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This paper presents a novel method for fabricating three-dimensional (3D) microstructures of cobalt–platinum (Co-Pt) permanent magnets using a localized electrochemical deposition (LECD) technique. The method involves the use of an electrolyte and a micro-nozzle to control the deposition process. However, traditional methods face significant challenges in controlling the thickness and uniformity of deposition layers, particularly in the manufacturing of magnetic materials. To address these challenges, this paper proposes a method that integrates machine learning algorithms to optimize the electrochemical deposition parameters, achieving a Co:Pt atomic ratio of 50:50. This optimized ratio is crucial for enhancing the material’s magnetic properties. The Co-Pt microstructures fabricated exhibit high coercivity and remanence magnetization comparable to those of bulk Co-Pt magnets. Our machine learning framework provides a robust approach for optimizing complex material synthesis processes, enhancing control over deposition conditions, and achieving superior material properties. This method opens up new possibilities for the fabrication of 3D microstructures with complex shapes and structures, which could be useful in a variety of applications, including micro-electromechanical systems (MEMSs), micro-robots, and data storage devices.

This article addresses the common issues of insufficient computing power and path congestion for automated guided vehicles (AGVs) in real-world production environments, as well as the shortcomings of traditional path-planning algorithms that mainly consider the shortest path while ignoring vehicle turning time and stability. We propose a secondary path-planning method based on an improved directed weighted graph theory integrated with an ROS. Firstly, the production environment is modeled in detail to identify the initial position of the AGV. Secondly, the operational area is systematically divided, key nodes are selected and optimized, and a directed weighted graph is constructed with optimized weights. It is integrated with the ROS for path planning, using the Floyd algorithm to find the optimal path. The effectiveness and superiority of this method have been demonstrated through simulation verification and actual AGV operation testing. The path planning strategy and fusion algorithm proposed in this article that comprehensively considers distance and angle steering are simple and practical, effectively reducing production costs for enterprises. This method is suitable for logistics sorting and small transport AGVs with a shorter overall path-planning time, higher stability, and limited computing power, and it has reference significance and practical value.

One of the keys in time-dependent routing is determining the weight of each road network link based on traffic information. To facilitate the estimation of the road’s weight, Global Position System (GPS) data are commonly used in obtaining real-time traffic information. However, the information obtained by taxi-GPS does not cover the entire road network. Aiming at incomplete traffic information on urban roads, this paper proposes a novel fuzzy inference method. It considers the combined effect of road grade, traffic information, and other spatial factors. Taking the third law of geography as the basic premise, that is, the more similar the geographical environment, the more similar the characteristics of the geographical target will be. This method uses a Typical Link Pattern (TLP) model to describe the geographical environment. The TLP represents typical road sections with complete information. Then, it determines the relationship between roads lacking traffic information and the TLPs according to their related factors. After obtaining the TLPs, this method ascertains the weight of road links by calculating their similarities with TLPs based on the theory of fuzzy inference. Aiming at road links at different places, the dividing – conquering strategy and globe algorithm are also introduced to calculate the weight. These two strategies are used to address the excessively fragmented or lengthy links. The experimental results with the case of Newcastle show robustness in that the average Root Mean Square Error (RMSE) is 1.430 mph, and the bias is 0.2%; the overall RMSE is 11.067 mph, and the bias is 0.6%. This article is the first to combine the third law of geography with fuzzy inference, which significantly improves the estimation accuracy of road weights with incomplete information. Empirical application and validation show that the method can accurately predict vehicle speed under incomplete information.

In order to optimize the development of automated logistics, this paper proposes to build a mobile robot based on ROS to carry out intelligent autonomous navigation and distribution in the warehouse environment and to use intelligent robots to improve the efficiency of logistics and distribution. Analyze logistics robots’ localization methods and propose a SLAM algorithm to obtain environmental map information. Improve the Manhattan distance function to propose a global path planning A* algorithm based on the two-way search of the initial point and the target point, as well as a local path planning DWA algorithm to seek the best path. Collecting robot scheduling principles and scheduling conflict types, a genetic algorithm was selected for workshop AGV scheduling planning. Set up an experimental environment to analyze the optimal performance of the DWA algorithm. Combined with the intelligent warehouse goods sorting environment, analyze the planning and scheduling strategy for the goods sorting path of the ROS robot cluster. In the experiment, it is concluded that the larger the value of parameter β of the trajectory evaluation function is, the longer the time to be consumed by the calculation of the DWA algorithm is, and the designed path distance will be farther. When α =0.02 and β =0.2, the DWA algorithm reaches the best. Combined with the actual task scheduling path of the mobile robot cluster, it can be considered to be able to complete the autonomous navigation experiment in the whole warehouse environment, which is in line with the demand for automated logistics use.

3D printing technology has subverted the traditional process manufacturing process. The main algorithms used in the model data processing is a key step that affects the accuracy of the model printing. This paper focuses on the 3D printing path planning algorithm. Analyze the realistic requirements for the coexistence of artistry and craftsmanship in 3D printing products and develop a design scheme. Visualize the 3D printing path planning suggested for contour information in the partition linear scanning corner filling algorithm. Set the 45° scanning direction of the X-axis, adjust the fitted spline curve function reciprocally, and even out the path of the printer in the corner of the partitioned scanning path. Improve printing accuracy. Combined with the experimental equipment to set up the 3D printer’s operating code, the proposed Hilbert filling curve method, combined with three groups of comparison tests, verified the accuracy improvement benefits of the composite scanning algorithm in this paper. Taking the experimental data of the uncovered box group as an example, the composite scanning algorithm of this paper changes the maximum warpage from 0.23mm to 0.16mm, which is an improvement of 30.43%, and the average warpage from 0.1675mm to 0.095mm, which is an improvement of 43.28%. The reduction and optimization of warpage not only shows the improvement of accuracy of 3D printing technology but also optimizes the shape of 3D products.

College English is a very flexible and important subject that needs to be constantly innovated. For the shortcomings of traditional teaching methods, this paper constructs a teaching behavior tree based on reinforcement learning. By analyzing the direction of the behavior tree, using the path planning algorithm to propose better teaching strategies, combined with the Q learning algorithm, the teaching theme and the environment of the information are timely updated in the Q value table to update the teaching behavior selection strategy, and ultimately obtain the teaching behavior tree. The reinforcement learning algorithm based on Q -function interacts with the environment to directly train the method of generating the behavior tree to check whether the teaching behavior tree reaches the optimal standard. Combine reinforcement deep learning with English teaching to introduce a new approach to teaching English. The results show that on the diversified evaluation behavior, teachers of 985 colleges scored significantly higher than teachers of 211 colleges, with a score of 3.05 for teachers of 985 colleges and 2.68 for teachers of 211 colleges. Among the two teaching factors, the F-value of the diversified evaluation is 4.601, and the F-value of the personalized teaching is 3.596, which indicates that both of them have a greater influence on the students and they are able to improve the English proficiency of the students. This study provides a new reference direction for college English teaching, which can effectively improve students’ practical skills.

The most challenging task in the wiring harness assembly process is the placement and connection of cables on harness boards. This task remains difficult to automate because it requires dual-arm manipulation to collaboratively grasp the harnesses. In this study, we propose a dual-arm manipulation system and a path-planning framework to automate the wiring harness task. The proposed framework enables the cooperative operation of dual-arm manipulator and plans efficient paths, while considering the kinematic constraints and obstacle avoidance conditions. The framework obtains paths in two steps: 1) the configuration of the robot arms is computed using an inverse kinematics solver; and 2) when the distance between obstacles and robots falls below a certain threshold, a sampling-based algorithm plans paths that satisfy the kinematic constraints and obstacle avoidance conditions. The merits of the framework include a significant reduction in the task time compared to existing methods. This was achieved by efficiently exploring the workspace within the constrained conditions using the proposed algorithm instead of determining the path-planning conditions throughout the entire workspace. The effectiveness of the proposed framework was validated through simulations using redundantly actuated robots with multiple DoF.

In the machining of parts, tool paths for complex cavity milling often have different generation options, as opposed to simple machining features. The different tool path generation options influence the machining time and cost of the part during the machining process. Decision makers prefer tool path solutions that have fewer blanking lengths, which means that the machining process is more efficient. Therefore, in order to reduce costs and increase efficiency, it is necessary to carefully design the tool path generation for the features to be machined on the part, especially for complex cavity milling features. However, solutions to the problem of optimal design of tool paths for complex cavity milling features have not been well developed in current research work. In this paper, we present a systematic solution for complex cavity milling tool path generation based on reinforcement learning. First, a grid converter is executed for converting the 3D geometry of the cavity milling feature into a matrix of planar grid points recognisable by the program, set according to the cutting parameters. Afterwards, the tool path generation process is refined and modelled as a Markov decision process. Ultimately, a tool path generation solution combining the A* algorithm with the Q-learning algorithm is executed. The agent iterates through trial and error to construct an optimal tool path for a given cavity milling task. Three case experiments demonstrate the feasibility of the proposed approach. The superiority of the reinforcement learning-based approach in terms of solution speed and solution quality is further demonstrated by comparing the proposed approach with the evolutionary computational techniques currently popular in research for solving tool path optimisation design problems.

Path planning algorithms are widely used in various motion planning tasks, such as robot motion and autonomous driving.So far, many excellent path planning algorithms have been proposed for applications in different fields.For a specific task environment, choosing the appropriate path planning algorithm can plan a better path that satisfies the constraints more efficiently.Based on the results of swarm intelligence, the adaptability and path planning efficiency of rapidly-exploring random tree (RRT) path planning algorithm and its variants RRT-Star path planning algorithm and RRT-Star-Smart path planning algorithm under different task environments were studied.Using genetic programming algorithm as a framework to design a system, which could automatically analyze the map features of the current environment and combine the characteristics of RRT path planning algorithm and its variants to generate new path planning algorithms that were more suitable for the current environment.The generated path planning algorithm can efficiently plan a feasible path from the starting point to the target point.

In order to meet the obstacle avoidance requirements of unmanned agricultural machinery in operation, it is necessary to plan a path to avoid obstacles in real time after obstacles are detected. However, the traditional path planning algorithm does not consider kinematic constraints, which makes it difficult to realize the plan, thus affecting the performance of the path tracking controller. In this paper, a real-time path planning algorithm based on particle swarm optimization for an agricultural machinery parametric kinematic model is proposed. The algorithm considers the agricultural machinery kinematic model, defines the path satisfying the kinematic model through a parametric equation, and solves the initial path through the analytic method. Then, considering the constraints of obstacles, acceleration, and turning angle, two objective functions are proposed. The particle swarm optimization algorithm is used to search the path near the initial path which satisfies the obstacle avoidance condition and has a better objective function value. In addition, the influence of the algorithm parameters on the running time is analyzed, and the method of compensating the radius of the obstacle is proposed to compensate the influence of the discrete time on the obstacle collision detection. Finally, experimental results show that the algorithm can plan a path in real time that avoids any moving obstacles and has a better objective function value.

In modern transportation, traffic congestion has become an urgent problem in large and medium-sized cities. In smart transportation systems, it is an effective solution to design load balancing path planning algorithms that can dynamically adapt to traffic conditions in order to avoid congestion. In this work, a traffic path planning algorithm based on data prediction (TPPDP) is proposed to find the path with the shortest travel time, which is built on a predictive model based on historical traffic data and current traffic information. Furthermore, a path planning algorithm based on data prediction with load balancing (TPPDP-LB) is also proposed, which combines the predicted information and the number of concurrent requests to achieve the path with shortest travel time while maintaining global load balancing. A specific distributed computing framework for TPPDP-LB algorithm is designed to reduce the runtime of the algorithm. The simulation results proved that both TPPDP and TPPDP-LB algorithms have the advantage of shortest travel time, and TPPDP-LB algorithm achieves load balancing of computing. It is also proved that the distributed computing framework designed for TPPDP-LP algorithm can effectively reduce the runtime of system as well as keep the accuracy of algorithm.

Aiming at the problem of the wide range and great difficulty in the future of battlefield airspace control, based on the unique advantages of an airspace grid system in an airspace grid representation and time–space binary computing, this paper designs a pre-clustering method for mission airspace based on airspace location correlation under the condition of future large-scale air combat missions in order to realize the block control of battlefield airspace. This method reduces the whole 3D battlefield space projection to a 2D plane and regards the task airspace projection as “obstacles” in the task area; Multi-A * algorithm is used to generate the airspace clustering line surrounding the task airspace, and the airspace association clustering problem is transformed into a multiple “start point-end point” path planning problem with autonomous optimization. Through the experiment, it was found that clustering the airspace can effectively improve the management and control efficiency of large-scale battlefield airspace.

One of the keys in time-dependent routing is determining the weight for each road network link based on symmetrical and complete traffic information. To facilitate travel planning considering traffic situations based on historical global position system (GPS) trajectory data which uncover the whole road network, this paper proposes a fuzzy random forest-based road section data estimation method, which uses the third law of geography as the core idea. For different time periods, road grade, tidal lane, proximity to infrastructure (main places that affect traffic, such as schools, hospitals), and accident road sections were selected as indicators that influence the traffic. The random forest algorithm is used to build the mapping relationship between attribute data with average traffic which is obtained based on GPS data. Subsequently, the fuzzy reasoning method is used to obtain the weight of road links missing traffic information by calculating their similarities with typical road section samples. Using the road network of Suzhou City as an example, the proposed method was used to analyze estimate the average driving speeds of road sections with missing traffic information for different time periods. Experimental results show that this method can effectively avoid congested road sections and obtain high-speed travel routes.

Low-cost robots are characterized by low computational resources and limited energy supply. Path planning algorithms aim to find the optimal path between two points so the robot consumes as little energy as possible. However, these algorithms were not developed considering computational limitations (i.e., processing and memory capacity). This paper presents the HCTNav path-planning algorithm (HCTLab research group’s navigation algorithm). This algorithm was designed to be run in low-cost robots for indoor navigation. The results of the comparison between HCTNav and the Dijkstra’s algorithms show that HCTNav’s memory peak is nine times lower than Dijkstra’s in maps with more than 150,000 cells.