Path Optimization Method Research Articles

Localization for Surface Microseismic Monitoring Based on Local Equivalent Path and Virtual Field Optimization Method

Localization for Surface Microseismic Monitoring Based on Local Equivalent Path and Virtual Field Optimization Method

A Fuzzy Variable H Strategy Based Ripple-Spreading Algorithm to Find the k Shortest Paths

Ripple-spreading Algorithm (RSA) is a relatively new, nature-inspired, multi-agent based method for path optimization. This paper demonstrates that by modifying the micro-level behaviors of nodes and ripples, RSA achieves good scalability for solving the k shortest paths problem (k–SPP). Initially, each node may generate k or more ripples to guarantee optimality. To improve computational efficiency for large-scale problems, we propose an approximate RSA (ARSA), where nodes generate no more than h ripples (1≤h<k). While this reduces optimality, it significantly improves efficiency. Further, we introduce a fuzzy variable H strategy, FVHSRSA, to strike a better balance between optimality and efficiency. The optimality/efficiency of ARSA may still suffer if it uses a constant h too small/large. This strategy allows nodes closer to the destination to generate more ripples, while nodes farther away use fewer ripples. By dynamically adjusting h, FVHSRSA achieves a better trade-off between optimality and efficiency. Comprehensive experiments on 4 common network categories validate the effectiveness and efficiency of FVHSRSA in solving the k–SPP.

Research on Mechanical Properties of a 3D Concrete Printing Component-Optimized Path by Multimodal Analysis

Three-dimensional concrete printing (3DCP) technology with solid wastes has significant potential for sustainable construction. However, the hardened mechanical properties of components manufactured using 3DCP technology are affected by weak interlayer interfaces, limiting the widespread application of 3DCP technology. To address the inherent limitations of 3DCP technology, conventional improvement strategies, such as external reinforcement and the optimization of material properties, lead to increased production costs, complex fabrication, and decreased automation. This study proposes an innovative spatial path optimization method to enhance the mechanical performance of 3D-printed, cement-based components. The novel S-path design introduces additional printed layers in the weak interlayer regions of the printed samples. This design improves the spatial distribution of fiber-reinforced filaments in continuous weak zones, thus enhancing the functional efficiency of fibers. This approach improves the mechanical performance of the printed samples, achieving compressive strengths close to those of cast samples and only a 20% reduction in average flexural strength. Compared to using a conventional printing path, the average compressive strength and flexural strength are improved by 30% and 55%, respectively, when the S-path layout is employed in 3DCP. Additionally, this method significantly reduces the anisotropy in compressive and flexural strengths to 26% and 28% of samples using conventional printing paths, respectively. Therefore, the proposed method can improve the mechanical properties and stability of the material, reducing the safety risks of printed structures.

Path Optimization of Two-Posture Manipulator of Apple Packing Robots

Automated packing is urgently needed in apple production. This paper proposes an improved genetic algorithm fused with an optimal parameter selection algorithm to optimize the two-posture manipulator working path of packing robots. First, the structure and working principle of the packing robot were designed. Second, the kinematics and packing paths of the two-posture manipulator were analyzed. Finally, the path optimization method for the two-posture manipulator was introduced. The method was based on the improved genetic algorithm by using a two-level coding and region crossover operator. The parameter values can be automatically determined by the optimal parameter selection algorithm. Ten repeated comparative tests show that the total packing time is 23.86 s under the working conditions of four grasping points and fourteen placing points. The optimal performance of the proposed algorithm is better than that of the traditional genetic algorithm, and the average optimization amplitudes are 14.63%, 15.42%, 16.24%, and 13.82% for 9-groove, 12-groove, 14-groove, and 16-groove trays, respectively. The proposed algorithm can effectively prevent the premature convergence problem of the traditional genetic algorithm and the optimization process instability problem, improve the range of optimization, and reduce the manipulator working time during packing.

An Agent-Based Method for Feature Recognition and Path Optimization of Computer Numerical Control Machining Trajectories.

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.

Experiment and path optimization on Large-Space Multi-Area crowd evacuation

Abstract In large spaces where there are multiple evacuation zones, the flow patterns during evacuation are intricate, leading to congestion in emergency scenarios, and affecting evacuation efficiency. To address this, we proposed an optimization method for crowd evacuation paths in large-space multi-area environment settings under fire scenarios by utilizing multi-agent simulation. Using Pathfinder software, disorganized evacuation simulations are conducted within expansive multi-zonal settings to pinpoint areas with inadequate evacuation capabilities. Subsequent evacuation experiments are performed for verification, facilitating adjustments to agent-based model parameters for evacuees in fire scenarios, and carrying out organized evacuation simulations. Through meticulous simulation analysis, we aim to optimize the most efficient evacuation paths under organized responses in large-space multi-area settings. Results indicate that in emergency conditions due to fire, the evacuation speeds reach 1.8 m/s, and significant reductions in total evacuation time are achieved through simulation optimization, with the time required for the slowest individuals to evacuate decreasing from 240.32 s to 209.32 s. Furthermore, the flow rate per second at congested exits diminishes from 2.01 persons/s to 1.39 persons/s, highlighting the efficacy of the proposed path optimization method.

Path Optimisation of Electric Vehicles for Collection and Delivery based on Intelligent Waste Bins

This paper proposes a dynamic electric vehicle path optimization method for intelligent garbage collection and transportation systems to address the increasing uncertainty of urban household waste and the carbon emissions caused by vehicles in traditional garbage collection and transportation processes. With the goal of minimizing total cost and maximizing garbage collection and transportation, adjust the soft time window in a timely manner based on whether the amount of garbage generated by the intelligent garbage bin reaches the collection and transportation threshold. Taking into account the position of the garbage bin to be cleared, the position of the garbage collection and transportation vehicle, the amount of electricity, and the loading capacity, dynamically adjust the vehicle path. Due to the fact that genetic algorithms require a long time to converge to an acceptable solution, this paper uses greedy genetic algorithms to comprehensively utilize their advantages in local search and global search capabilities to achieve faster convergence solutions. The research results indicate that compared with traditional static garbage collection and transportation modes, dynamic garbage collection and transportation schemes can reduce total costs and increase garbage collection and transportation volume, and reasonable planning of the path of electric collection and transportation vehicles can effectively reduce carbon emissions. Finally, the effectiveness and efficiency of the algorithm were verified through comparative analysis of numerical examples.

A Path-Planning Method for UAV Swarm under Multiple Environmental Threats

To weaken or avoid the impact of dynamic threats such as wind and extreme weather on the real-time path of a UAV swarm, a path-planning method based on improved long short-term memory (LSTM) network prediction parameters was constructed. First, models were constructed for wind, static threats, and dynamic threats during the flight of the drone. Then, it was found that atmospheric parameters are typical time series data with spatial correlation. The LSTM network was optimized and used to process time series parameters to construct a network for predicting atmospheric parameters. The state of the drone was adjusted in real time based on the prediction results to mitigate the impact of wind or avoid the threat of extreme weather. Finally, a path optimization method based on an improved LSTM network was constructed. Through simulation, it can be seen that compared to the path that does not consider atmospheric effects, the optimized path has significantly improved flightability and safety.

Continuous fiber path optimization in additive manufacturing: A gradient-based B-spline finite element approach

Additive manufacturing of continuous fiber enables the control of local material distribution and mechanical characteristics. This localized control is realized through fiber path generation and optimization, which is one of the core problems in the additive manufacturing of continuous fiber. Current fiber path generation and optimization methods mostly try to infill topology-optimized parts or align the fiber with the maximum stress directions, which may lead to sub-optimality or time-consuming computations. This paper provides a different approach to optimize the fiber path directly by parameterizing it with B-splines and manipulating its control points. A finite element model with B-spline fiber parameterization is established, with an analytical fiber-in-element probability. This framework streamlines the computation of local and global stiffness and mass matrices, enabling effective and efficient prediction of loading responses, stiffness, and natural frequencies. The probability framework also allows efficient calculation of the gradients of the stiffness and mass matrices, leading to a gradient-based fiber path optimization. The effectiveness and accuracy of both the finite element and optimization methods are verified with simulation and experiment case studies. Results demonstrate enhancements in stiffness and strength with less fiber usage compared to a commonly adopted principal stress direction method.

Genetic Algorithm-Based Framework for Optimization of Laser Beam Path in Additive Manufacturing

In this study, a genetic algorithm-based laser beam (LB) path optimization method is presented to improve laser-based additive manufacturing (LBAM). To emulate the LBAM process, LB irradiation of a thin metal substrate is applied. The LB path generation is formulated as the search for the optimal sequence of LB irradiation into the cells on the substrate that minimizes the fitness function, which is composed of two components, i.e., thermal fitness and process fitness. The thermal fitness is expressed by the average thermal gradient, and a simple thermal model is developed to simulate the effects of laser-induced heat input on the temperature distribution in the substrate. The process fitness regulates the suitability of the proposed LB path for the implementation of the LBAM process. In addition to standardized tool paths (i.e., raster, spiral, etc.), novel LB path generators are proposed to define the initial population of LB path solutions. To implement a genetic algorithm-based LB path optimization, a framework is proposed, and custom initialization, crossover, and mutation operators are developed for application in LBAM. The effectiveness of the proposed approach is demonstrated through a simulation case study aiming to identify LB paths that minimize the fitness function and thus provide more suitable LB path solutions with respect to the defined fitness function. Compared with the traditional trial-and-error LB path formulations, the proposed approach provides an improved and automated method for an efficient laser beam path selection in LBAM.

Global Dynamic Path Planning of AGV Based on Fusion of Improved A* Algorithm and Dynamic Window Method.

Designed to meet the demands of AGV global optimal path planning and dynamic obstacle avoidance, this paper proposes a combination of an improved A* algorithm and dynamic window method fusion algorithm. Firstly, the heuristic function is dynamically weighted to reduce the search scope and improve the planning efficiency; secondly, a path-optimization method is introduced to eliminate redundant nodes and redundant turning points in the path; thirdly, combined with the improved A* algorithm and dynamic window method, the local dynamic obstacle avoidance in the global optimal path is realized. Finally, the effectiveness of the proposed method is verified by simulation experiments. According to the results of simulation analysis, the path-planning time of the improved A* algorithm is 26.3% shorter than the traditional A* algorithm, the search scope is 57.9% less, the path length is 7.2% shorter, the number of path nodes is 85.7% less, and the number of turning points is 71.4% less. The fusion algorithm can evade moving obstacles and unknown static obstacles in different map environments in real time along the global optimal path.

Locating Transition States by Variational Reaction Path Optimization with an Energy-Derivative-Free Objective Function.

Locating transition states is essential for understanding molecular reactions. We propose a double-ended transition state search method by revisiting a variational reaction path optimization method known as the MaxFlux method. Although its original purpose is to add temperature effects to reaction paths, we conversely let the temperature approach zero to obtain an asymptotically exact minimum energy path and its corresponding transition state in variational formalism with an energy-derivative-free objective function. Using several numerical techniques to directly optimize the objective function, the present method reliably finds transition states with low computational cost. In particular, only three force evaluations per iteration are sufficient. This is confirmed on a variety of molecular reactions where the nudged elastic band method often fails. The present method is implemented in Python using the Atomic Simulation Environment and is available on GitHub.

An improved tracking method for bearing characteristic frequencies in the time-frequency representation of vibration signal

Rolling bearing is one of the most critical components for support and energy conversion in machines. The fault characteristic frequency (FCF) of time–frequency representation has received increasing attention in bearing diagnosis under variable speed conditions. However, FCF-extracted methods have poor adaptability to amplitude attenuation and noise interference due to local distortions or even transitions in the estimated instantaneous frequency ridges. Consequently, this paper proposes an improved FCF tracking method for variable speed bearing diagnosis. A strategy for locating distortion intervals is first developed using exponential smoothing and residual distribution. Subsequently, an advanced fast path optimization method, including peak map renewal and curve search optimization, is proposed to extract the ridges of interest. Finally, the probability density function of curve-to-curve ratios is designed to accurately identifying bearing faults. Simulation and experimental results demonstrate the effectiveness of the proposed method.

Novel Adaptive Path-Smoothening Optimization Method for Mobile Robots

A safe and smooth operating path is a prerequisite for mobile robots to accomplish tasks. Although the existing path optimization methods improve the smoothness of the planned path by introducing Bezier curve to locally optimize the path with regard to turning points, most of these methods manually select the position of control points and subjectively analyze the feasibility of the optimized path. It is argued unfavorably that it exhibits strong subjectivity and cumbersome selection process. To fill this gap, an adaptive path-smoothening optimization method is proposed in this study, which combines neural network, genetic algorithm, and Bezier curve to transform the path smoothing problem into an optimization problem. It rapidly determines the position of the optimal control point based on comprehension of constraints, e.g., path safety, curvature and kinematic restrains of the robot. The currently proposed method resolves the long-standing problems of strong subjectivity, cumbersome steps, and thus low efficiency in the selection process of control points, and lays the theoretical groundwork for smoothening the locus and path. To start with, according to the actual working conditions, the dataset corresponding to the position of the control point and the path deviation is constructed, and the neural network algorithm is used to solve the prediction model of the path deviation, so as to obtain the mapping relationship between the length and included angle of the control edge in the second-order Bezier curve and the path deviation. Subsequently, with reference to the prediction model of path deviation, a reliability evaluation function is formulated by comprehending multiple influential factors of mobile robot motion safety and path smoothness. The genetic algorithm is then introduced to detect the satisfactory control points in different environments. The currently proposed method is verified by experiments in different operating environments. The study results show that the currently proposed adaptive path-smoothening optimization method exhibits remarkably superior applicability and effectiveness compared to the currently prevailing methods. It demonstrates advantages of fast path planning, reduced path turning points, and desirable path smoothness. In addition, it can also ensure the safety of mobile robot along the planned path as availed by a pre-set criterion.

Optimization Method for Robot Moving Object Recognition and Grasping Strategy Based on Binocular Vision

<p>This article proposes a more accurate grasping strategy for the recognition and grasping of moving targets based on binocular vision cameras. Firstly, the front and back scene separation algorithm is used to identify the moving target grabbing object in the production line. Then, by setting an appropriate threshold, the SiamMask target tracking algorithm is improved to achieve dynamic target tracking. Finally, the conveyor belt speed is detected and the real-time position of the object is obtained. Then, the Cartesian strategy is used to achieve path planning and optimization methods for the robotic arm during movement. Through experimental simulation, the effectiveness and stability of the proposed method in this paper have been demonstrated.</p> <p> </p>

Multimodal network path optimization based on a two‐stage algorithm in the perspective of sustainable transport development

AbstractThe environmental issues brought on by carbon emissions from transport have risen to prominence in recent years. More and more academics are using the multi‐objective path optimization method to solve the multimodal optimization problem from the standpoint of sustainable development in order to address the environmental issues brought on by the transport process. The research proposes a two‐stage method to handle multi‐objective optimization convergence and simplify multimodal transport path optimization. In the first stage, a fuzzy C clustering model is established, and based on the clustering results, the multimodal transport network nodes are identified. In the second stage, a multimodal transport multi‐objective path optimization model is established, and the optimal path is solved using a genetic algorithm. The research method was applied in the Bohai Rim region. Results indicated that the fuzzy C‐clustering method and the genetic method were able to select the optimal node city, thus solving the actual site selection problem of multimodal transportation networks. Using the FCM model, the 86 city nodes were categorized into four types, leading to the establishment of the most proficient multimodal transportation network in the Bohai Rim region. Using a genetic algorithm for optimization, a stable state is reached after 25 iterations. In the validation experiment on path optimization, the cost was reduced by 47.12% compared to the minimum single objective time, and transportation carbon emissions saw a reduction of 28.23%. Similarly, compared to the lowest target for transportation carbon emissions, the cost was reduced by 39.48% and the time was reduced by 38.12%. Compared to the lowest target for transportation carbon emissions, the time was reduced by 32.02% and the carbon emissions were reduced by 19.23%. Notably, the transportation multi‐objective path optimization model showed significant improvement compared to the single‐target model. The research method has been proven to be superior, and can offer the most optimal transportation route guidance for participants in multimodal transportation. Furthermore, it can effectively tackle the issue of node selection convergence and multi‐objective optimization, while also serving as a valuable source of data to support the theoretical advancement of multimodal transportation network path optimization.

Obstacle avoidance path planning of 6-DOF robotic arm based on improved A* algorithm and artificial potential field method

AbstractMost studies on path planning of robotic arm focus on obstacle avoidance at the end position of robotic arm, while ignoring the obstacle avoidance of robotic arm joint linkage, and the obstacle avoidance method has low flexibility and adaptability. This paper proposes a path obstacle avoidance algorithm for the overall 6-DOF robotic arm that is based on the improved A* algorithm and the artificial potential field method. In the first place, an improved A* algorithm is proposed to address the deficiencies of the conventional A* algorithm, such as a large number of search nodes and low computational efficiency, in robotic arm end path planning. The enhanced A* algorithm proposes a new node search strategy and local path optimization method, which significantly reduces the number of search nodes and enhances search efficiency. To achieve the manipulator joint rod avoiding obstacles, a method of robotic arm posture adjustment based on the artificial potential field method is proposed. The efficiency and environmental adaptability of the robotic arm path planning algorithm proposed in this paper are validated through three types of simulation analysis conducted in different environments. Finally, the AUBO-i10 robotic arm is used to conduct path avoidance tests. Experimental results demonstrate that the proposed method can make the manipulator move smoothly and effectively plan an obstacle-free path, proving the method’s viability.

Topology Design of 3D Printing Continuous Fiber‐Reinforced Structure Considering Strength and Non‐Equidistant Fiber

The utilization of continuous fiber‐reinforced composites (CFRC) technology in 3D printing opens up new avenues for designing composite products. This paper introduces a novel parallel topology optimization framework tailored for CFRC 3D printing. The framework incorporates strength constraints and uses topology, fiber volume fraction, and fiber orientation as essential design variables. A fiber material interpolation model for multiple design variables is proposed based on the law of mixtures for composites. Bidirectional evolutionary structural optimization and solid isotropic material with penalization are used for optimization of topology and fiber volume fraction, respectively, while the fiber orientation herein is determined using the principal stress method. Strength constraint is considered according to Tsai–Hill criterion. To match manufacturing process demands, a nonequidistant continuous fiber path optimization method based on Hermite interpolation function is introduced. The optimization framework introduced herein is successfully used for optimal design of L‐shaped beam, cantilever beam, and Michell beam. It is concluded that the design space of composite structure can be further broadened using the proposed method. To validate the viability of the proposed optimization method and fiber path design approach, the optimization results are printed using a custom‐developed continuous fiber 3D printer.

Application of the path optimization method to a discrete spin system

Application of the path optimization method to a discrete spin system

A feature and optimized RRT algorithm-based assembly path planning method of complex products

Currently, manufacturing enterprises lack strict regulations and plans for complex products. The actual assembly path mainly relies on technicians’ experience, leading to an unreasonable assembly path for each part at the workstation, an unreasonable transportation path across the workstation, and the problem of mutual interference between parts. To solve these problems, this paper studies the assembly path planning technology of complex products based on the basic geometric features and assembly sequence information. The complexity of assembly path planning is reduced by converting the assembly problem into a disassembly problem. The disassembly direction and distance of each part are determined based on the geometric feature type of each mating face, and the rapid disassembly of the assembly body is achieved. The Rapidly-exploring Random Trees (RRT) algorithm is optimized by target-tendency and double random tree strategy, and the optimal assembly path of each component is quickly generated by combining the path optimization method and interference checking method. Finally, the effectiveness of the proposed method and the superiority of the path planning algorithm are verified using the cylinder head of a Marine diesel engine (MDE) as a case study.