results demonstrate that the improved YOLOv8_PSS model achieves a precision of 85.3%, a recall of 88.4%, and an average accuracy of 90.6%. Compared to the original base network, it reduces the number of parameters by 55.8%, decreases the model size by 59.5%, and lowers computational cost by 51.2%. When compared with other algorithms, such as Faster RCNN, SSD, YOLOv3-tiny, and YOLOv5, the improved model strikes an optimal balance between parameter count, computational efficiency, detection speed, and accuracy, yielding superior results. In positioning accuracy tests, the, average and maximum errors in the measured distances between the camera and typical obstacles (within a range of 2-15 meters) were 2.73% and 4.44%, respectively. The model performed effectively under real-world conditions, providing robust technical support for future research on autonomous obstacle avoidance in unmanned agricultural machinery.

A risk-based unmanned aerial vehicle path planning scheme for complex air-ground environments.

Multifarious applications of unmanned aerial vehicles (UAVs) are thriving in extensive fields and facilitating our lives. However, the potential third-party risks (TPRs) on the ground are neglected by developers and companies, which limits large-scale commercialization. Risk assessment is an efficacious method for mitigating TPRs before undertaking flight tasks. This article incorporates the probability of UAV crashing into the TPR assessment model and employs an A* path-planning algorithm to optimize the trade-off between operational TPR cost and economic cost, thereby maximizing overall benefits. Experiments demonstrate the algorithm outperforms both the best-first-search algorithm and Dijkstra's algorithm. In comparison with the path with the least distance, initially, the trade-off results in a increase in distance while achieving an reduction in TPR. As the trade-off progresses, this relationship shifts, leading to a reduction in the distance with only a negligible increase in TPR by 0.0001, matching the TPR-cost-based algorithm. Furthermore, we conduct simulations on the configuration of UAV path networks in five major cities in China based on real-world travel data and building data. Results reveal that the networks consist of one-way paths that are staggered in height. Moreover, in coastal cities particularly, the networks tend to extend over the sea, where the TPR cost istrivial.

RSP-UV: real-time sampling-based path planning method for unmanned vehicles

Abstract The Rapidly-exploring Random Trees Star (RRT*) is widely used in unmanned vehicle path planning. However, it suffers from excessive sampling randomness, long search time and not capable of dynamic obstacle avoidance. This is not only fatal to safety, but also detrimental to smooth driving. We propose a real-time sampling-based path planning method for unmanned vehicles (RSP-UV) that systematically solves these problems. To address the problem of excessive sampling randomness, we formulate a partitioned sampling strategy and limit the density of sampling points. It reduces the sampling time to a certain extent. Additionally, we design a fuzzy controller to adjust the scale factor of the Dynamic Window Approach (DWA) evaluation function to optimise the local path and improve the dynamic obstacle avoidance efficiency. Fusing the improved RRT* and the improved DWA, the global path output from the improved RRT* is used as the bootstrap path for the improved DWA. Besides, the return path set rule is made to avoid the backtracking phenomenon. This improves the reliability and asymptotic optimality of the final path. Simulation experiments show that the trajectories planned by RSP-UV outperform the three compared methods, the similar method, ORRT*-DWA and MTAR. Real experiments show that the average path length of RSP-UV is reduced by 2.26%, 3.03%, and 3.33% compared to the other three methods, respectively. The average planning time of RSP-UV is reduced by 7.16%, 2.15% and 8.78% compared to the other three methods, respectively. RSP-UV not only ensures the success of path planning but also excels in real-time and adaptability. The paths formed by RSP-UV are cheaper and safer.

面向农业巡检场景的多无人机任务分配与路径规划方法

A multi-unmanned aerial vehicle (UAV) task allocation and path planning model with the maximum endurance constraint was constructed specific to the agricultura l patrol scene. Moreover, an optimized ant colony optimization (ACO) algorithm applicable to grid map environment was proposed given such problems of the traditional ACO algorithm as limited path search direction and field of view, failure to find the shortest path and proneness to deadlock. This method preprocessed the grid map environment, extracted the feature points of obstacles, and selected such feature points as the way-finding access nodes; then, the construction efficiency of the solution was enhanced via the nonuniform pheromone distribution based on ACO algorithm, the guiding function of path search was strengthened using Tent chaotic mapping, and the pheromone evaporation coefficient was dynamically adjusted to prevent the algorithm from too early convergence. The experimental results show that the proposed method more conforms to the operational requirements of rotary-wing UAVs with limited cruising ability in comparison with the existing methods. Besides, the convergence efficiency of the improved ACO algorithm embedded with the niche genetic algorithm is 30.55% higher than that of the traditional ACO algorithm. The experimental results verify the practicability and effectiveness of the proposed method.

A Novel Spherical Shortest Path Planning Method for UAVs

As a central subdivision of the low-altitude economy industry, industrial and consumer drones have broad market application prospects and are becoming the primary focus of the low-altitude economy; however, with increasing aircraft density, effective planning of reasonable flight paths and avoiding conflicts between flight paths have become critical issues in UAV clustering. Current UAV path planning often concentrates on 2D and 3D realistic scenes, which do not meet the actual requirements of realistic spherical paths. This paper has proposed a Gradient-Based Optimization algorithm based on the State Transition function (STGBO) to address the spherical path planning problem for UAV clusters. The state transition function is applied on the scale of medium and high-dimensional cities, balancing the stability and efficiency of the algorithm. Through evolution and comparisons with many mainstream meta-heuristic algorithms, STGBO has demonstrated superior performance and effectiveness in solving Medium-Altitude Unmanned Aerial Vehicle (MUAV) path planning problems on three-dimensional spherical surfaces, contributing to the development of the low-altitude economy.

A novel hybrid improved dingo algorithm for unmanned aerial vehicle path planning

A novel hybrid improved dingo algorithm for unmanned aerial vehicle path planning

Checkpoint data-driven GCN-GRU vehicle trajectory and traffic flow prediction

With the development of information technology, massive traffic data-driven short-term traffic situation analysis of urban road networks has become a research hotspot in urban traffic management. Accurate vehicle trajectory and traffic flow prediction can provide technical support for vehicle path planning and road congestion warning. Unlike most studies that use GPS data to predict vehicle trajectories, this paper combines the broad coverage, high reliability, and lighter weight of traffic checkpoint data to propose a method that uses trajectory prediction technology to forecast the traffic flow in urban road networks accurately. The method adopts a checkpoint data-driven approach for data collection, combines graph convolutional neural network (GCN) and gated recurrent unit (GRU) models to more effectively learn and extract spatiotemporal correlation features of vehicle trajectories, which significantly improves the accuracy of vehicle trajectory prediction, and uses the output of the trajectory prediction model to forecast traffic flow more accurately. Firstly, transforming the checkpoint data into daily vehicle trajectories with time series characteristics, realizing the vehicle trajectory travel chain division. Secondly, the adjacency matrix is established by using the spatial relationship of each checkpoint, and the feature matrix of the vehicle’s driving trajectory over time is established, which is used as the input of GCN to learn the spatial characteristics of the vehicle while driving on the road network, and then GRU is added to further process the data after GCN training, constructing a GCN-GRU vehicle trajectory prediction model for vehicle trajectory prediction. Finally, the traffic flow of each checkpoint is calculated based on the prediction result of vehicle trajectory and compared with the real checkpoint flow. This paper conducts many experiments on the Qingdao City Shinan district checkpoint dataset. The results show that compared with the single models GCN, GRU, BiGRU, and BiLSTM, the GCN-GRU model has reduced the MAE by 0.75, 0.46, 0.52, and 0.57, and the RMSE by 0.76, 0.52, 0.58, and 0.68, respectively, demonstrating stronger spatial and temporal correlation characteristics and higher prediction accuracy. The MAPE between the forecasted flow and the real flow is 0.18, which verifies the reliability of the proposed method.