The Report of the 20th National Congress of the Communist Party of China explicitly emphasized the promotion of educational digitalization.The rapid development of new media in the era of network information has not o...The Report of the 20th National Congress of the Communist Party of China explicitly emphasized the promotion of educational digitalization.The rapid development of new media in the era of network information has not only broadened the horizons of college students but also profoundly transformed the content and methods of ideological and political education.As the frontline of ideological work,colleges and universities in Xinjiang are guided by the Party’s strategy for governing Xinjiang in the new era to advance network ideological and political education.This is of great significance in guiding students to develop correct network literacy and promoting ideological and political education to keep pace with the times.Through methods such as text analysis,questionnaire surveys,and interviews,this paper outlines the concept,characteristics,and value of network ideological and political education in colleges and universities in Xinjiang,analyzes its current development status and existing issues,and proposes optimization paths such as adhering to correct political guidance,highlighting regional characteristics,innovating educational methods,and strengthening subject construction.These efforts aim to fulfill the fundamental task of“cultivating talents with moral integrity”and serve the overall goal of social stability and long-term peace in Xinjiang.展开更多
Aiming to address the limitations of the standard Chimp Optimization Algorithm(ChOA),such as inadequate search ability and susceptibility to local optima in Unmanned Aerial Vehicle(UAV)path planning,this paper propose...Aiming to address the limitations of the standard Chimp Optimization Algorithm(ChOA),such as inadequate search ability and susceptibility to local optima in Unmanned Aerial Vehicle(UAV)path planning,this paper proposes a three-dimensional path planning method for UAVs based on the Improved Chimp Optimization Algorithm(IChOA).First,this paper models the terrain and obstacle environments spatially and formulates the total UAV flight cost function according to the constraints,transforming the path planning problem into an optimization problem with multiple constraints.Second,this paper enhances the diversity of the chimpanzee population by applying the Sine chaos mapping strategy and introduces a nonlinear convergence factor to improve the algorithm’s search accuracy and convergence speed.Finally,this paper proposes a dynamic adjustment strategy for the number of chimpanzee advance echelons,which effectively balances global exploration and local exploitation,significantly optimizing the algorithm’s search performance.To validate the effectiveness of the IChOA algorithm,this paper conducts experimental comparisons with eight different intelligent algorithms.The experimental results demonstrate that the IChOA outperforms the selected comparison algorithms in terms of practicality and robustness in UAV 3D path planning.It effectively solves the issues of efficiency in finding the shortest path and ensures high stability during execution.展开更多
In order to find out the optimal press bend forming path in fabricating aircraft integral panels, this article proposes a new method on the basis of the authors＇ previous work. It is composed of the finite element me...In order to find out the optimal press bend forming path in fabricating aircraft integral panels, this article proposes a new method on the basis of the authors＇ previous work. It is composed of the finite element method （FEM） equivalent model, the surface curvature analysis, the artificial neural network response surface and the genetic algorithm. The method begins with analyzing the objective＇s shape curvature to determine the bending position. Then it optimizes the punch travel at each bending position by the following steps： （1） Establish a multi-step press bend forming FEM equivalent model, with which the FEM ex- periments designed with the Taguchi method are performed. （2） Construct a back-propagation （BP） neural network response surface with the data from the FEM experiments. （3） Use the genetic algorithm to optimize the neural network response surface as the objective function. Finally, this method is verified by press bending a complicated double-curvature grid-type stiffened panel and bears out its effectiveness and intrinsic worth in designing the press bend forming path.展开更多
Path planning is an important issue for autonomous underwater vehicles （AUVs） traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning usi...Path planning is an important issue for autonomous underwater vehicles （AUVs） traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning using large-scale chart data was studied, and the principles of ant colony optimization （ACO） were applied. This paper introduced the idea of a visibility graph based on the grid workspace model. It also brought a series of pheromone updating rules for the ACO planning algorithm. The operational steps of the ACO algorithm are proposed as a model for a global path planning method for AUV. To mimic the process of smoothing a planned path, a cutting operator and an insertion-point operator were designed. Simulation results demonstrated that the ACO algorithm is suitable for global path planning. The system has many advantages, including that the operating path of the AUV can be quickly optimized, and it is shorter, safer, and smoother. The prototype system successfully demonstrated the feasibility of the concept, proving it can be applied to surveys of unstructured unmanned environments.展开更多
In milling around sharp corners, residual materials are left at sharp corners when the stepover is extremely long in the contour-parallel tool path. Milling force at the sharp corner rises momentarily due to the incre...In milling around sharp corners, residual materials are left at sharp corners when the stepover is extremely long in the contour-parallel tool path. Milling force at the sharp corner rises momentarily due to the increase of the cutter contact length, thus shortening the tool life and leading to machine chatter, even cutter breakage. Then a tool path improvement method by inserting biarc transition segments in the contour-parallel tool path is proposed for milling the pocket. Using the method, the cutter moves along the biarc transition tool path. And the corner material is removed. The improved tool path is continuous for clearing residual materials at the sharp corner. Finally, the machining experiment validates the proposed method.展开更多
This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,le...This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,length and angle variable rate.First,a three-dimensional(3D)modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs.Considering the length,height and tuning angle of a path,the path planning of R-UAVs is described as a tri-objective optimization problem.Then,an improved multi-objective particle swarm optimization algorithm is developed.To render the algorithm more effective in dealing with this problem,a vibration function is introduced into the collided solutions to improve the algorithm efficiency.Meanwhile,the selection of the global best position is taken into account by the reference point method.Finally,the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine.Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.展开更多
Structure design and fabricating methods of three-dimensional （3D） artificial spherical compound eyes have been researched by many scholars. Micro-nano optical manufacturing is mostly used to process 3D artificial c...Structure design and fabricating methods of three-dimensional （3D） artificial spherical compound eyes have been researched by many scholars. Micro-nano optical manufacturing is mostly used to process 3D artificial compound eyes. However, spherical optical compound eyes are less at optical performance than the eyes of insects, and it is difficult to further improve the imaging quality of compound eyes by means of micro-nano optical manufacturing. In this research, nonhomogeneous aspheric compound eyes （ACEs） are designed and fabricated. The nonhomogeneous aspheric structure is applied to calibrate the spherical aberration. Micro milling with advantages in processing three-dimensional micro structures is adopted to manufacture ACEs. In order to obtain ACEs with high imaging quality, the tool paths are optimized by analyzing the influence factors consisting of interpolation allowable error, scallop height and tool path pattern. In the experiments, two kinds of ACEs are manufactured by micro-milling with different too path patterns and cutting parameter on the miniature precision five-axis milling machine tool. The experimental results indicate that the ACEs of high surface quality can be achieved by circularly milling small micro-lens individually with changeable cutting depth. A prototype of the aspheric compound eye （ACE） with surface roughness （Ra） below 0.12 p.m is obtained with good imaging performance. This research ameliorates the imaging quality of 3D artificial compound eyes, and the proposed method of micro-milling can improve surface processing quality of compound eyes.展开更多
The information transmission path optimization(ITPO) can often a ect the e ciency and accuracy of remanufactur?ing service. However, there is a greater degree of uncertainty and complexity in information transmission ...The information transmission path optimization(ITPO) can often a ect the e ciency and accuracy of remanufactur?ing service. However, there is a greater degree of uncertainty and complexity in information transmission of remanu?facturing service system, which leads to a critical need for designing planning models to deal with this added uncer?tainty and complexity. In this paper, a three?dimensional(3D) model of remanufacturing service information network for information transmission is developed, which combines the physic coordinate and the transmitted properties of all the devices in the remanufacturing service system. In order to solve the basic ITPO in the 3D model, an improved 3D ant colony algorithm(Improved AC) was put forward. Moreover, to further improve the operation e ciency of the algorithm, an improved ant colony?genetic algorithm(AC?GA) that combines the improved AC and genetic algorithm was developed. In addition, by taking the transmission of remanufacturing service demand information of certain roller as example, the e ectiveness of AC?GA algorithm was analyzed and compared with that of improved AC, and the results demonstrated that AC?GA algorithm was superior to AC algorithm in aspects of information transmission delay, information transmission cost, and rate of information loss.展开更多
Continuum robot is a new type of biomimetic robot,which realizes the motion by bending some parts of its body.So its path planning becomes more difficult even compared with hyper-redundant robots.In this paper a circu...Continuum robot is a new type of biomimetic robot,which realizes the motion by bending some parts of its body.So its path planning becomes more difficult even compared with hyper-redundant robots.In this paper a circular arc spline interpolating method is proposed for the robot shape description,and a new two-stage position-selectable-updating particle swarm optimization(TPPSO)algorithm is put forward to solve this path planning problem.The algorithm decomposes the standard PSO velocity’s single-step updating formula into twostage multi-point updating,specifically adopting three points as candidates and selecting the best one as the updated position in the first half stage,and similarly taking seven points as candidates and selecting the best one as the final position in the last half stage.This scheme refines and widens each particle’s searching trajectory,increases the updating speed of the individual best,and improves the converging speed and precision.Aiming at the optimization objective to minimize the sum of all the motion displacements of every segmental points and all the axial stretching or contracting displacements of every segment,the TPPSO algorithm is used to solve the path planning problem.The detailed solution procedure is presented.Numerical examples of five path planning cases show that the proposed algorithm is simple,robust,and efficient.展开更多
When using the beam scanning method for particle beam therapy, the target volume is divided into many iso-energy slices and is irradiated slice by slice. Each slice may comprise thousands of discrete scanning beam pos...When using the beam scanning method for particle beam therapy, the target volume is divided into many iso-energy slices and is irradiated slice by slice. Each slice may comprise thousands of discrete scanning beam positions. An optimized scanning path can decrease the transit dose and may bypass important organs. The minimization of the scanning path length can be considered as a variation of the traveling salesman problem; the simulated annealing algorithm is adopted to solve this problem. The initial scanning path is assumed as a simple zigzag path;subsequently, random searches for accepted new paths are performed through cost evaluation and criteria-based judging. To reduce the optimization time of a given slice,random searches are parallelized by employing thousands of threads. The simultaneous optimization of multiple slices is realized by using many thread blocks of generalpurpose computing on graphics processing units hardware.Running on a computer with an Intel i7-4790 CPU and NVIDIA K2200 GPU, our new method required only 1.3 s to obtain optimized scanning paths with a total of 40 slices in typically studied cases. The procedure and optimization results of this new method are presented in this work.展开更多
The 6-DOF manipulator provides a new option for traditional shipbuilding for its advantages of vast working space,low power consumption,and excellent flexibility.However,the rotation of the end effector along the tool...The 6-DOF manipulator provides a new option for traditional shipbuilding for its advantages of vast working space,low power consumption,and excellent flexibility.However,the rotation of the end effector along the tool axis is functionally redundant when using a robotic arm for five-axis machining.In the process of ship construction,the performance of the parts’protective coating needs to bemachined tomeet the Performance Standard of Protective Coatings(PSPC).The arbitrary redundancy configuration in path planning will result in drastic fluctuations in the robot joint angle,greatly reducing machining quality and efficiency.There have been some studies on singleobjective optimization of redundant variables,However,the quality and efficiency of milling are not affected by a single factor,it is usually influenced by several factors,such as the manipulator stiffness,the joint motion smoothness,and the energy consumption.To solve this problem,this paper proposed a new path optimization method for the industrial robot when it is used for five-axis machining.The path smoothness performance index and the energy consumption index are established based on the joint acceleration and the joint velocity,respectively.The path planning issue is formulated as a constrained multi-objective optimization problem by taking into account the constraints of joint limits and singularity avoidance.Then,the path is split into multiple segments for optimization to avoid the slow convergence rate caused by the high dimension.An algorithm combining the non-dominated sorting genetic algorithm(NSGA-II)and the differential evolution(DE)algorithm is employed to solve the above optimization problem.The simulations validate the effectiveness of the algorithm,showing the improvement of smoothness and the reduction of energy consumption.展开更多
Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies, according to the foraging behavior of ant colonies and the characteristic of food, concepti...Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies, according to the foraging behavior of ant colonies and the characteristic of food, conceptions of neighboring area and smell area were presented. The former can ensure the diversity of paths and the latter ensures that each ant can reach the goal. Then the whole path was divided into three parts and ACO was used to search the second part path. When the three parts pathes were adjusted, the final path was found. The valid path and invalid path were defined to ensure the path valid. Finally, the strategies of the pheromone search were applied to search the optimum path. However, when only the pheromone was used to search the optimum path, ACO converges easily. In order to avoid this premature convergence, combining pheromone search and random search, a hybrid ant colony algorithm(HACO) was used to find the optimum path. The comparison between ACO and HACO shows that HACO can be used to find the shortest path.展开更多
Using the traditional swarm intelligence algorithm to solve the cooperative path planning problem for multi-UAVs is easy to incur the problems of local optimization and a slow convergence rate.A cooperative path plann...Using the traditional swarm intelligence algorithm to solve the cooperative path planning problem for multi-UAVs is easy to incur the problems of local optimization and a slow convergence rate.A cooperative path planning method for multi-UAVs based on the improved sheep optimization is proposed to tackle these.Firstly,based on the three-dimensional planning space,a multi-UAV cooperative cost function model is established according to the path planning requirements,and an initial track set is constructed by combining multiple-population ideas.Then an improved sheep optimization is proposed and used to solve the path planning problem and obtain multiple cooperative paths.The simulation results show that the sheep optimization can meet the requirements of path planning and realize the cooperative path planning of multi-UAVs.Compared with grey wolf optimizer(GWO),improved gray wolf optimizer(IGWO),chaotic gray wolf optimizer(CGWO),differential evolution(DE)algorithm,and particle swam optimization(PSO),the convergence speed and search accuracy of the improved sheep optimization are significantly improved.展开更多
To solve the path following control problem for unmanned surface vehicles(USVs),a control method based on deep reinforcement learning(DRL)with long short-term memory(LSTM)networks is proposed.A distributed proximal po...To solve the path following control problem for unmanned surface vehicles(USVs),a control method based on deep reinforcement learning(DRL)with long short-term memory(LSTM)networks is proposed.A distributed proximal policy opti-mization(DPPO)algorithm,which is a modified actor-critic-based type of reinforcement learning algorithm,is adapted to improve the controller performance in repeated trials.The LSTM network structure is introduced to solve the strong temporal cor-relation USV control problem.In addition,a specially designed path dataset,including straight and curved paths,is established to simulate various sailing scenarios so that the reinforcement learning controller can obtain as much handling experience as possible.Extensive numerical simulation results demonstrate that the proposed method has better control performance under missions involving complex maneuvers than trained with limited scenarios and can potentially be applied in practice.展开更多
The path planning of autonomous mobile robots(PPoAMR)is a very complex multi-constraint problem.The main goal is to find the shortest collision-free path from the starting point to the target point.By the fact that th...The path planning of autonomous mobile robots(PPoAMR)is a very complex multi-constraint problem.The main goal is to find the shortest collision-free path from the starting point to the target point.By the fact that the PPoAMR problem has the prior knowledge that the straight path between the starting point and the target point is the optimum solution when obstacles are not considered.This paper proposes a new path planning algorithm based on the prior knowledge of PPoAMR,which includes the fitness value calculation method and the prior knowledge particle swarm optimization(PKPSO)algorithm.The new fitness calculation method can preserve the information carried by each individual as much as possible by adding an adaptive coefficient.The PKPSO algorithm modifies the particle velocity update method by adding a prior particle calculated from the prior knowledge of PPoAMR and also implemented an elite retention strategy,which improves the local optima evasion capability.In addition,the quintic polynomial trajectory optimization approach is devised to generate a smooth path.Finally,some experimental comparisons with those state-of-the-arts are carried out to demonstrate the effectiveness of the proposed path planning algorithm.展开更多
Modular Solar-Powered Aircraft(M-SPA)is a kind of High-Altitude Long-Endurance(HALE)aircraft which exploits the mission advantage of swarm UAV and the HALE advantage of large aspect-ratio SPA.M-SPA’s separated mode a...Modular Solar-Powered Aircraft(M-SPA)is a kind of High-Altitude Long-Endurance(HALE)aircraft which exploits the mission advantage of swarm UAV and the HALE advantage of large aspect-ratio SPA.M-SPA’s separated mode and combined mode give it the potential to maximize the mission efficiency with limited solar energy.In this paper,firstly,oriented by the mission of maximizing the cruise area,the overall design of the M-SPA is modeled,including the energy model,the aerodynamic model and the flight environment settings.Secondly,by analyzing the energy consumption of the flight modes,we design a multi-phase flight mission strategy.Then,a 24-hour three-dimensional(3D)flight profile of the M-SPA is optimized,including the sub-SPA cooperative path planning in the separation mode.Finally,inspired by the Traveling Salesman Problem(TSP),an improved Ant Colony Algorithm(ACA)is exploited to find the optimal path for each sub-SPA,which is further developed into a dynamic separation and combination scheme for the M-SPA.The simulation results show that the mission performance of the M-SPA outperforms that of the conventional SPA,and explicitly,the mission coverage of the M-SPA is slightly less than a linear increase under comparable simulation conditions.展开更多
In this paper, a unique combination among probabilistic roadmap, modified ant colony optimization, and third order B-spline curve has been proposed to solve path planning problems?in complex and very complex environme...In this paper, a unique combination among probabilistic roadmap, modified ant colony optimization, and third order B-spline curve has been proposed to solve path planning problems?in complex and very complex environments. This proposed approach can be divided into three stages. First stage involves constructing a random roadmap depending on the environment complexity using probabilistic roadmap algorithm. Roadmap can be constructed by distributing N nodes randomly in complex and very complex static environments then pairing these nodes together according to some criteria or conditions. The constructed roadmap contains a huge number of possible random paths that may lead to connecting?the start and the goal points together. Second stage includes finding path within the pre-constructed roadmap. Modified ant colony optimization has been proposed to find or to search the best path between start and goal points, where in addition to the proposed combination, ACO has been modified to increase its ability to find shorter path. Finally, the third stage uses B-spline curve?to smooth and reduce the total length of the found path in the previous stage. The results of the proposed approach ensure?the?feasible?path between start and goal points in complex and very complex environments. Also, the path is guaranteed to be short, smooth, continuous?and safe.展开更多
A bi-objective optimization problem for flapping airfoils is solved to maximize the time-averaged thrust coefficient and the propulsive efficiency. Design variables include the plunging amplitude, the pitching amplitu...A bi-objective optimization problem for flapping airfoils is solved to maximize the time-averaged thrust coefficient and the propulsive efficiency. Design variables include the plunging amplitude, the pitching amplitude and the phase shift angle. A well defined Kriging model is used to substitute the time-consuming high fidelity model, and a multi-objective genetic algorithm is employed as the search algorithm. The optimization results show that the propulsive efficiency can be improved by reducing the plunging amplitude and the phase shift angle in a proper way. The results of global sensitivity analysis using the Sobol’s method show that both of the time-averaged thrust coefficient and the propulsive efficiency are most sensitive to the plunging amplitude, and second most sensitive to the pitching amplitude. It is also observed that the phase shift angle has an un-negligible influence on the propulsive efficiency, and has little effect on the time-averaged thrust coefficient.展开更多
A new genetic algorithm named niche pseudo-parallel genetic algorithm （NPPGA） is presented for path evolution and genetic optimization of autonomous mobile robot. The NPPGA is an effective improvement to maintain th...A new genetic algorithm named niche pseudo-parallel genetic algorithm （NPPGA） is presented for path evolution and genetic optimization of autonomous mobile robot. The NPPGA is an effective improvement to maintain the population diversity as well for the sake of avoiding premature and strengthen parallelism of the population to accelerate the search process combined with niche genetic algorithms and pseudo-parallel genetic algorithms. The proposed approach is evaluated by robotic path optimization, which is a specific application of traveler salesman problem （TSP）. Experimental results indicated that a shortest path could be obtained in the practical traveling salesman problem named ＂Robot tour around Pekin＂, and the performance conducted by NPPGA is better than simple genetic algorithm （SGA） and distributed paralell genetic algorithms （DPGA）.展开更多
In order to overcome some defects of the traditional immune algorithm, the immune algorithm was improved for solving a path optimization problem in deep immune learning of a gene network. Firstly, the diversity of the...In order to overcome some defects of the traditional immune algorithm, the immune algorithm was improved for solving a path optimization problem in deep immune learning of a gene network. Firstly, the diversity of the solution population was enhanced in the evolution process by improving the memory cell processing method. Moreover, effective gene information was dynamically extracted from the genes of the excellent antibodies to make good vaccines in the process of immune evolution. Worse antibodies were optimized by vaccinating these antibodies, and the convergence of the immune algorithm to the optimal solution was improved. Finally, the feasibility of the improved immune algorithm was verified in the experimental simulation for solving the classic NP problem in deep immune learning of the gene network.展开更多
基金Social Science Fund Project of the Xinjiang Uygur Autonomous Region“Research on the Construction of Network Ideological Discourse Power in Colleges and Universities in Xinjiang”(2023BKS010)。
文摘The Report of the 20th National Congress of the Communist Party of China explicitly emphasized the promotion of educational digitalization.The rapid development of new media in the era of network information has not only broadened the horizons of college students but also profoundly transformed the content and methods of ideological and political education.As the frontline of ideological work,colleges and universities in Xinjiang are guided by the Party’s strategy for governing Xinjiang in the new era to advance network ideological and political education.This is of great significance in guiding students to develop correct network literacy and promoting ideological and political education to keep pace with the times.Through methods such as text analysis,questionnaire surveys,and interviews,this paper outlines the concept,characteristics,and value of network ideological and political education in colleges and universities in Xinjiang,analyzes its current development status and existing issues,and proposes optimization paths such as adhering to correct political guidance,highlighting regional characteristics,innovating educational methods,and strengthening subject construction.These efforts aim to fulfill the fundamental task of“cultivating talents with moral integrity”and serve the overall goal of social stability and long-term peace in Xinjiang.
基金supported by the Shaanxi Province Natural Science Basic Research Program Project(2024JC-YBMS-572)partially funded by Yan’an University Graduate Education Innovation Program Project(YCX2023032,YCX2023033,YCX2024094,YCX2024097)the“14th Five Year Plan Medium and Long Term Major Scientific Research Project”(2021ZCQ015)of Yan’an University.
文摘Aiming to address the limitations of the standard Chimp Optimization Algorithm(ChOA),such as inadequate search ability and susceptibility to local optima in Unmanned Aerial Vehicle(UAV)path planning,this paper proposes a three-dimensional path planning method for UAVs based on the Improved Chimp Optimization Algorithm(IChOA).First,this paper models the terrain and obstacle environments spatially and formulates the total UAV flight cost function according to the constraints,transforming the path planning problem into an optimization problem with multiple constraints.Second,this paper enhances the diversity of the chimpanzee population by applying the Sine chaos mapping strategy and introduces a nonlinear convergence factor to improve the algorithm’s search accuracy and convergence speed.Finally,this paper proposes a dynamic adjustment strategy for the number of chimpanzee advance echelons,which effectively balances global exploration and local exploitation,significantly optimizing the algorithm’s search performance.To validate the effectiveness of the IChOA algorithm,this paper conducts experimental comparisons with eight different intelligent algorithms.The experimental results demonstrate that the IChOA outperforms the selected comparison algorithms in terms of practicality and robustness in UAV 3D path planning.It effectively solves the issues of efficiency in finding the shortest path and ensures high stability during execution.
基金Specialized Research Fund for the Doctoral Program of High Education of China (20091102110021)
文摘In order to find out the optimal press bend forming path in fabricating aircraft integral panels, this article proposes a new method on the basis of the authors＇ previous work. It is composed of the finite element method （FEM） equivalent model, the surface curvature analysis, the artificial neural network response surface and the genetic algorithm. The method begins with analyzing the objective＇s shape curvature to determine the bending position. Then it optimizes the punch travel at each bending position by the following steps： （1） Establish a multi-step press bend forming FEM equivalent model, with which the FEM ex- periments designed with the Taguchi method are performed. （2） Construct a back-propagation （BP） neural network response surface with the data from the FEM experiments. （3） Use the genetic algorithm to optimize the neural network response surface as the objective function. Finally, this method is verified by press bending a complicated double-curvature grid-type stiffened panel and bears out its effectiveness and intrinsic worth in designing the press bend forming path.
基金Supported by State Key Laboratory of Robotics and System (HIT) under Grant No.SKLRS200706the Heilongjiang Scientific Research Foundation for Postdoctoral Financial Assistance under Grant No.323630221the Project of Harbin Technological Talent Research Foundation under Grant No.RC2006QN009015
文摘Path planning is an important issue for autonomous underwater vehicles （AUVs） traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning using large-scale chart data was studied, and the principles of ant colony optimization （ACO） were applied. This paper introduced the idea of a visibility graph based on the grid workspace model. It also brought a series of pheromone updating rules for the ACO planning algorithm. The operational steps of the ACO algorithm are proposed as a model for a global path planning method for AUV. To mimic the process of smoothing a planned path, a cutting operator and an insertion-point operator were designed. Simulation results demonstrated that the ACO algorithm is suitable for global path planning. The system has many advantages, including that the operating path of the AUV can be quickly optimized, and it is shorter, safer, and smoother. The prototype system successfully demonstrated the feasibility of the concept, proving it can be applied to surveys of unstructured unmanned environments.
文摘In milling around sharp corners, residual materials are left at sharp corners when the stepover is extremely long in the contour-parallel tool path. Milling force at the sharp corner rises momentarily due to the increase of the cutter contact length, thus shortening the tool life and leading to machine chatter, even cutter breakage. Then a tool path improvement method by inserting biarc transition segments in the contour-parallel tool path is proposed for milling the pocket. Using the method, the cutter moves along the biarc transition tool path. And the corner material is removed. The improved tool path is continuous for clearing residual materials at the sharp corner. Finally, the machining experiment validates the proposed method.
基金supported by the National Natural Science Foundation of China(6167321461673217+2 种基金61673219)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(18KJB120011)the Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX19_0299)
文摘This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,length and angle variable rate.First,a three-dimensional(3D)modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs.Considering the length,height and tuning angle of a path,the path planning of R-UAVs is described as a tri-objective optimization problem.Then,an improved multi-objective particle swarm optimization algorithm is developed.To render the algorithm more effective in dealing with this problem,a vibration function is introduced into the collided solutions to improve the algorithm efficiency.Meanwhile,the selection of the global best position is taken into account by the reference point method.Finally,the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine.Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.
基金Supported by National Natural Science Foundation of China(Grant No.50935003)National Numerical Control Major Projects of China(Grant No.2013ZX04001000215)
文摘Structure design and fabricating methods of three-dimensional （3D） artificial spherical compound eyes have been researched by many scholars. Micro-nano optical manufacturing is mostly used to process 3D artificial compound eyes. However, spherical optical compound eyes are less at optical performance than the eyes of insects, and it is difficult to further improve the imaging quality of compound eyes by means of micro-nano optical manufacturing. In this research, nonhomogeneous aspheric compound eyes （ACEs） are designed and fabricated. The nonhomogeneous aspheric structure is applied to calibrate the spherical aberration. Micro milling with advantages in processing three-dimensional micro structures is adopted to manufacture ACEs. In order to obtain ACEs with high imaging quality, the tool paths are optimized by analyzing the influence factors consisting of interpolation allowable error, scallop height and tool path pattern. In the experiments, two kinds of ACEs are manufactured by micro-milling with different too path patterns and cutting parameter on the miniature precision five-axis milling machine tool. The experimental results indicate that the ACEs of high surface quality can be achieved by circularly milling small micro-lens individually with changeable cutting depth. A prototype of the aspheric compound eye （ACE） with surface roughness （Ra） below 0.12 p.m is obtained with good imaging performance. This research ameliorates the imaging quality of 3D artificial compound eyes, and the proposed method of micro-milling can improve surface processing quality of compound eyes.
基金National Natural Science Foundation of China(Grant Nos.51805385,71471143)Hubei Provincial Natural Science Foundation of China(Grant No.2018CFB265)Center for Service Science and Engineering of Wuhan University of Science and Technology(Grant No.CSSE2017KA04)
文摘The information transmission path optimization(ITPO) can often a ect the e ciency and accuracy of remanufactur?ing service. However, there is a greater degree of uncertainty and complexity in information transmission of remanu?facturing service system, which leads to a critical need for designing planning models to deal with this added uncer?tainty and complexity. In this paper, a three?dimensional(3D) model of remanufacturing service information network for information transmission is developed, which combines the physic coordinate and the transmitted properties of all the devices in the remanufacturing service system. In order to solve the basic ITPO in the 3D model, an improved 3D ant colony algorithm(Improved AC) was put forward. Moreover, to further improve the operation e ciency of the algorithm, an improved ant colony?genetic algorithm(AC?GA) that combines the improved AC and genetic algorithm was developed. In addition, by taking the transmission of remanufacturing service demand information of certain roller as example, the e ectiveness of AC?GA algorithm was analyzed and compared with that of improved AC, and the results demonstrated that AC?GA algorithm was superior to AC algorithm in aspects of information transmission delay, information transmission cost, and rate of information loss.
基金Supported by the Fundamental Research Funds for the Central Universities(Grant No.DL09CB02)the Heilongjiang Province Natural Science Fund(Grant No.E201013)
文摘Continuum robot is a new type of biomimetic robot,which realizes the motion by bending some parts of its body.So its path planning becomes more difficult even compared with hyper-redundant robots.In this paper a circular arc spline interpolating method is proposed for the robot shape description,and a new two-stage position-selectable-updating particle swarm optimization(TPPSO)algorithm is put forward to solve this path planning problem.The algorithm decomposes the standard PSO velocity’s single-step updating formula into twostage multi-point updating,specifically adopting three points as candidates and selecting the best one as the updated position in the first half stage,and similarly taking seven points as candidates and selecting the best one as the final position in the last half stage.This scheme refines and widens each particle’s searching trajectory,increases the updating speed of the individual best,and improves the converging speed and precision.Aiming at the optimization objective to minimize the sum of all the motion displacements of every segmental points and all the axial stretching or contracting displacements of every segment,the TPPSO algorithm is used to solve the path planning problem.The detailed solution procedure is presented.Numerical examples of five path planning cases show that the proposed algorithm is simple,robust,and efficient.
文摘When using the beam scanning method for particle beam therapy, the target volume is divided into many iso-energy slices and is irradiated slice by slice. Each slice may comprise thousands of discrete scanning beam positions. An optimized scanning path can decrease the transit dose and may bypass important organs. The minimization of the scanning path length can be considered as a variation of the traveling salesman problem; the simulated annealing algorithm is adopted to solve this problem. The initial scanning path is assumed as a simple zigzag path;subsequently, random searches for accepted new paths are performed through cost evaluation and criteria-based judging. To reduce the optimization time of a given slice,random searches are parallelized by employing thousands of threads. The simultaneous optimization of multiple slices is realized by using many thread blocks of generalpurpose computing on graphics processing units hardware.Running on a computer with an Intel i7-4790 CPU and NVIDIA K2200 GPU, our new method required only 1.3 s to obtain optimized scanning paths with a total of 40 slices in typically studied cases. The procedure and optimization results of this new method are presented in this work.
文摘The 6-DOF manipulator provides a new option for traditional shipbuilding for its advantages of vast working space,low power consumption,and excellent flexibility.However,the rotation of the end effector along the tool axis is functionally redundant when using a robotic arm for five-axis machining.In the process of ship construction,the performance of the parts’protective coating needs to bemachined tomeet the Performance Standard of Protective Coatings(PSPC).The arbitrary redundancy configuration in path planning will result in drastic fluctuations in the robot joint angle,greatly reducing machining quality and efficiency.There have been some studies on singleobjective optimization of redundant variables,However,the quality and efficiency of milling are not affected by a single factor,it is usually influenced by several factors,such as the manipulator stiffness,the joint motion smoothness,and the energy consumption.To solve this problem,this paper proposed a new path optimization method for the industrial robot when it is used for five-axis machining.The path smoothness performance index and the energy consumption index are established based on the joint acceleration and the joint velocity,respectively.The path planning issue is formulated as a constrained multi-objective optimization problem by taking into account the constraints of joint limits and singularity avoidance.Then,the path is split into multiple segments for optimization to avoid the slow convergence rate caused by the high dimension.An algorithm combining the non-dominated sorting genetic algorithm(NSGA-II)and the differential evolution(DE)algorithm is employed to solve the above optimization problem.The simulations validate the effectiveness of the algorithm,showing the improvement of smoothness and the reduction of energy consumption.
基金Projects(60234030, 60404021) supported by the National Natural Science Foundation of China
文摘Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies, according to the foraging behavior of ant colonies and the characteristic of food, conceptions of neighboring area and smell area were presented. The former can ensure the diversity of paths and the latter ensures that each ant can reach the goal. Then the whole path was divided into three parts and ACO was used to search the second part path. When the three parts pathes were adjusted, the final path was found. The valid path and invalid path were defined to ensure the path valid. Finally, the strategies of the pheromone search were applied to search the optimum path. However, when only the pheromone was used to search the optimum path, ACO converges easily. In order to avoid this premature convergence, combining pheromone search and random search, a hybrid ant colony algorithm(HACO) was used to find the optimum path. The comparison between ACO and HACO shows that HACO can be used to find the shortest path.
基金supported in part by the Fundamental Research Funds for the Central Universities(No.NZ18008)。
文摘Using the traditional swarm intelligence algorithm to solve the cooperative path planning problem for multi-UAVs is easy to incur the problems of local optimization and a slow convergence rate.A cooperative path planning method for multi-UAVs based on the improved sheep optimization is proposed to tackle these.Firstly,based on the three-dimensional planning space,a multi-UAV cooperative cost function model is established according to the path planning requirements,and an initial track set is constructed by combining multiple-population ideas.Then an improved sheep optimization is proposed and used to solve the path planning problem and obtain multiple cooperative paths.The simulation results show that the sheep optimization can meet the requirements of path planning and realize the cooperative path planning of multi-UAVs.Compared with grey wolf optimizer(GWO),improved gray wolf optimizer(IGWO),chaotic gray wolf optimizer(CGWO),differential evolution(DE)algorithm,and particle swam optimization(PSO),the convergence speed and search accuracy of the improved sheep optimization are significantly improved.
基金supported by the National Natural Science Foundation(61601491)the Natural Science Foundation of Hubei Province(2018CFC865)the China Postdoctoral Science Foundation Funded Project(2016T45686).
文摘To solve the path following control problem for unmanned surface vehicles(USVs),a control method based on deep reinforcement learning(DRL)with long short-term memory(LSTM)networks is proposed.A distributed proximal policy opti-mization(DPPO)algorithm,which is a modified actor-critic-based type of reinforcement learning algorithm,is adapted to improve the controller performance in repeated trials.The LSTM network structure is introduced to solve the strong temporal cor-relation USV control problem.In addition,a specially designed path dataset,including straight and curved paths,is established to simulate various sailing scenarios so that the reinforcement learning controller can obtain as much handling experience as possible.Extensive numerical simulation results demonstrate that the proposed method has better control performance under missions involving complex maneuvers than trained with limited scenarios and can potentially be applied in practice.
基金This work was supported by the National Key R&D Funding of China(No.2018YFB1403702)the Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars(No.LR22F030003).
文摘The path planning of autonomous mobile robots(PPoAMR)is a very complex multi-constraint problem.The main goal is to find the shortest collision-free path from the starting point to the target point.By the fact that the PPoAMR problem has the prior knowledge that the straight path between the starting point and the target point is the optimum solution when obstacles are not considered.This paper proposes a new path planning algorithm based on the prior knowledge of PPoAMR,which includes the fitness value calculation method and the prior knowledge particle swarm optimization(PKPSO)algorithm.The new fitness calculation method can preserve the information carried by each individual as much as possible by adding an adaptive coefficient.The PKPSO algorithm modifies the particle velocity update method by adding a prior particle calculated from the prior knowledge of PPoAMR and also implemented an elite retention strategy,which improves the local optima evasion capability.In addition,the quintic polynomial trajectory optimization approach is devised to generate a smooth path.Finally,some experimental comparisons with those state-of-the-arts are carried out to demonstrate the effectiveness of the proposed path planning algorithm.
基金supported by the National Natural Science Foundation of China(Nos.61901448,61871401,12002340).
文摘Modular Solar-Powered Aircraft(M-SPA)is a kind of High-Altitude Long-Endurance(HALE)aircraft which exploits the mission advantage of swarm UAV and the HALE advantage of large aspect-ratio SPA.M-SPA’s separated mode and combined mode give it the potential to maximize the mission efficiency with limited solar energy.In this paper,firstly,oriented by the mission of maximizing the cruise area,the overall design of the M-SPA is modeled,including the energy model,the aerodynamic model and the flight environment settings.Secondly,by analyzing the energy consumption of the flight modes,we design a multi-phase flight mission strategy.Then,a 24-hour three-dimensional(3D)flight profile of the M-SPA is optimized,including the sub-SPA cooperative path planning in the separation mode.Finally,inspired by the Traveling Salesman Problem(TSP),an improved Ant Colony Algorithm(ACA)is exploited to find the optimal path for each sub-SPA,which is further developed into a dynamic separation and combination scheme for the M-SPA.The simulation results show that the mission performance of the M-SPA outperforms that of the conventional SPA,and explicitly,the mission coverage of the M-SPA is slightly less than a linear increase under comparable simulation conditions.
文摘In this paper, a unique combination among probabilistic roadmap, modified ant colony optimization, and third order B-spline curve has been proposed to solve path planning problems?in complex and very complex environments. This proposed approach can be divided into three stages. First stage involves constructing a random roadmap depending on the environment complexity using probabilistic roadmap algorithm. Roadmap can be constructed by distributing N nodes randomly in complex and very complex static environments then pairing these nodes together according to some criteria or conditions. The constructed roadmap contains a huge number of possible random paths that may lead to connecting?the start and the goal points together. Second stage includes finding path within the pre-constructed roadmap. Modified ant colony optimization has been proposed to find or to search the best path between start and goal points, where in addition to the proposed combination, ACO has been modified to increase its ability to find shorter path. Finally, the third stage uses B-spline curve?to smooth and reduce the total length of the found path in the previous stage. The results of the proposed approach ensure?the?feasible?path between start and goal points in complex and very complex environments. Also, the path is guaranteed to be short, smooth, continuous?and safe.
基金Supported by the National Science Foundation for Post-doctoral Scientists of China (20090460216 )the National Defense Fundamental Research Foundation of China(B222006060)
文摘A bi-objective optimization problem for flapping airfoils is solved to maximize the time-averaged thrust coefficient and the propulsive efficiency. Design variables include the plunging amplitude, the pitching amplitude and the phase shift angle. A well defined Kriging model is used to substitute the time-consuming high fidelity model, and a multi-objective genetic algorithm is employed as the search algorithm. The optimization results show that the propulsive efficiency can be improved by reducing the plunging amplitude and the phase shift angle in a proper way. The results of global sensitivity analysis using the Sobol’s method show that both of the time-averaged thrust coefficient and the propulsive efficiency are most sensitive to the plunging amplitude, and second most sensitive to the pitching amplitude. It is also observed that the phase shift angle has an un-negligible influence on the propulsive efficiency, and has little effect on the time-averaged thrust coefficient.
文摘A new genetic algorithm named niche pseudo-parallel genetic algorithm （NPPGA） is presented for path evolution and genetic optimization of autonomous mobile robot. The NPPGA is an effective improvement to maintain the population diversity as well for the sake of avoiding premature and strengthen parallelism of the population to accelerate the search process combined with niche genetic algorithms and pseudo-parallel genetic algorithms. The proposed approach is evaluated by robotic path optimization, which is a specific application of traveler salesman problem （TSP）. Experimental results indicated that a shortest path could be obtained in the practical traveling salesman problem named ＂Robot tour around Pekin＂, and the performance conducted by NPPGA is better than simple genetic algorithm （SGA） and distributed paralell genetic algorithms （DPGA）.
文摘In order to overcome some defects of the traditional immune algorithm, the immune algorithm was improved for solving a path optimization problem in deep immune learning of a gene network. Firstly, the diversity of the solution population was enhanced in the evolution process by improving the memory cell processing method. Moreover, effective gene information was dynamically extracted from the genes of the excellent antibodies to make good vaccines in the process of immune evolution. Worse antibodies were optimized by vaccinating these antibodies, and the convergence of the immune algorithm to the optimal solution was improved. Finally, the feasibility of the improved immune algorithm was verified in the experimental simulation for solving the classic NP problem in deep immune learning of the gene network.
