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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Navigating narrow restrooms is challenging for the elderly using wheelchairs or walkers (EUWW). Existing research focuses on the convenience of furniture use in the restroom and overlooks the convenience of mobility between furniture pieces. However, the floor plans of accessible restrooms determine the mobility path of EUWW, affecting mobility convenience. Thus, to improve mobility, floor plans should be evaluated to shorten movement distances and reduce bends. Experiments in constructed environments were expensive. This study instead proposes a novel path-planning based method for accessible restroom floor plan evaluation, simulating mobility paths in a virtual environment, and evaluating how the floor plans of accessible restrooms affect mobility and convenience. The results showed that: (1) wheelchair users, who approach the toilet from the rear, and walker users find opposite wall toilet-sink arrangements convenient. While Wheelchair users, who approach the toilet from the side or front, find it convenient if the toilet and sink are situated on different walls at a 90° angle. Toilets and sinks on the same wall are inconvenient. (2) Corner sinks and toilets reduce EUWW's turning angles. (3) The farther the sink is from the side wall, the more convenient it is for EUWW. (4) The 45° side approach is the most convenient for moving, followed by the frontal approach, and lastly the 90° approach. Theoretically, this study uses path planning algorithms, simulating the movement of EUWW with varying capabilities and offering a new perspective for barrier-free architectural design. Practically, this study provides recommendations for optimizing accessible restroom floor plans. [ABSTRACT FROM AUTHOR]

Skin, as the crucial component of aircraft, should be milled along the edges of the frame to ensure the mounting accuracy. Traditional skin milling uses visual measurement and offline planning to generate a machining path that corresponds to the overall shape of the frame edge. To enhance adaptability and processing efficiency, an online planning method is proposed. However, it imposes greater demands on the computational efficiency, accuracy, and stability of the algorithm. In this paper, a real-time path planning method based on frame feature extraction and chronological feature optimization is proposed for the milling of attached frame-skin. The method uses a line laser to scan the surface features of both skin and frame structures, facilitating real-time extraction of feature structures. Periodic calculations of position and posture are conducted based on the time series, and a non-fitting method is used to generate a smooth and high-precision machining trajectory. The efficacy in achieving real-time path planning for the milling module is verified by experiments on prototypes of aircraft skin. The algorithm exhibits a duration of less than 8 ms in a run cycle, while maintaining accuracy within ± 0.5 mm. • Propose a real-time extraction method of the position and posture characteristics of the attached frame and skin structure. • Propose a path planning method using chronological features to improve path point continuity and establish a processing path. • The algorithm achieved a path planning period of less than 8 ms and maintained tracking path accuracy within ±0.5 mm. [ABSTRACT FROM AUTHOR]

This paper proposes the shortest path optimization algorithm for domestic and overseas e-commerce logistics based on a bilateral search method. This paper uses the logistics distribution route optimization algorithm based on the shortest path to set the collaborative parameters. Then, it builds an adaptive optimization model for the grid planning of domestic and overseas e-commerce logistics path. The route is optimized. Then, the PSO and genetic algorithm are integrated to establish the logistics path planning model of domestic and overseas e-commerce. The superiority of the proposed route optimization algorithm in domestic and overseas e-commerce logistics distribution is verified through simulation experiments. This algorithm has high spatial positioning efficiency and high transportation efficiency. [ABSTRACT FROM AUTHOR]

With the continuous development of automated driving, the path planning algorithm of unmanned vehicles has been continuously innovated. First, some basic concepts of path planning are briefly introduced. Secondly, starting with the classification idea of path planning algorithm based on graph structure and sampling, the paper introduces four typical basic algorithms: Dijkstra algorithm, A* algorithm, PRM algorithm and RRT algorithm. In addition, the Apollo algorithm, which has been studied most at present, is also introduced. The principle and application scenario of each algorithm are summarized. Finally, several new algorithms and their development prospects are introduced. [ABSTRACT FROM AUTHOR]

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.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

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. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

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.

Autonomous Mobile Robots' performance relies on intelligent motion planning algorithms. In autonomous mobile robots, sampling-based path-planning algorithms are widely used. One of the efficient sampling-based path planning algorithms is the Rapidly Exploring Random Tree (RRT). However, the solution provided by RRT is suboptimal. An RRT extension known as RRT* is optimal, but it takes time to converge. To improve the RRT* slow convergence problem, a goal-oriented sampling-based RRT* algorithm known as GS-RRT* is proposed in this paper. The focus of the proposed research work is to reduce unwanted sample exploration and solve the slow convergence problem of RRT* by taking more samples in the vicinity of the goal region. The proposed research work is validated in three different environments with a map size of 384*384 and compared to the existing algorithms: RRT, Goal-directed RRT(G-RRT), RRT*, and Informed-RRT*. The proposed research work is compared with existing algorithms using four metrics: path length, time to find the solution, the number of nodes visited, and the convergence rate. The validation is done in the Gazebo Simulation and on a TurtleBot3 mobile robot using the Robotics Operating System (ROS). The numerical findings show that the proposed research work improves the convergence rate by an average of 33 % over RRT* and 27 % over Informed RRT*, and the node exploration is 26 % better than RRT* and 20 % better than Informed RRT*. [ABSTRACT FROM AUTHOR]

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.

• This paper designs a time window-based initial solution scheme to improve the quality of the initial population. • We introduce a tabu search strategy to balance the global and local search performance of the algorithm. • We adopted a fitness evaluation mechanism to provide operational plans for cross-regional agricultural machinery operations. • The paper discusses the effects of different numbers of farmlands, machinery, and operation time window lengths on the model. This article aims to solve the problems of high cost, low efficiency, and task delay caused by the lack of scientific scheduling strategies during the agricultural harvest season. Considering the constraints of operation time windows, a multimachine multiobjective cross-regional collaborative operation scheduling model is constructed with the goal of minimizing the transfer distance of agricultural machinery and nonoperational scheduling costs. A multiobjective genetic algorithm (HTSMOGA) based on a hybrid time window priority rule and a tabu search strategy is designed. The initial population is generated by utilizing time window priority rules, genes with earlier operation start times are preferentially retained, and a mixed search strategy is introduced to avoid local optimal solutions. Experiments were conducted among 24 farms in a region of Hebei Province, and the results showed that, compared with other algorithms, the HTSMOGA achieved an average reduction of 17.18 % in the transfer distance of agricultural machine operations and 19.36 % in the nonoperational waiting time. Therefore, this study provides a rational and feasible solution for the cross-regional operation scheduling of agricultural machinery cooperatives and provides theoretical support for ensuring the timely completion of harvesting tasks and the cost savings and efficiency of agricultural machinery operations. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

Efficient and accurate path planning in a complex biological environment have become a challenge for nanorobot research. This paper first reviews the current path planning algorithms that can be used in the operation of nanorobots in the scientific community. The algorithms are mainly divided into four parts, including Dijkstra algorithm, A* algorithm, Rapidly-exploring Random Tree (RRT) algorithm, and Swarm Intelligence (SI) algorithm. In the application of SI, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are the most commonly used approaches to solve the path planning of nanorobot swarm. Then, their research status, advantages, and limitations are outlined in each section. The improvement of different algorithms in different environments is discussed while fully demonstrating that they are superior to other methods. Finally, the future research on optimal path planning is expected as the next step in high-precision control of nanorobot. This review aims to provide some ideas for the improvement of nanorobot performance and accelerate another leap of path planning technology in the field of nanomanipulation. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Intelligent Science & Technology is the property of Beijing Xintong Media Co., Ltd. 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.)

Energy management is a fundamental task and challenge of plug-in split hybrid electric vehicle (PSHEV) research field because of the complicated powertrain and variable driving conditions. Motivated by the foresight of intelligent vehicle and the breakthroughs of deep reinforcement learning framework, an energy management strategy of intelligent plug-in split hybrid electric vehicle (IPSHEV) based on optimized Dijkstra's path planning algorithm (ODA) and reinforcement learning Deep-Q-Network (DQN) is proposed to cope with the challenge. Firstly, a gray model is used to predict the traffic congestion of each road and the length of each road calculated in the traditional Dijkstra's algorithm (DA) is modified for path planning. Secondly, on the basis of the predicted velocity of each road, the planned velocity is constrained by the vehicle dynamics to ensure the driving security. Finally, the planning information is inputted to DQN to control the working mode of IPSHEV, so as to achieve energy saving of the vehicle. The simulation results show the optimized path planning algorithm and proposed energy management strategy is feasible and effective. [ABSTRACT FROM AUTHOR]

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.

The path planning of autonomous land vehicle has become a research hotspot in recent years. In this article, we present a novel path planning algorithm for an autonomous land vehicle. According to the characteristics of autonomous movement towards the autonomous land vehicle, an improved A-Star path planning algorithm is designed. The disadvantages of using the A-Star algorithm for path planning are that the path planned by the A-Star algorithm contains many unnecessary turning points and is not smooth enough. Autonomous land vehicle needs to adjust its posture at each turning point, which will greatly waste time and also will not be conducive to the motion control of autonomous land vehicle. In view of these shortcomings, this article proposes a new heuristic function combined with the artificial potential field method, which contains both distance information and obstacle information. Our proposed algorithm shows excellent performance in improving the execution efficiency and reducing the number of turning points. The simulation results show that the proposed algorithm, compared with the traditional A-Star algorithm, makes the path smoother and makes the autonomous land vehicle easier to control. [ABSTRACT FROM AUTHOR]

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.

In order to overcome the limitations of the A* algorithm in the autonomous control of electromagnetically actuated microrobots, this study introduces three modified path planning algorithms (A*-WAPP, A*-waypoints, A*-WAPP-waypoints) using the concept of Wall Avoiding Path Planning (WAPP) and waypoints. Through the autonomous driving experiment of an electromagnetically actuated microrobot, the three modified path planning algorithms based on A* and the original A* algorithm were evaluated using four performance measures. As a result, it was confirmed whether significant changes exist between the A* algorithm and the A*-based modified algorithms about the fitness for the autonomous driving environment of the electromagnetically actuated microrobot. First, compared to the path of the A* algorithm, A*-WAPP algorithm generated a stable path that dramatically reduced the collision between the microrobot and the obstacle. However, in the autonomous driving of the microrobot, A*-WAPP algorithm increased the driving distance and driving time. On the other hand, A*-waypoints algorithm showed a tendency in reducing the driving distance and driving time of the autonomous driving microrobot by simplifying the generated path, but still showed the collision problem between the microrobot and the obstacle. Finally, the path generated by the A*-WAPP-waypoints algorithm greatly increased the stability of the autonomous driving microrobot and showed great advantages of the decreases in the driving distance and driving time. In conclusion, it was confirmed that the proposed A*-WAPP-waypoints algorithm showed the best path generation results in the autonomous driving microrobot among the three A*-based algorithms. [ABSTRACT FROM AUTHOR]

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.

This study investigates the task assignment problem where a fleet of dispersed vehicles needs to visit multiple target locations in a time‐invariant drift field with obstacles while trying to minimise the vehicles' total travel time. The vehicles have different capabilities, and each kind of vehicles can visit a certain type of the target locations; each target location might require to be visited more than once by different kinds of vehicles. The task assignment problem has been proven to be NP‐hard. A path planning algorithm is first designed to minimise the time for a vehicle to travel between two given locations through the drift field while avoiding any obstacle. The path planning algorithm provides the travel cost matrix for the target assignment, and generates routes once the target locations are assigned to the vehicles. Then, a distributed algorithm is proposed to assign the target locations to the vehicles using only local communication. The algorithm guarantees that all the visiting demands of every target will be satisfied within a total travel time that is at worst twice of the optimal when the travel cost matrix is symmetric. Numerical simulations show that the algorithm can lead to solutions close to the optimal. [ABSTRACT FROM AUTHOR]

Copyright of AL-yarmouk Journall is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

This brief presents a trajectory planning algorithm for aerial vehicles traveling in 3-D space while avoiding obstacles. The nature of the obstacles can be, for example, radar detection areas, cooperating and non-cooperating vehicles, and so on. Thus, it is a complex trajectory planning problem. The proposed planner is based on the optimal rapidly exploring random tree (RRT*) algorithm. Artificial potential fields are combined with the RRT* algorithm to accelerate the convergence speed to a suboptimal solution by biasing the random state generation. The performance of this framework is demonstrated on a complex missile application in a heterogeneous environment. Indeed, since the air density decreases exponentially with altitude, the maneuverability of the aerial vehicle depending on aerodynamic forces also decreases exponentially with altitude. To face this problem, the shortest paths of Dubins-like vehicles traveling in a heterogeneous environment are used to build the metric. In the simulation results, this framework can find the first solution with fewer iterations than the RRT and the RRT* algorithm. Moreover, the final solution obtained within a given number of iterations is closer to an optimal solution regarding the considered criterion. [ABSTRACT FROM PUBLISHER]

In Wireless Sensors Networks (WSNs), researcher's primary focus is on energy preservation and prolonging network lifetime. More energy resources are required in case of remote applications of WSNs, where during active network stage some of the nodes die early, that shortens the network lifetime and decreases the stability of the network. It is mainly caused due to the non-optimal Cluster Heads (CHs) selection based on the single criterion and uneven distribution of energy. We propose a new distributed clustering protocol for both homogeneous and heterogeneous environments, named Optimized path planning algorithm with energy efficiency and extending network lifetime in WSN (OPEN). In the proposed protocol, we use timer value concept for efficient CH selection based on multiple parameters, e.g., residual energy, the average distance from its neighbors, and node density. Simulation results prove that OPEN performs better than the existing schemes regarding the network lifetime, throughput, and stability. The results explicitly explain the cluster head selection of OPEN protocol and efficient solution of uneven energy distribution problem. [ABSTRACT FROM AUTHOR]