Search Results (612)

Effective path planning is essential for autonomous drone flight to enhance task efficiency. Many researchers have applied swarm intelligence algorithms to drone path planning. For instance, the traditional Butterfly Optimization Algorithm (BOA) has been used for this purpose. However, traditional BOA faces challenges such as slow convergence and susceptibility to being trapped in local optima. An Improved Butterfly Optimization Algorithm (IBOA) has been developed to identify optimal routes to address these limitations. Initially, ICMIC mapping is utilized to establish the butterfly community, enhancing the initial population’s diversity and preventing premature algorithm convergence. Following this, a population reset strategy is introduced, replacing weaker individuals over a specified number of iterations while maintaining a constant population size. This strategy enhances the algorithm’s ability to avoid local optima and increases its robustness. Additionally, characteristics of the Particle Swarm Optimization (PSO) algorithm are integrated to enhance the butterfly’s location update mechanism, accelerating the algorithm’s convergence rate. To evaluate the performance of the IBOA algorithm, this study designed a CEC2020 function test experiment and compared it with several swarm intelligence algorithms. The results showed that IBOA achieved the best performance in 70% of the function tests, outperforming 75% of the other algorithms. In the path planning experiments within a simulated environment, IBOA quickly converged to the optimal path, and the paths it planned were the shortest and safest compared to those generated by other algorithms. Full article

This research presents a novel hybrid approach, which combines the White Shark Optimizer (WSO) metaheuristic algorithm with chaotic maps integrated into the binarization process. Inspired by the predatory behavior of white sharks, WSO has shown great potential to navigate complex search spaces for optimization tasks. On the other hand, chaotic maps are nonlinear dynamical systems that generate pseudo-random sequences, allowing for better solution diversification and avoiding local optima. By hybridizing WSO and chaotic maps through adaptive binarization rules, the complementary strengths of both approaches are leveraged to obtain high-quality solutions. We have solved the Set Covering Problem (SCP), a well-known NP-hard combinatorial optimization challenge with real-world applications in several domains, and experimental results indicate that LOG and TENT chaotic maps are better after statistical testing. This hybrid approach could have practical applications in telecommunication network optimization, transportation route planning, and resource-constrained allocation. Full article

The logistics demands of industries represented by e-commerce have experienced explosive growth in recent years. Vehicle path-planning plays a crucial role in optimization systems for logistics and distribution. A path-planning scheme suitable for an actual scenario is the key to reducing costs and improving service efficiency in logistics industries. In complex application scenarios, however, it is difficult for conventional heuristic algorithms to ensure the quality of solutions for vehicle routing problems. This study proposes a joint approach based on the genetic algorithm and graph convolutional network for solving the capacitated vehicle routing problem with multiple distribution centers. First, we use the heuristic method to modularize the complex environment and encode each module based on the constraint conditions. Next, the graph convolutional network is adopted for feature embedding for the graph representation of the vehicle routing problem, and multiple decoders are used to increase the diversity of the solution space. Meanwhile, the REINFORCE algorithm with a baseline is employed to train the model, ensuring quick returns of high-quality solutions. Moreover, the fitness function is calculated based on the solution to each module, and the genetic algorithm is employed to seek the optimal solution on a global scale. Finally, the effectiveness of the proposed framework is validated through experiments at different scales and comparisons with other algorithms. The experimental results show that, compared to the single decoder GCN-based solving method, the method proposed in this paper improves the solving success rate to 100% across 15 generated instances. The average path length obtained is only 11% of the optimal solution produced by the GCN-based multi-decoder method. Full article

In urban logistics, effective maintenance is crucial for maintaining the reliability and efficiency of energy supply systems, impacting both asset performance and operational stability. This paper addresses the scheduling and routing plans for maintenance of power generation assets over a multi-period horizon. We model this problem as a multi-period team orienteering problem. To address this multi-period challenge, we propose a dual approach: a novel reinforcement learning (RL) framework and a biased-randomized heuristic algorithm. The RL-based method dynamically learns from real-time operational data and evolving asset conditions, adapting to changes in asset health and failure probabilities to optimize decision making. In addition, we develop and apply a biased-randomized heuristic algorithm designed to provide effective solutions within practical computational limits. Our approach is validated through a series of computational experiments comparing the RL model and the heuristic algorithm. The results demonstrate that, when properly trained, the RL-based model is able to offer equivalent or even superior performance compared to the heuristic algorithm. Full article

Inland waterway transport is an important mode of transportation for many countries and regions. Route planning optimization can reduce navigation time, avoid traffic congestion, and improve transportation efficiency. In actual operations, many vessels determine their navigation routes based on the experience of their shipowners. When the captain fails to obtain accurate information, experience-based routes may pose significant navigation risks and may not consider the overall economic efficiency. This study proposes a comprehensive method for optimizing inland waterway vessel routes using automatic identification system (AIS) data, considering the geographical characteristics of inland waterways and navigation constraints. First, AIS data from vessels in inland waters are collected, and the multi-objective Peak Douglas–Peucker (MPDP) algorithm is applied to compress the trajectory data. Compared to the traditional DP algorithm, the MPDP algorithm reduces the average compression rate by 5.27%, decreases length loss by 0.04%, optimizes Euclidean distance by 50.16%, and improves the mean deviations in heading and speed by 23.53% and 10.86%, respectively. Next, the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm is used to perform cluster analysis on the compressed route points. Compared to the traditional DBSCAN algorithm, the OPTICS algorithm identifies more clusters that are both detailed and hierarchically structured, including some critical waypoints that DBSCAN may overlook. Based on the clustering results, the A* algorithm is used to determine the connectivity between clusters. Finally, the nondominated sorting genetic algorithm II is used to select suitable route points within the connected clusters, optimizing objectives, including path length and route congestion, to form an optimized complete route. Experiments using vessel data from the waters near Shuangshan Island indicate that, when compared to three classic original routes, the proposed method achieves path length optimizations of 4.28%, 1.67%, and 0.24%, respectively, and reduces congestion by 24.15%. These improvements significantly enhance the planning efficiency of inland waterway vessel routes. These findings provide a scientific basis and technical support for inland waterway transport. Full article

Due to the lack of real-time planning for fire escape routes in large buildings, the current route planning methods fail to adequately consider factors related to the fire situation. This study introduces a real-time fire monitoring and dynamic path planning system based on an improved ant colony algorithm, comprising a hierarchical arrangement of upper and lower computing units. The lower unit employs an array of sensors to collect environmental data in real time, which is subsequently transmitted to an upper-level computer equipped with LabVIEW. Following a comprehensive data analysis, pertinent visualizations are presented. Capitalizing on the acquired fire situational awareness, a propagation model for fire spreading is developed. An enhanced ant colony algorithm is then deployed to calculate and plan escape routes by introducing a fire spread model to enhance the accuracy of escape route planning and incorporating the A* algorithm to improve the convergence speed of the ant colony algorithm. In response to potential anomalies in sensor data under elevated temperature conditions, a correction model for data integrity is proposed. The real-time depiction of escape routes is facilitated through the integration of LabVIEW2018 and MATLAB2023a, ensuring the dependability and safety of the path planning process. Empirical results demonstrate the system’s capability to perform real-time fire surveillance coupled with efficient escape route planning. When benchmarked against the traditional ant colony algorithm, the refined version exhibits expedited convergence, augmented real-time performance, and effectuates an average reduction of 17.1% in the length of the escape trajectory. Such advancements contribute significantly to enhancing evacuation efficiency and minimizing potential casualties. Full article

This study presents a bi-objective optimization model for the Green Vehicle-Routing Problem in cold chain logistics, with a focus on symmetric distance matrices, aiming to minimize total costs, including carbon emissions, while maximizing customer satisfaction. To address this complex challenge, we developed a Stage-Specific Multi-Objective Five-Element Cycle Optimization algorithm (MOFECO-SS), which dynamically adjusts optimization strategies across different stages of the process, thereby enhancing overall efficiency. Extensive comparative analyses with existing algorithms demonstrate that MOFECO-SS consistently outperforms in solving the multi-objective optimization model, particularly in reducing total costs and carbon emissions while maintaining high levels of customer satisfaction. The symmetric nature of the distance matrix further aids in achieving balanced and optimized route planning. The results highlight that MOFECO-SS offers decision-makers flexible route planning options that balance cost efficiency with environmental sustainability, ultimately improving the effectiveness of cold chain logistics operations. Full article

Nature-inspired algorithms allow us to solve many problems related to the search for optimal solutions. One such issue is the problem of searching for optimal routes. In this paper, ant colony optimization is used to search for optimal tram routes. Ant colony optimization is a method inspired by the behavior of ants in nature, which as a group are able to successfully find optimal routes from the nest to food. The aim of this paper is to present a practical application of the algorithm as a tool for public transport network planning. In urban public transport, travel time is crucial. It is a major factor in passengers’ choice of transport mode. Therefore, in this paper, the objective function determining the operation of the algorithm is driving time. Scheduled time, real time and theoretical time are analyzed and compared. The routes are then compared with each other in order to select the optimal solution. A case study involving one of the largest tramway networks in Poland demonstrates the effectiveness of the nature-inspired algorithm. The obtained results allow route optimization by selecting the route with the shortest travel time. Thus, the development of the entire network is also possible. In addition, due to its versatility, the method can be applied to various modes of transport. Full article

This research focuses on integrating seakeeping indices into the trajectory planning of a mother ship in order to minimize risks during UAV (unmanned aerial vehicle) takeoff and landing in challenging sea conditions. By considering vessel dynamics and environmental factors, the proposed trajectory planning algorithm computes optimal paths that prioritize the stability and safety of the ship, mitigating the impact of adverse weather on UAV operations. Specifically, the new adaptive weather routing model presented is based on a genetic algorithm. The model uses the previously evaluated response amplitude operators (RAOs) for the reference ship at different velocities and encounter angles, along with weather forecast data provided by the global wave model (GWAM). Preliminary evaluations confirm the effectiveness of the presented model in significantly improving the reliability of autonomous UAV operations from a mother ship across all encountered sea state conditions, particularly when compared with a graph-based solution. The current results clearly demonstrate that it is possible to achieve appreciable improvements in ship seakeeping performance, thereby making UAV-related operations safer. Full article

With the improvement of people’s living standards and the widening of circulation channels, the demand for fresh agricultural products continues to increase. The increase in demand will lead to an increase in delivery vehicles, costs, and carbon emissions, among which the increase in carbon emissions will aggravate pollution and is not conducive to sustainable development. Therefore, it is very important to balance economic and environmental benefits in the distribution of fresh agricultural products. Based on the analysis of the distribution characteristics of fresh agricultural products, this paper studies the optimization of the cold chain distribution route of fresh agricultural products considering carbon emission. Firstly, the cold chain distribution route planning of fresh agricultural products was investigated and analyzed by the interview method, and the basis for establishing the model objective and constraint conditions was obtained. Then, taking the minimum total cost including carbon emission cost as the optimization goal, the cold chain distribution route optimization model for mixed vehicle types is established considering electric refrigerated vehicles, gasoline refrigerated vehicles, and so on. Genetic algorithm was used to solve the model, and MATLAB2018b was used to substitute specific case data for simulation analysis. The analysis results show that increasing the consideration of carbon emission and mixed vehicle types in the distribution route of fresh agricultural products can not only reduce the distribution cost but also reduce the carbon emission. To some extent, the research content of this paper can provide a reference for enterprises in planning cold chain distribution routes of fresh agricultural products. Full article

This study addresses the challenges of maritime traffic management in the western waters of Taiwan, a region characterized by substantial commercial shipping activity and ongoing environmental development. Using 2023 Automatic Identification System (AIS) data, this study develops a robust feature extraction framework involving data cleaning, anomaly trajectory point detection, trajectory compression, and advanced processing techniques. Dynamic Time Warping (DTW) and the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithms are applied to cluster the trajectory data, revealing 16 distinct maritime traffic patterns, key navigation routes, and intersections. The findings provide fresh perspectives on analyzing maritime traffic, identifying high-risk areas, and informing safety and spatial planning. In practical applications, the results help navigators optimize route planning, improve resource allocation for maritime authorities, and inform the development of infrastructure and navigational aids. Furthermore, these outcomes are essential for detecting abnormal ship behavior, and they highlight the potential of route extraction in maritime surveillance. Full article

The development of autonomous agricultural robots using a global navigation satellite system aided by real-time kinematics and an inertial measurement unit for position and orientation determination must address the accuracy, reliability, and cost of these components. This study aims to develop and evaluate a robotic platform with autonomous navigation using low-cost components. A navigation algorithm was developed based on the kinematics of a differential vehicle, combined with a proportional and integral steering controller that followed a point-to-point route until the desired route was completed. Two route mapping methods were tested. The performance of the platform control algorithm was evaluated by following a predefined route and calculating metrics such as the maximum cross-track error, mean absolute error, standard deviation of the error, and root mean squared error. The strategy of planning routes with closer waypoints reduces cross-track errors. The results showed that when adopting waypoints every 3 m, better performance was obtained compared to waypoints only at the vertices, with maximum cross-track error being 44.4% lower, MAE 64.1% lower, SD 39.4% lower, and RMSE 52.5% lower. This study demonstrates the feasibility of developing autonomous agricultural robots with low-cost components and highlights the importance of careful route planning to optimize navigation accuracy. Full article

The implementation of unmanned intelligent construction on the concrete surfaces of an airport effectively improves construction accuracy and reduces personnel investment. On the basis of three known common tracked vehicle dynamics models, reference trajectory planning and trajectory tracking controller algorithms need to be designed. In this paper, based on the driving characteristics of the tracked vehicle and the requirements of the stepping trajectory, a quartic polynomial trajectory planning algorithm was selected with the stability of the curve as a whole and the end point as the optimization goal, combining the tracked vehicle dynamics model, collision constraints, start–stop constraints and other boundary conditions. The objective function of trajectory planning was designed to effectively plan the reference trajectory of the tracked vehicle’s step-by-step travel. In order to realize accurate trajectory tracking control, a nonlinear model predictive controller with transverse-longitudinal integrated control was designed. To improve the real-time performance of the controller, a linear model predictive controller with horizontal and longitudinal decoupling was designed. MATLAB 2023A and CoppeliaSim V4.5.1 were used to co-simulate the two controller models. The experimental results show that the advantages and disadvantages of the tracked vehicle dynamics model and controller design are verified. Full article

As an important part of urban terminal delivery, automated guided vehicles (AGVs) have been widely used in the field of takeout delivery. Due to the real-time generation of takeout orders, the delivery system is required to be extremely dynamic, so the AGV needs to be dynamically scheduled. At the same time, the uncertainty in the delivery process (such as the meal preparation time) further increases the complexity and difficulty of AGV scheduling. Considering the influence of these two factors, the method of embedding a stochastic programming model into a rolling mechanism is adopted to optimize the AGV delivery routing. Specifically, to handle real-time orders under dynamic demand, an optimization mechanism based on a rolling scheduling framework is proposed, which allows the AGV’s route to be continuously updated. Unlike most VRP models, an open chain structure is used to describe the dynamic delivery path of AGVs. In order to deal with the impact of uncertain meal preparation time on route planning, a stochastic programming model is formulated with the purpose of minimizing the expected order timeout rate and the total customer waiting time. In addition, an effective path merging strategy and after-effects strategy are also considered in the model. In order to solve the proposed mathematical programming model, a multi-objective optimization algorithm based on a NSGA-III framework is developed. Finally, a series of experimental results demonstrate the effectiveness and superiority of the proposed model and algorithm. Full article

In addressing the high costs, inefficiencies, and limitations of purely digital simulations in maritime trials for unmanned vessel path planning, this paper introduces a ship virtual path planning simulation test system. This system, unbound by temporal and spatial constraints, vividly showcases the navigational performance of vessels. After analyzing the virtual testing requirements for the autonomous navigation performance of unmanned surface vehicles (USVs), we established the overall framework of this system. Data-driven by a numerical simulation platform, the system achieves synchronized operation between physical and virtual platforms and supports interactive path planning simulations between USVs and the virtual testing system. Furthermore, to address the limitations of traditional ship trajectory planning evaluation, this paper develops a global path planning fitness evaluation function that comprehensively considers trajectory safety, navigation distance, and vessel stability, achieving optimal comprehensive routes through the particle swarm optimization algorithm. Test results indicate an average roll reduction of 14.31% in the planned routes, with a slight increase in navigation distance. By integrating two-dimensional curve simulation with three-dimensional visualization, this paper not only overcomes the limitations of purely physical and purely virtual simulations but also enhances the overall credibility and intuitiveness of the simulation. Experimental results validate the system’s effectiveness, providing a novel method for autonomous navigation testing and evaluation of USVs. Full article