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Background Purpose Methods Results Conclusions Robotic radiosurgery treatments allow for precise non‐coplanar beam delivery by utilizing a robot equipped with a linac that traverses through a set of predetermined nodes. High quality treatment plans can be produced but treatment times can grow large, with one substantial component being the robot traversal time.The aim of this study is to reduce the treatment time for robotic radiosurgery treatments by introducing algorithms for reducing the robot traversal time. The algorithms are integrated into a commercial treatment planning system.First, an optimization framework for robotic radiosurgery planning is detailed, including a heuristic optimization method for node selection. Second, two methods aimed at reducing the traversal time are introduced. One utilizes a centrality measure focusing on the structure of the node network, while the other is based on the direct computation of traversal times during optimization. A comparison between plans with and without the time‐reducing algorithms is made for three brain cases and one liver case with basis in treatment time, plan quality, monitor units, and network structure of the selected nodes.Large decreases in traversal times are obtained by the traversal time reducing algorithms, with reductions of up to 49 % in the brain cases and 31 % in the liver case. The resulting reductions in treatment times are up to 30 % and 13 %, respectively. Small differences in plan quality are observed, with similar dose‐volume histograms, dose distributions, and conformity/gradient indices.The total treatment time of the robotic radiosurgery treatments can be reduced by selecting nodes with more efficient robot traversal paths, while maintaining plan quality. [ABSTRACT FROM AUTHOR]

In recent years, the delivery of medical supplies has faced significant challenges due to natural disasters and recurrent public health emergencies. Addressing the need for improved logistics operations during such crises, this article presents an innovative approach, namely integrating vehicle and unmanned aerial vehicle (UAV) logistics to enhance the efficiency and resilience of medical supply chains. Our study introduces a dual-mode distribution framework which employs the density-based spatial clustering of applications with noise (DBSCAN) algorithm for efficiently clustering demand zones unreachable by conventional vehicles, thereby identifying areas requiring UAV delivery. Furthermore, we categorize the demand for medical supplies into two distinct sets based on vehicle accessibility, optimizing distribution routes via both UAVs and vehicles. Through comparative analysis, our findings reveal that the artificial bee colony (ABC) algorithm significantly outperforms the genetic algorithm in terms of solving efficiency, iteration counts, and delivery speed. However, the ABC algorithm's tendency toward early local optimization and rapid convergence leads to potential stagnation in local optima. To mitigate this issue, we incorporate a simulated annealing technique into the ABC framework, culminating in a refined optimization approach which successfully overcomes the limitations of premature local optima convergence. The experimental results validate the efficacy of our enhanced algorithm, demonstrating reduced iteration counts, shorter computation times, and substantially improved solution quality over traditional logistic models. The proposed method holds promise for significantly improving the operational efficiency and service quality of the healthcare system's logistics during critical situations. [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligence technology has seen increasingly widespread application in the field of intelligent manufacturing, particularly with deep learning offering novel methods for recognizing geometric shapes with specific features. In traditional CNC machining, computer-aided manufacturing (CAM) typically generates G-code for specific machine tools based on existing models. However, the tool paths for most CNC machines consist of a series of collinear motion commands (G01), which often result in discontinuities in the curvature of adjacent tool paths, leading to machining defects. To address these issues, this paper proposes a method for CNC system machining trajectory feature recognition and path optimization based on intelligent agents. This method employs intelligent agents to construct models and analyze the key geometric information in the G-code generated during CNC machining, and it uses the MCRL deep learning model incorporating linear attention mechanisms and multiple neural networks for recognition and classification. Path optimization is then carried out using mean filtering, Bézier curve fitting, and an improved novel adaptive coati optimization algorithm (NACOA) according to the degree of unsmoothness of the path. The effectiveness of the proposed method is validated through the optimization of process files for gear models, pentagram bosses, and maple leaf models. The research results indicate that the CNC system machining trajectory feature recognition and path optimization method based on intelligent agents can significantly enhance the smoothness of CNC machining paths and reduce machining defects, offering substantial application value. [ABSTRACT FROM AUTHOR]

The efficient operation and intelligent upgrading of public transportation can effectively enhance the attractiveness of conventional public transportation. In order to improve the delicacy management level of bus operations, this study designed a new dynamic optimization model for single-line bus operations with the dual optimization objectives of the lowest passenger travel cost and lowest operation cost, using a combination of the strategy of stop-skipping control and local route optimization. Simulated annealing (SA) was introduced into the genetic algorithm (GA) to design a hybrid heuristic algorithm for model solving. The effectiveness of the optimization model and the hybrid algorithm were verified and evaluated by using the No. 115 bus line in Ganzhou City as an example. The results showed that the proposed optimization model had a good usability, which can effectively improve the average vehicle speed, shorten the overall waiting time of passengers, and enhance the operational efficiency of the line. The hybrid algorithm saw significant improvement in terms of the iteration speed and the quality of the optimal solution compared with the conventional genetic algorithm. [ABSTRACT FROM AUTHOR]

Optimal path planning refers to choosing a collision-free, shortest, and smooth path, which is achieved by proposing a new technique, Optimized A* (OA*). The proposed OA* is the hybrid version of three techniques that are ABA* (Adaptive Bidirectional A*), VTM (Vector triangulation method), and PSF (Path smoothing filter which is the modification of Bezier curve technique). The conventional A* only deal with the heuristic values of concerned nodes, but the proposed ABA* algorithm also considers the occupied cells/ obstacles present near the starting and goal nodes in the grid cells to select the shortest path from start to destination. Due to the involvement of the occupied cells, the execution time, path curves, and the number of operations is primarily reduced because occupied nodes are considered as closed nodes, and its heuristic calculation is not considered in the conventional method. The trajectory generated by the proposed ABA* technique is also further made smoother by implementing a path smoothing filter that is the combination of VTM and modified Bezier curve technique based on selecting optimal control point for the collision free trajectory. While implementing the proposed OA* makes the path trajectory become smoother by reducing the number of sharp turns by 85.36% w.r.t traditional ACO, 73.91% w.r.t improved ACO, and 64.70% w.r.t hybrid solution of ACO+ A*. Due to the reduction in the number of sharp turns in the path trajectory, the acceleration of the mobile robot is increased by 52.96% w.r.t traditional ACO, 28.63% w.r.t improved ACO, and 19.96% w.r.t hybrid solution of ACO+ A*. [ABSTRACT FROM AUTHOR]

Recreational sports are essential for boosting physical health and improving quality of life. The goal of this research is to optimize the planning of leisure sports by presenting a new method based on a multi-objective genetic algorithm. Acknowledging the intricacy of organizing recreational sports events, we suggest an approach that concurrently maximizes several goals, such as the use of resources, player happiness, and ecological impact. The topic is first formulated as a multi-objective optimization task in the paper, and a genetic algorithm is used to handle the objectives' inherent conflict. With its ability to effectively explore the solution space, the genetic algorithm produces a set of Pareto-optimal solutions that show trade-offs for the conflicting goals. The integration of several factors, including time preferences, geographical limitations, and environmental sustainability, guarantees a thorough and equitable strategy for leisure sports scheduling. In the area of leisure sports planning, the use of a multi-objective genetic algorithm offers a reliable solution that can be tailored to meet various circumstances and goals. As the need of encouraging healthy lifestyles becomes more widely acknowledged, this research offers a useful tool for maximizing the organization and performance of recreational sports activities, enhancing the overall wellbeing of people and communities. [ABSTRACT FROM AUTHOR]

Slope instability, driven by factors such as rainfall, seismic activity, and human influence, presents a pervasive hazard worldwide. Timely assessment of slope stability and accurate identification of its most critical slip surface are important for slope safety early warning and management. Currently, for the slope reliability analysis by the strength reduction method (SRM), the critical failure path is approximately determined through visualization techniques, which is not sufficiently precise. Therefore, this study proposes a critical failure search method based on SRM for slope reliability analysis, aiming to accurately identify the critical slip surface. The critical failure path is considered as the path with the maximum plastic dissipative energy density (PDED) and is searched in the constructed weighted graph based on the dissipated energy of the slope. This proposed method is further applied with an engineering slope to find the critical failure path and assess its reliability during and after construction. The searched critical failure path lies within the approximate range obtained through conventional visualization methods. Finally, a reliability prediction model consisting of time, rainfall, and deformation component is further constructed, which allows rapid estimation of the slope reliability through available monitor data. The results of reliability analysis indicate that construction disturbances have a significant impact on slope stability, along with other factors such as rainfall and creep. [ABSTRACT FROM AUTHOR]

Robot tea picking is an inevitable trend to solve the problem of tea picking, and the picking path planning is directly related to the robot picking efficiency. An Improved Ant Colony Algorithm (IACA) is proposed, which firstly introduces the adaptive adjustment mechanism into the pheromone volatilization factor of the ant colony algorithm, and then sets the pheromone volatilization factor with a high initial value to improve the searching speed, and then adjusts the size of its value within a certain range in real time according to the iterative results, and finally solves the problem that the searching of the ant colony algorithm is prone to fall into the local optimal solution. On the basis of visual recognition of tea leaves and obtaining coordinate information, the improved ant colony algorithm is used to enter the path planning simulation experiments, and the planning results of the other six algorithms are compared with the similar algorithms and dissimilar algorithms, and the experimental results indicate that the IACA method has improved the shortest path index by 5% compared to the basic ant colony algorithm, and by an average of 4% compared to similar improved ant colony algorithms. In comparison to different optimization algorithms, the enhancement has an average increase of 6%; Furthermore, the convergence speed has been improved by 60% compared to six other methods. The standard deviation of repeated experimental results is 50% lower than the other six methods. The gap between the results of multiple repeated experiments is small, the degree of fluctuation is low, and the calculation results are more stable, which verifies the superiority of IACA method. Therefore, the improvement of the ant colony algorithm makes the pheromone concentration value with adaptive adjustment ability, which reflects good effects in path optimization, convergence speed improvement, stability of results, etc., and has good application value for the path planning problems such as tea picking, which has complex paths and large computational volume. [ABSTRACT FROM AUTHOR]

Polyphenol-based materials, primarily composed of polyphenolic compounds, have attracted considerable attention due to their unique chemical structures and biological activities. However, there are many derivatives of polyphenols, resulting in the complexity and diversity of polyphenol-based materials. Traditional methods are difficult to meet the rapid development of polyphenol-based materials. Machine learning, known for its proficiency in predicting performance, optimizing synthesis processes, and designing novel materials, offers significant potential in the intelligent design and applications of polyphenol-based materials. In this review, we summarize the recent advancements in the research and development of polyphenol-based materials and machine learning. The intersection of polyphenol-based materials and machine learning is also discussed, including their applications in biomedical, environmental, and energy fields. The challenges and prospects for the future development of polyphenol-based materials based on machine learning are highlighted.

Objective After manually polishing the surface of railway vehicle carbodies and subsequently repairing paint defects, because of uncertainties in the vehicle operating environment and varying degrees of paint deterioration, the surface maintenance process becomes difficult to control, leading to instability in the process. Therefore, it is necessary to study the maintenance technology for the surface quality of railway vehicle carbodies. Method A rail vehicle carbody grinding robot process system is established, employing visual sensors to detect surface paint defects such as yellow spots, scratches, and peeling along the grinding path. The defects are processed through image analysis, and converted into central coordinates of the paint defect areas. The 'nine-point method' is applied for hand-eye calibration of the robot to calculate the transformation matrix. The central coordinates obtained from image acquisition are transformed into the robot working space coordinates. Using an improved adaptive genetic algorithm based on the shortest path principle, the robot coordinates are optimized for path planning to form the most efficient route for defect grinding on vehicle carbody surface. Result & Conclusion The system automatically extracts defect area central coordinates from captured images and converts them into end-effector movement coordinates in robot workspace. Through the optimization of robot defect grinding paths using the above algorithm, the system improves the efficiency of manual operation while enhancing the stability of surface quality maintenance technology for vehicle carbodies.

Abstract Manned and unmanned systems are prevalent in a wide range of aerial searching applications. For aircraft whose trajectory is not or cannot be planned on-the-fly, optimal deterministic search pattern generation is a critical area of research. Lissajous curves have recently caught attention as excellent candidates for all kinds of aerial search applications, but little fundamental research has been done to understand how best to design Lissajous pattern (LP)s for this use. This paper examines the optimization of these search patterns from analytical, numerical, and data-driven perspectives to establish the state of the field in Lissajous curves for aerial search. From an analytical perspective, it was found that the average expected distance between a Lissajous searcher and a random target on a unit square approaches 0.586 as search time increases. Furthermore, an analytical approximation for the average searcher speed was found to guarantee error of no more than 22.1%. Important outcomes from the numerical optimization of Lissajous search patterns include the development of an intuitive evaluation criterion and the conclusion that irrational frequency ratios near 0.8 typically yield highest performance. Finally, while a robust predictive model for fast pattern optimization is yet out of reach, initial results indicate that such an approach shows promise.

Optimizing travel routes in urban transportation networks is essential for enhancing traffic efficiency in major cities. This study focuses on optimizing the combined subway and taxi travel routes from Beijing Union University to Beijing South Railway Station, a critical route for students and staff in Beijing. To address this, we propose three genetic algorithms: the Standard Genetic Algorithm (SGA), the Multipoint Crossover and Elitist Selection Genetic Algorithm (MPEGA), and the Improved Dynamic Crossover and Mutation Rate Genetic Algorithm (IDCMGA). Experimental results indicate that MPEGA reduces the mean travel cost by 15.21%, the variance by 81.72%, and the standard deviation by 57.25% compared to SGA. Additionally, IDCMGA reduces the average by 1.76%, the variance by 63.16%, and the standard deviation by 39.30% compared to MPEGA. The improved IDCMGA algorithm demonstrates significant advantages over the other two algorithms in terms of global optimization capability, convergence speed, and stability, and is more adept at adapting to new traffic conditions to identify the optimal route. Optimizing this route not only reduces commuting time and costs but also alleviates traffic congestion, thereby enhancing the overall efficiency of the urban transportation system. [ABSTRACT FROM AUTHOR]

This study aims to optimize the logistics distribution path of unmanned vehicles under vehicle road collaboration. The vehicle road resistance was calculated, and an optimization objective function for the logistics distribution path of unmanned vehicles was constructed based on the calculation results. The paper uses genetic algorithm to improve the ant colony algorithm, and the combination of the two can accelerate the convergence speed of the algorithm and improve the solving efficiency. Meanwhile, the population diversity of genetic algorithms and the pheromone propagation utilized by ant colony algorithms can also increase path diversity, making optimization results more robust and diverse. The improved ant colony algorithm was employed to optimize the delivery path. Experimental validation demonstrated that the proposed method outperforms others in terms of total delivery delay time and total distance of unmanned vehicle logistics delivery paths, offering higher efficiency and more optimized delivery plans. Specifically, the experimental results revealed that the total delivery delay time of this method is the shortest, not exceeding 30 minutes, and the shortest delivery path is only 100.3 kilometers. [ABSTRACT FROM AUTHOR]

Traditional villages set a good example of harmonious coexistence between humans and nature, which constitutes abundant ecological products. Therefore, grasping the value realization status of traditional villages′ ecological products and promoting their value realization under the construction of rural ecological civilization is particularly significant. We conducted a study on the measurement and optimization path of the value realization of ecological products in traditional villages through questionnaire surveys, data analysis, and field investigations. Based on the property rights theory, we considered four typical traditional village in the Hunan Province, categorized the types of ecological products in these traditional villages and constructed an index system for realizing their value. The results indicate that traditional village ecological products can be divided into three categories: private, quasi-public, and public ecological products. They consist of three elements: ecological material products, ecological cultural services, and ecological regulatory services. The low measured difference of the value realization of the traditional village ′ s ecological products is shown, and the relatively moderate level of value realization is indicated, ranging from 57.08 to 60.85 points, in which the Hengling Village had the highest score. In comparison, Yutou Village had the lowest score. Based on the analysis of the measurement results of value realization and field research, targeted optimization paths were proposed for traditional villages with tourism development-oriented or agriculture-dominated models, in order to promote the realization of ecological product value in traditional villages and facilitate their revitalization and sustainable development. [ABSTRACT FROM AUTHOR]

Excessive emission of greenhouse gases leads to the increasing greenhouse effect, adversely affecting the global climate. Carbon dioxide is the dominant part of greenhouse gases; reducing its emission is the most important way to solve the climate problem. Aiming at the characteristics of the dangerous goods transportation market and the development of the carbon tax policy, the carbon tax cost and the cost of dangerous goods transportation are introduced, and the characteristics of the dangerous goods transportation and the road traffic network and the road traffic in each period are fully analyzed. The optimization model of the path of the hazardous goods vehicles is established with the optimization objective of the lowest total cost. Then, by analyzing the advantages and disadvantages of bacterial foraging algorithm (BFA) and ant colony algorithm (ACO), the hybrid BFA-ACO algorithm is established by combining the two, and the replication and convergence operations of bacterial foraging algorithm are introduced into the ACO algorithm to improve the convergence speed and global convergence ability of the algorithm. The hybrid algorithm is then used to optimize and solve the path optimization model of hazardous materials vehicles and compared with the classical algorithms Genetic Algorithm (GA) and ACO for solving the path of dangerous materials vehicles. A comparison of the optimization results reveals that optimizing the model by bacterial foraging-ACO algorithm is better than optimizing the model by a single algorithm. [ABSTRACT FROM AUTHOR]

This study focuses on the lateral safety of autonomous vehicles. The aim is to provide greater safety margins for the motion controller under hazardous conditions by improving the planning methods. The proposed lateral planning method employs a hierarchical structure. First, a graph search method that considers road curvature is presented. This method benefits from the design of jump nodes and can generate a safe corridor in real-time by backtracking. Next, a concise and effective optimization problem is constructed in the Frenet frame. It is designed to optimize the path by incorporating the peak, integral, and differential of curvature into the objective function. By solving this problem using CasADi, the path within the safe corridor is obtained. Finally, the proposed planning method is combined with a model predictive motion controller and tested through simulation and experimentation on a real vehicle platform. This planning method has good scalability, adaptability, and real-time performance. Simulation and experimental results show that this method can help avoid exceeding lateral limits of road surface friction, improve path-following accuracy, and enhance the safety of autonomous vehicles in dangerous conditions. [ABSTRACT FROM AUTHOR]

This paper addresses the diversified needs of urban cold chain distribution and proposes innovative solutions based on storage type multi-temperature co-distribution and mechanical type multitemperature co-distribution modes. We present an electric vehicle path optimization model aimed at minimizing total costs, taking into account time-varying speed in accordance with urban traffic patterns. Additionally, a genetic algorithm is designed to solve the multi-temperature co-matching optimization path. The study's results reveal that the storage type multi-temperature co-distribution transport mode offers superior economic efficiency, product security, safety, and resource utilization. By comparing and analyzing the results of model solving under different battery capacities, loads, and distributions speeds, the total cost of distribution is optimal when the battery capacity is 120 kWh, the maximum load is 100 kg, and the normal driving speed is 60 km/h. The mechanical multi-temperature co-distribution mode is optimal for the total cost of distribution at a battery capacity of 100 kWh, a maximum load of 100 kg, and a normal driving speed of 50 km/h. The study aims to provide reference significance for logistics companies when making route selection. [ABSTRACT FROM AUTHOR]

The core of the modern lightweight wind turbine blade may be a truss structure based on carbon-fiber rods connected to stiff ribs. A specialized robot manufactures these structures by spatially winding impregnated carbon fibers around pre-positioned ribs with cutouts for securing the fiber bundle. Quantity of the ribs, their positions, and positions of the rods can vary, making this type of blades well-suited for optimization. We present an optimization approach for designing the core of a lightweight wind turbine blade. Our approach involves dividing the task into two stages and utilizing multiple optimization algorithms at each stage. Proposed approach used the FUD, DAJA, and Evolution Strategy algorithms. To test the approach, an existing blade structure with known characteristics was used. Comparison of the resulting design with the original one reveals 53 percent smaller value of objective function. These results demonstrate the effectiveness of applying the proposed approach. New approach allows for the optimization of complex rod with ribs' structures that cannot be optimized using standard optimization method. This will hopefully result in a significant decrease of wind turbine production cost. [ABSTRACT FROM AUTHOR]

The current cold chain logistics management and distribution routes generally face problems such as environmental pollution and high costs. Therefore, the study proposed an improved fireworks-artificial bee colony for optimizing delivery paths. Firstly, the artificial bee colony was improved by using neighborhood search. Secondly, for the optimal solution, the fireworks explosion operator was used for the final result search. The benchmark function validation showed that the proposed algorithm performed better than other algorithms in solving optimal value, average value, and standard deviation. In the simulation analysis of a fresh enterprise in China, the total delivery cost was reduced by an average of 3.30% and 4.93% compared to other algorithms, considering carbon emission and not considering it, respectively. This proposed algorithm had more advantages in reducing quality loss, pollutant emissions, and CE. Therefore, this improved fireworks-artificial bee colony can effectively reduce the economic costs and environmental pollution of cold chain logistics distribution routes. This ensures economic benefits while considering social benefits, achieving the optimal path planning for logistics distribution. [ABSTRACT FROM AUTHOR]

In response to the problems of low efficiency, high cost, and serious environmental pollution faced by traditional logistics scheduling methods, this article introduced the Metaheuristic algorithm into intelligent logistics scheduling and environmentally sustainable development. This article took the Metaheuristic algorithm as the research object. It was based on an in-depth analysis of its core ideas and unique advantages, combined intelligent logistics scheduling with relevant theories and methods such as green environmental protection, and innovatively constructed an intelligent logistics scheduling model based on the Metaheuristic algorithm. This article experimentally compared the effects of different Metaheuristic algorithms on total driving distance, transportation time, fuel consumption, and carbon emissions. The experimental findings indicated that the ant colony optimization (ACO) algorithm in this article performed the best among them, and the performance of traditional algorithms and Metaheuristic algorithms was also tested in terms of performance. The findings indicated that the computational accuracy of the Metaheuristic algorithm reached 97%, which was better than the traditional 80%. Experimental results have shown that the Metaheuristic algorithm is an efficient and feasible method that can improve the efficiency of logistics scheduling and environmental sustainability. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cultivated land is the most important carrier of grain production. Quantity, quality, and utilization rate can also determine the production capacity of grain for national grain security. Among them, agricultural carbon emission has been one of the important sources of greenhouse gases. There is a complex logical relationship among land use, land use carbon reduction, and grain production under the strategic context of climate change response to the overall layout of ecological civilization. In response to the growing demand for grain, agricultural production factors in the short term have caused ecological pollution and carbon emissions in cultivated land. The demand for diversified agricultural products has also intensified the excessive investment of production resources, further deteriorating the ecological environment of cultivated land and carbon emissions. Therefore, this study aims to explore the relationship between cultivated land utilization and grain production in the context of carbon emission constraints, in order to reveal the complex logical relationship among the three elements of "soil-carbon-grain". The challenges were also proposed to sustainably utilize the cultivated land under the goals of grain security and carbon emission reduction. Furthermore, the optimal paths were constructed to promote the green transformation and upgrading of cultivated land use for better grain production. The results showed that: (1) A complex "soilcarbon-grain" factor system was obtained to form the logical connection between cultivated land utilization and grain production under carbon emission constraints. (2) The carbon reduction was implemented to utilize the cultivated land under current agricultural production. There were uncertain impacts on national grain security. But the ever-increasing grain production still resulted in a large amount of carbon emissions. (3) There was a better balance between cultivated land utilization and grain production under carbon emission constraints. A coordinated optimal path was established for the "soilcarbon-grain" elements from three aspects: cultivated land protection, carbon reduction, and grain production. The cultivated land resources were allocated to construct a carbon trading market for the structure of grain production. According to the current agricultural production, it was still feasible to coordinate the "Carbon Peaking and Carbon Neutrality" goals with grain security goals, although there was a high degree of uncertainty on carbon emissions that were reduced from cultivated land utilization. The proportion and quantity of input were optimized from the factors of grain production, in order to implement the "Trinity" protection system for the cultivated land, differentiated carbon reduction for cultivated land use, and stable support of agricultural funding. A mature trading market of agricultural carbon was established to innovate the green technologies of agricultural production. The carbon emissions were reduced from the cultivated land use for the national grain security, the carbon sequestration emission reduction potential of cultivated land, and grain production. It is very necessary to plan the spatial patterns of cultivated land using various policy tools, in order to promote the sustainable and synergistic development of low-carbon and green utilization of cultivated land and national food security. [ABSTRACT FROM AUTHOR]

The seriousness of vessel air pollution has forced the International Maritime Organization (IMO) to introduce a series of relevant laws and regulations. This paper proposes a monitoring scheme based on the synergistic operation of motherships and UAVs. This scheme innovatively adopts a harbor sea patrol vessel or the other official vessel (mothership) as the mobile power supply base for UAVs and realizes efficient and accurate monitoring of vessel air pollution in the pre-monitored area at sea by carrying multiple UAVs. The focus of this paper is on the path optimization problem for multi-UAV collaboration with mothership (MUCWM) monitoring, where the objective is to minimize the total monitoring time for MUCWM. The following three main aspects are studied in this paper: (1) multi-UAV monitoring path optimization; (2) the collaboration mechanism between the mothership and multiple UAVs; and (3) mothership traveling path optimization. In order to effectively solve the above problems, this thesis constructs a path optimization model for multi-UAV collaborative mothership monitoring of air pollution from vessels in port waters; solves the model using the improved adaptive differential evolution (IADE) algorithm; and verifies the effectiveness of the model and the algorithm by using the position data in the Automatic Identification System (AIS) of vessels in Ningbo Zhoushan Port. Through the performance comparison and sensitivity analysis of the algorithm, it is confirmed that the algorithm can effectively solve the path planning problem of the collaborative operation between the mothership and multiple UAVs. The research results in this paper not only help to reduce the air pollution level of harbor vessels and improve the efficiency of sea cruising but also play an important supporting role in the enforcement of relevant emission regulations. [ABSTRACT FROM AUTHOR]

In response to the issue of travel comfort in electric vehicle path planning, an electric vehicle path planning model considering traveler comfort was constructed. Based on the Dijkstra algorithm, path planning simulations were conducted considering various influencing factors such as time, distance, electric vehicle (EV) energy consumption, and traveler comfort. The effectiveness of the model and algorithm was verified through corresponding test cases, and an example calculation and analysis was conducted in surrounding area of Nanning Station in Nanning City. The results indicate that the optimal path model constructed has increased travel distance, travel time, and travel energy consumption by 3.1%, 13.3%, and 4.6% compared to conventional path planning models, but has improved comfort by 9.4% . Therefore, the study can provide reference for research on improving the quality of electric vehicle travel. [ABSTRACT FROM AUTHOR]

To solve the problem of collaborative search mission planning (CSMP) for a heterogeneous unmanned aerial vehicle (UAV) swarm for dynamic targets, this paper proposes a new CSMP scheme for a heterogeneous UAV swarm. First, a new polar coordinate system motion method is used to establish the UAV motion model and decision input solution model, which effectively solves the path‐unified coding problem of a heterogeneous UAV swarm. Then, in the mission area, a dynamically updated multisearch situation map model is established. Finally, to improve the global searching capability of a UAV, an improved path optimization algorithm PSO‐Rolling Horizon Control (PSO‐RHC) is designed. Multiple Monte Carlo simulations are performed for three time‐sensitive moving target types and three constraint types. The simulation results show that the task execution efficiency indexes of the proposed scheme for the decision input solution model, pheromone update mechanism, and optimization algorithm are improved by 188%, 72%, and 102%, respectively, and the overall performance is improved by 227%. [ABSTRACT FROM AUTHOR]

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The data management system of health cloud authentication plays an important role in the optimization of logistics vehicle routing. It can not only help logistics vehicles choose the best distribution path, but also save time and cost and improve economic efficiency. At present, logistics has not yet formed a complete service system. High distribution costs and low distribution efficiency limit the development of the entire logistics. The reduction of logistics costs and the improvement of distribution efficiency have become the top priorities of the society. The optimization of the distribution route is the key to cost saving and distribution logistics. It is particularly important to study and optimize the distribution route, because the distribution route affects the logistics transportation efficiency and the loss cost during transportation. Therefore, this paper adjusted the scheduling system of logistics vehicles through a distributed intelligent system, and optimized the path of logistics vehicles according to the improved genetic algorithm, thereby reducing the transportation cost of logistics and improving the efficiency of logistics distribution. This article first explains the definition, classification, and main components of the delivery vehicle routing problem. Then, using cloud authentication path optimization, a distributed intelligent system is constructed. Finally, an improved ant colony algorithm is used to analyze and study the distance constraints of vehicles. By improving the ant colony algorithm, it can be seen that the optimized path pheromone concentration and the optimized sub-function have gradually increased with time. The mean pheromone concentration was 40 %, and the seventh day was 15 % higher than the first. The mean value of the optimized subfunction was 0.34 %, and the seventh day was 20 % higher than the first. The distribution cost and distribution efficiency of the optimized logistics vehicle distribution path were much higher than those of the traditional logistics distribution path. Moreover, the distribution cost of the logistics distribution path was 9 % lower than the traditional one, and the distribution efficiency was 13 % higher. The average smoothness of the optimized logistics path is about 90 %, and the seventh day is 11 % higher than the first day. The average fitness of the optimized logistics path is 88 %, and the seventh day is 14 % higher than the first day. In a word, the data management system can uniformly schedule logistics vehicles and improve the efficiency of distribution. [ABSTRACT FROM AUTHOR]

Ball-end milling cutters are commonly used for precision processing of complex curved parts in CNC systems. However, the milling process often experiences chatter, leading to final surface damage. To solve the problem, the method of automatically adjusting the tool posture was proposed to avoid chatter problems during the milling process. Frequency response function varies along the processing path. The frequency domain control equation for ball-end milling cutter machining under different operating conditions was established in the feed coordinate system, and the stability of the system at the cutting point can be quickly determined by Nyquist. In order to accurately solve the control equation, a method for solving the Cutter Workpiece Engagement (CWE) boundary under different tool postures was designed. The feasible region for the tool axis was searched based on geometric and stability constraints at each original tool path position. In the feasible domain, the tool axis path was optimized and the tool position file was updated with the constraint of machine tool rotation axis smoothness. Milling experiments were conducted on AL7050-T745 workpiece. From the simulation and experimental results, it can be concluded that milling width and tilt angle have significant impact on the milling process. The method proposed in this article has been experimentally validated in a five-axis ball-end milling experiment. [ABSTRACT FROM AUTHOR]

In some large buildings, the efficiency of path planning algorithms for mobile robots still faces great challenges. To deal with this problem, a path planning algorithm combining Voronoi region segmentation and path optimization (VSO) is proposed to achieve fast path planning in large-scale indoor scenario. The algorithm uses the generalized Voronoi graph (GVG) to construct the topological map from the environmental map, and then the initial heuristic path can be obtained quickly on the topological map. By sampling in the area around the initial path, the over-exploration of the workspace can be reduced. What's more, the algorithm selects waypoints to divide the sampling area into multiple sub-regions, and search paths in the sub-regions in parallel to further reduce the search space and improve the search efficiency. Finally, the path obtained from each sub-region is connected as the solution of the overall problem, and the optimization algorithm is used to smooth the final path. Simulation and robot experiments are conducted to verify the practicability and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

To address the difficulties in logistics distribution in remote rural areas, a systematic planning of agricultural logistics distribution for UAV distribution was performed in this study. Considering the limit of cruising range, from the perspective of green routing, a multi-package distribution path planning model of UAV agricultural logistics considering the limitation of cruising range of unmanned aerial vehicle (UAV) was established to minimize total energy consumption. The task allocation was conducted according to the actual number of UAVs. Meanwhile, a mixed integer nonlinear programming model of task allocation was established. The improved ant colony algorithm was employed to solve the problem. The core idea was to exchange the pheromones of each ant subgroup, and subsequently to apply the insertion-based heuristic method and crossover and inversion operations to optimize the path. For the cases of remote areas in western China, the agricultural UAV distribution path planning considering the mileage limit contributes to saving resources and obtaining the lowest energy consumption distribution path. In addition, for the problem of agricultural logistics distribution path planning considering the mileage limit of UAV, the improved ant colony algorithm exhibits higher solution accuracy than the traditional ant colony algorithm. [ABSTRACT FROM AUTHOR]

The farmland in the southwestern mountainous areas of China is mostly hilly terrain with multiple obstacles, and traditional manual spraying operations are time-consuming and laborious. The use of agricultural plant protection unmanned aerial vehicle (UAV) can reduce the problem of high manual operation costs. To solve the problem of optimizing the spraying operation path of plant protection UAVs, this study focused on the complex agricultural environment in the southwestern mountainous areas of China. First, a 2D agricultural map model with multiple obstacles was constructed using MATLAB. Second, the optimization requirements for job paths were analyzed, and a path optimization model based on the grid graph method was studied, aiming to shorten the total flight distance and reduce the number of paths. By applying the genetic algorithm, efficient optimization of the spraying path of plant protection UAV was carried out. Simulation verification showed that the optimized path significantly shortened the flight distance, accelerated convergence speed, and effectively avoided local repeated paths, thereby greatly improving the spraying efficiency of plant protection UAV. [ABSTRACT FROM AUTHOR]

As people's environmental awareness improves, the development of green economy has become an important goal for countries to achieve sustainable development. The extensive generation of greenhouse gases during the cold chain logistics and distribution process poses a significant challenge to the advancement of the green economy. By utilizing an enhanced genetic algorithm (GA), this research examines the expenses associated with distributing fresh agricultural products via the cold chain and constructs a model for optimizing the path of distribution. Simulation experiments show that the total cost of this model constructed in the study is 2323.94 RMB, which is less than 2623.35 RMB of the traditional path planning model, and the carbon emission is 85 kg less. It proves that the path optimization model constructed by the research has high economic and environmental benefits, and the designed and planned path is more in line with the needs of green economy than the traditional path, which has the potential to minimize carbon emissions during the fresh agricultural product cold chain distribution process while simultaneously promoting sustainable development.

Slope instability, driven by factors such as rainfall, seismic activity, and human influence, presents a pervasive hazard worldwide. Timely assessment of slope stability and accurate identification of its most critical slip surface are important for slope safety early warning and management. Currently, for the slope reliability analysis by the strength reduction method (SRM), the critical failure path is approximately determined through visualization techniques, which is not sufficiently precise. Therefore, this study proposes a critical failure search method based on SRM for slope reliability analysis, aiming to accurately identify the critical slip surface. The critical failure path is considered as the path with the maximum plastic dissipative energy density (PDED) and is searched in the constructed weighted graph based on the dissipated energy of the slope. This proposed method is further applied with an engineering slope to find the critical failure path and assess its reliability during and after construction. The searched critical failure path lies within the approximate range obtained through conventional visualization methods. Finally, a reliability prediction model consisting of time, rainfall, and deformation component is further constructed, which allows rapid estimation of the slope reliability through available monitor data. The results of reliability analysis indicate that construction disturbances have a significant impact on slope stability, along with other factors such as rainfall and creep.

Open-pit mines, characterized by complicated environmental conditions and unstructured roads, pose challenges for the path planning of mine trucks, marked by inefficiency due to long-distance search and the need to adhere to the left-hand traffic rules common in mining settings. This paper introduces an optimization algorithm for adaptive dual-layer search path planning based on the left-hand traffic rules in mining areas, aiming to improve the navigation performance of mine trucks within these areas while ensuring the path safety and the real-time planning. The upper-layer search, employing the rapidly-exploring random tree (RRT) algorithm and accommodating the environmental characteristics of mining areas, features adaptive adjustments of search step sizes, achieving efficient searches with larger step sizes in open areas, while detailed searches with smaller step sizes in narrow or winding areas. Moreover, the search process is further optimized by setting range constraints for left-hand driving, to prevent potential vehicle conflicts and facilitate rapid convergence of path search. The lower-layer search incorporates a hybrid A* algorithm, effectively narrowing search space and enhancing the real-time nature of path planning through a reward and penalty mechanism for path selection. Furthermore, Reeds-Shepp (RS) curve and an improved cubic spline curve are applied to smooth the resultant path, not only optimizing path curvature but also ensuring that the mine truck reaches the target location in an optimal posture. Further optimization through the gradient descent method enables the efficient generation of safe and smooth paths. Experimental results showed a 14.8% reduction in path generation time and a 0.011 m-1 decrease in path curvature, demonstrating a significant enhancement in the navigation efficiency and safety during autonomous truck driving in mining areas.

ABSTRACTThe Vertical Takeoff and Landing Aircraft (VTOL) is capable of both low-speed hovering and high-speed flight. This paper proposes an autonomous motion planning and anti-wind disturbance control algorithm for the VTOL, implemented spatial and temporal joint motion planning for VTOL, under the limitation of maximum flight speed of the aircraft, VTOL can achieve fully autonomous flight in complex environments, which can realize the autonomous flight of a single aircraft in a complex and dense environment, achieve real-time perception and avoidance of static and dynamic obstacles. The information generated by motion planning includes the position, velocity, and acceleration information of the aircraft, and proposes a wind disturbance estimation and active control strategy in a wind disturbance environment, accurate estimation of wind disturbance is achieved through a state observer, and the wind disturbance was suppressed through nonlinear error feedback control law, implemented wind disturbance estimation and suppression for aircraft in wind disturbance environments, and achieved complete autonomous motion planning and trajectory tracking of VTOL in a complex and wind disturbance environment. Our work can provide a basis for the collaborative control of multiple UAVs.

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The optimization of the cutting path of the sample can effectively reduce the cutting time, thereby improving the production efficiency of numerical control processing. This paper comprehensively considers the impact of the cutting order and the position of the knife entry point on the cutting path, converts the cutting path problem into a type of traveling salesman problem (TSP), and proposes an improved genetic-particle swarm optimization algorithm. The selection mechanism of the algorithm combines the elitist retention strategy and roulette wheel selection method to accelerate the search for the optimal solution; the mutation strategy designs a linear decreasing mutation rate, which enhances the global search ability; at the same time, introduces the ant colony optimization algorithm to process the fitness function, adjusts the population evolution difference, and speeds up the optimization process. Through this hybrid algorithm, the cutting order of the sample can be quickly optimized, and the nearest neighbor algorithm is used to determine the position of the knife entry point. Tests are conducted on clothing patterning charts and standard examples. Compared with several commonly used algorithms, experimental results verify the feasibility and effectiveness of this algorithm. [ABSTRACT FROM AUTHOR]