To guide an unmanned aerial vehicle(UAV)flying in complex three-dimensional(3D)environments with unknown obstacles,a novel UAV path planning algorithm named IRRT^(∗)-C2TD3 is proposed.The algorithm combines the rapidl...To guide an unmanned aerial vehicle(UAV)flying in complex three-dimensional(3D)environments with unknown obstacles,a novel UAV path planning algorithm named IRRT^(∗)-C2TD3 is proposed.The algorithm combines the rapidly-exploring random tree star(RRT^(∗))algorithm with the twin delayed deep deterministic policy gradients(TD3)algorithm(a deep reinforcement learning algorithm).By employing exploration strategies from reinforcement learning,IRRT^(∗)-C2TD3 improves the RRT^(∗)algorithm.IRRT^(∗)-C2TD3 is a two-stage path planning algorithm comprising pre-planning and real-time planning.It performs pre-planning of paths by generating paths based on geometric connections toward the goal and smoothing them using cubic B-spline curves.By designing the network architecture and reward function of the TD3 algorithm,real-time planning in unknown environments is achieved based on the pre-planned path from the first stage.Simulation results show that IRRT^(∗)-C2TD3 demonstrates better path planning performance in 3D partially unknown environments than RRT^(∗)-C2TD3,M-C2TD3 and MODRRT^(∗)algorithms.展开更多
The introduction of path planning and visual navigation in vascular interventional surgery can provide an intuitive reference and guidance for doctors.In this study,based on the preprocessing results of vessel skeleto...The introduction of path planning and visual navigation in vascular interventional surgery can provide an intuitive reference and guidance for doctors.In this study,based on the preprocessing results of vessel skeleton extraction and stenosis diagnosis in X-ray coronary angiography images,clustering is used to determine the connectivity of the intersection points,and then the improved Dijkstra algorithm is used to automatically plan the surgical path.On this basis,the intermediate point is introduced to piecewise correct the path and improve the accuracy of the system.Finally,the epipolar constrained inverse projection transformation is used to reconstruct the coronary artery 3D model,and the optimal path is marked to achieve a multi-angle 3D visual navigation.Clinical experimental results show that compared with the traditional Dijkstra algorithm,the improved method can reduce the need for intermediate points,which improves computational efficiency,and the average error of manual calibration path is reduced to 4%of that before overall optimization.The results of 3D reconstruction and reprojection further qualitatively and quantitatively verify the effectiveness of the whole scheme.展开更多
The influence of ocean environment on navigation of autonomous underwater vehicle(AUV)cannot be ignored.In the marine environment,ocean currents,internal waves,and obstacles are usually considered in AUV path planning...The influence of ocean environment on navigation of autonomous underwater vehicle(AUV)cannot be ignored.In the marine environment,ocean currents,internal waves,and obstacles are usually considered in AUV path planning.In this paper,an improved particle swarm optimization(PSO)is proposed to solve three problems,traditional PSO algorithm is prone to fall into local optimization,path smoothing is always carried out after all the path planning steps,and the path fitness function is so simple that it cannot adapt to complex marine environment.The adaptive inertia weight and the“active”particle of the fish swarm algorithm are established to improve the global search and local search ability of the algorithm.The cubic spline interpolation method is combined with PSO to smooth the path in real time.The fitness function of the algorithm is optimized.Five evaluation indexes are comprehensively considered to solve the three-demensional(3D)path planning problem of AUV in the ocean currents and internal wave environment.The proposed method improves the safety of the path planning and saves energy.展开更多
In this paper, a novel algorithm based on disturbed fluid and trajectory propagation is developed to solve the three-dimensional（3-D） path planning problem of unmanned aerial vehicle（UAV） in static environment.Fir...In this paper, a novel algorithm based on disturbed fluid and trajectory propagation is developed to solve the three-dimensional（3-D） path planning problem of unmanned aerial vehicle（UAV） in static environment.Firstly, inspired by the phenomenon of streamlines avoiding obstacles, the algorithm based on disturbed fluid is developed and broadened.The effect of obstacles on original fluid field is quantified by the perturbation matrix, where the tangential matrix is first introduced.By modifying the original flow field, the modified one is then obtained, where the streamlines can be regarded as planned paths.And the path proves to avoid all obstacles smoothly and swiftly, follow the shape of obstacles effectively and reach the destination eventually.Then, by considering the kinematics and dynamics equations of UAV, the method called trajectory propagation is adopted to judge the feasibility of the path.If the planned path is unfeasible, repulsive and tangential parameters in the perturbation matrix will be adjusted adaptively based on the resolved state variables of UAV.In most cases, a flyable path can be obtained eventually.Simulation results demonstrate the effectiveness of this method.展开更多
In this paper, a four-dimensional coordinated path planning algorithm for multiple UAVs is proposed, in which time variable is taken into account for each UAV as well as collision free and obstacle avoidance. A Spatia...In this paper, a four-dimensional coordinated path planning algorithm for multiple UAVs is proposed, in which time variable is taken into account for each UAV as well as collision free and obstacle avoidance. A Spatial Refined Voting Mechanism(SRVM) is designed for standard Particle Swarm Optimization(PSO) to overcome the defects of local optimal and slow convergence.For each generation candidate particle positions are recorded and an adaptive cube is formed with own adaptive side length to indicate occupied regions. Then space voting begins and is sorted based on voting results, whose centers with bigger voting counts are seen as sub-optimal positions. The average of all particles of corresponding dimensions are calculated as the refined solutions. A time coordination method is developed by generating specified candidate paths for every UAV, making them arrive the same destination with the same time consumption. A spatial-temporal collision avoidance technique is introduced to make collision free. Distance to destination is constructed to improve the searching accuracy and velocity of particles. In addition, the objective function is redesigned by considering the obstacle and threat avoidance, Estimated Time of Arrival(ETA), separation maintenance and UAV self-constraints. Experimental results prove the effectiveness and efficiency of the algorithm.展开更多
This paper presents a 3D path planning algorithm for an unmanned aerial vehicle （UAV） in complex environments. In this algorithm, the environments are divided into voxels by octree algorithm. In order to satisfy the...This paper presents a 3D path planning algorithm for an unmanned aerial vehicle （UAV） in complex environments. In this algorithm, the environments are divided into voxels by octree algorithm. In order to satisfy the safety requirement of the UAV, free space is represented by free voxels, which have enough space margin for the UAV to pass through. A bounding box array is created in the whole 3D space to evaluate the free voxel connectivity. The probabilistic roadmap method （PRM） is improved by random sampling in the bounding box array to ensure a more efficient distribution of roadmap nodes in 3D space. According to the connectivity evaluation, the roadmap is used to plan a feasible path by using A＊ algorithm. Experimental results indicate that the proposed algorithm is valid in complex 3D environments.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
Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D pa...Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D path planning, a modified ant colony optimization is proposed in this paper. Firstly, the grid method for environment modeling is adopted. Heuristic information is connected with the planning space. A semi-iterative global pheromone update mechanism is proposed. Secondly, the optimal ants mutate the paths to improve the diversity of the algorithm after a defined iterative number. Thirdly, co-evolutionary algorithm is used. Finally, the simulation result shows the effectiveness of the proposed algorithm in solving the problem of 3D pipe path planning.展开更多
Online three-dimensional(3D)path planning in dynamic environments is a fundamental problem for achieving autonomous navigation of unmanned aerial vehicles(UAVs).However,existing methods struggle to model traversable d...Online three-dimensional(3D)path planning in dynamic environments is a fundamental problem for achieving autonomous navigation of unmanned aerial vehicles(UAVs).However,existing methods struggle to model traversable dynamic gaps,resulting in conservative and suboptimal trajectories.To address these challenges,this paper proposes a hierarchical reinforcement learning(RL)framework that integrates global path guidance,local trajectory generation,predictive safety evaluation,and neural network-based decision-making.Specifically,the global planner provides long-term navigation guidance,and the local module then utilizes an improved 3D dynamic window approach(DWA)to generate dynamically feasible candidate trajectories.To enhance safety in dense dynamic scenarios,the algorithm introduces a predictive axis-aligned bounding box(AABB)strategy to model the future occupancy of obstacles,combined with convex hull verification for efficient trajectory safety assessment.Furthermore,a double deep Q-network(DDQN)is employed with structured feature encoding,enabling the neural network to reliably select the optimal trajectory from the candidate set,thereby improving robustness and generalization.Comparative experiments conducted in a high-fidelity simulation environment show that the algorithm outperforms existing algorithms,reducing the average number of collisions to 0.2 while shortening the average task completion time by approximately 15%,and achieving a success rate of 97%.展开更多
To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned aerial vehicle (UAV) low-altitude penetration,a novel route planning method was proposed.Fir...To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned aerial vehicle (UAV) low-altitude penetration,a novel route planning method was proposed.First and foremost,a coevolutionary multi-agent genetic algorithm (CE-MAGA) was formed by introducing coevolutionary mechanism to multi-agent genetic algorithm (MAGA),an efficient global optimization algorithm.A dynamic route representation form was also adopted to improve the flight route accuracy.Moreover,an efficient constraint handling method was used to simplify the treatment of multi-constraint and reduce the time-cost of planning computation.Simulation and corresponding analysis show that the planning results of CE-MAGA have better performance on terrain following,terrain avoidance,threat avoidance (TF/TA2) and lower route costs than other existing algorithms.In addition,feasible flight routes can be acquired within 2 s,and the convergence rate of the whole evolutionary process is very fast.展开更多
Complete and efficient detection of unknown targets is the most popular application of UAV swarms. Under most situations, targets have directional characteristics so that they can only be successfully detected within ...Complete and efficient detection of unknown targets is the most popular application of UAV swarms. Under most situations, targets have directional characteristics so that they can only be successfully detected within specific angles. In such cases, how to coordinate UAVs and allocate optimal paths for them to efficiently detect all the targets is the primary issue to be solved. In this paper, an intelligent target detection method is proposed for UAV swarms to achieve real-time detection requirements. First, a target-feature-information-based disintegration method is built up to divide the search space into a set of cubes. Theoretically, when the cubes are traversed, all the targets can be detected. Then, a Kuhn-Munkres(KM)-algorithm-based path planning method is proposed for UAVs to traverse the cubes. Finally, to further improve search efficiency, a 3 D realtime probability map is established over the search space which estimates the possibility of detecting new targets at each point. This map is adopted to modify the weights in KM algorithm, thereby optimizing the UAVs’ paths during the search process. Simulation results show that with the proposed method, all targets, with detection angle limitations, can be found by UAVs. Moreover, by implementing the 3 D probability map, the search efficiency is improved by 23.4%–78.1%.展开更多
Recently in the area of biological manufacturing and rapid prototyping manufacturing, the bone scaffolds based on the additive manufacturing in repairing bone defects have been paid more and more attention. In the pro...Recently in the area of biological manufacturing and rapid prototyping manufacturing, the bone scaffolds based on the additive manufacturing in repairing bone defects have been paid more and more attention. In the process of preparation, path planning directly affects the structure, performance as well as the final bone cell culture conditions. Due to the special natural bone scaffold structural characteristic, the traditional rapid prototyping(RP) path planning is not fully suitable for the preparation of bone scaffolds. In this paper, based on the 3D printing extrusion forming technology, a method of path planning for osteochondral integrated scaffolds with gradient structure is put forward, which provides a theoretical basis for bone-scaffold modeling and practical preparation. The implementation of the path planning processing system makes it possible to process data automatically from the initial stereo lithography(STL) model of the actual bone defect part by computer X-ray tomography technique(CT) scan or modeling,to generate the path code and to generate the final machining information after post-processing. This work provides some guidelines for independent research and development of automation equipment for biological manufacturing preparation and software technology.The experiment and test results have verified the validity of the path planning method and the good properties of the bone scaffolds with gradient structures.展开更多
Based on patient computerized tomography data,we segmented a region containing an intracranial hematoma using the threshold method and reconstructed the 3D hematoma model.To improve the efficiency and accuracy of iden...Based on patient computerized tomography data,we segmented a region containing an intracranial hematoma using the threshold method and reconstructed the 3D hematoma model.To improve the efficiency and accuracy of identifying puncture points,a point-cloud search arithmetic method for modified adaptive weighted particle swarm optimization is proposed and used for optimal external axis extraction.According to the characteristics of the multitube drainage tube and the clinical needs of puncture for intracranial hematoma removal,the proposed algorithm can provide an optimal route for a drainage tube for the hematoma,the precise position of the puncture point,and preoperative planning information,which have considerable instructional significance for clinicians.展开更多
基金National Natural Science Foundation of China(No.62173084)Foundation of Shanghai Committee of Science and Technology,China(Nos.23ZR1401800 and 22JC1401403)。
文摘To guide an unmanned aerial vehicle(UAV)flying in complex three-dimensional(3D)environments with unknown obstacles,a novel UAV path planning algorithm named IRRT^(∗)-C2TD3 is proposed.The algorithm combines the rapidly-exploring random tree star(RRT^(∗))algorithm with the twin delayed deep deterministic policy gradients(TD3)algorithm(a deep reinforcement learning algorithm).By employing exploration strategies from reinforcement learning,IRRT^(∗)-C2TD3 improves the RRT^(∗)algorithm.IRRT^(∗)-C2TD3 is a two-stage path planning algorithm comprising pre-planning and real-time planning.It performs pre-planning of paths by generating paths based on geometric connections toward the goal and smoothing them using cubic B-spline curves.By designing the network architecture and reward function of the TD3 algorithm,real-time planning in unknown environments is achieved based on the pre-planned path from the first stage.Simulation results show that IRRT^(∗)-C2TD3 demonstrates better path planning performance in 3D partially unknown environments than RRT^(∗)-C2TD3,M-C2TD3 and MODRRT^(∗)algorithms.
基金the National Natural Science Foundation of China(No.61973210)the Interdisciplinary Program of Shanghai Jiao Tong University(Nos.YG2019ZDA17 and ZH2018QNB23)+1 种基金the Shanghai Advanced Technology Joint Research Fund(No.USCAST2020-7)the Shenzhen Science and Technology Commission Key Technology Project(No.JSGG20200701095003006)。
文摘The introduction of path planning and visual navigation in vascular interventional surgery can provide an intuitive reference and guidance for doctors.In this study,based on the preprocessing results of vessel skeleton extraction and stenosis diagnosis in X-ray coronary angiography images,clustering is used to determine the connectivity of the intersection points,and then the improved Dijkstra algorithm is used to automatically plan the surgical path.On this basis,the intermediate point is introduced to piecewise correct the path and improve the accuracy of the system.Finally,the epipolar constrained inverse projection transformation is used to reconstruct the coronary artery 3D model,and the optimal path is marked to achieve a multi-angle 3D visual navigation.Clinical experimental results show that compared with the traditional Dijkstra algorithm,the improved method can reduce the need for intermediate points,which improves computational efficiency,and the average error of manual calibration path is reduced to 4%of that before overall optimization.The results of 3D reconstruction and reprojection further qualitatively and quantitatively verify the effectiveness of the whole scheme.
基金supported by the High-tech Ship Projects of the Ministry of Industry and Information Technology of China(2021-342).
文摘The influence of ocean environment on navigation of autonomous underwater vehicle(AUV)cannot be ignored.In the marine environment,ocean currents,internal waves,and obstacles are usually considered in AUV path planning.In this paper,an improved particle swarm optimization(PSO)is proposed to solve three problems,traditional PSO algorithm is prone to fall into local optimization,path smoothing is always carried out after all the path planning steps,and the path fitness function is so simple that it cannot adapt to complex marine environment.The adaptive inertia weight and the“active”particle of the fish swarm algorithm are established to improve the global search and local search ability of the algorithm.The cubic spline interpolation method is combined with PSO to smooth the path in real time.The fitness function of the algorithm is optimized.Five evaluation indexes are comprehensively considered to solve the three-demensional(3D)path planning problem of AUV in the ocean currents and internal wave environment.The proposed method improves the safety of the path planning and saves energy.
基金supported by the National Natural Science Foundation of China (No.61175084)the Program for Changjiang Scholars and Innovative Research Team in University of Ministry of Education of China (No.IRT13004)
文摘In this paper, a novel algorithm based on disturbed fluid and trajectory propagation is developed to solve the three-dimensional（3-D） path planning problem of unmanned aerial vehicle（UAV） in static environment.Firstly, inspired by the phenomenon of streamlines avoiding obstacles, the algorithm based on disturbed fluid is developed and broadened.The effect of obstacles on original fluid field is quantified by the perturbation matrix, where the tangential matrix is first introduced.By modifying the original flow field, the modified one is then obtained, where the streamlines can be regarded as planned paths.And the path proves to avoid all obstacles smoothly and swiftly, follow the shape of obstacles effectively and reach the destination eventually.Then, by considering the kinematics and dynamics equations of UAV, the method called trajectory propagation is adopted to judge the feasibility of the path.If the planned path is unfeasible, repulsive and tangential parameters in the perturbation matrix will be adjusted adaptively based on the resolved state variables of UAV.In most cases, a flyable path can be obtained eventually.Simulation results demonstrate the effectiveness of this method.
基金co-supported by China Scholarship Council (No. 201604000003)the National Natural Science Foundation of China (Nos. U1433203, U1533119 and L142200032)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No. 61221061)
文摘In this paper, a four-dimensional coordinated path planning algorithm for multiple UAVs is proposed, in which time variable is taken into account for each UAV as well as collision free and obstacle avoidance. A Spatial Refined Voting Mechanism(SRVM) is designed for standard Particle Swarm Optimization(PSO) to overcome the defects of local optimal and slow convergence.For each generation candidate particle positions are recorded and an adaptive cube is formed with own adaptive side length to indicate occupied regions. Then space voting begins and is sorted based on voting results, whose centers with bigger voting counts are seen as sub-optimal positions. The average of all particles of corresponding dimensions are calculated as the refined solutions. A time coordination method is developed by generating specified candidate paths for every UAV, making them arrive the same destination with the same time consumption. A spatial-temporal collision avoidance technique is introduced to make collision free. Distance to destination is constructed to improve the searching accuracy and velocity of particles. In addition, the objective function is redesigned by considering the obstacle and threat avoidance, Estimated Time of Arrival(ETA), separation maintenance and UAV self-constraints. Experimental results prove the effectiveness and efficiency of the algorithm.
基金supported by National Natural Science Foundation of China(No.61305128)Fundamental Research Funds for the Central Universities,and U.S.Army Research Ofce(No.W911NF-091-0565)
文摘This paper presents a 3D path planning algorithm for an unmanned aerial vehicle （UAV） in complex environments. In this algorithm, the environments are divided into voxels by octree algorithm. In order to satisfy the safety requirement of the UAV, free space is represented by free voxels, which have enough space margin for the UAV to pass through. A bounding box array is created in the whole 3D space to evaluate the free voxel connectivity. The probabilistic roadmap method （PRM） is improved by random sampling in the bounding box array to ensure a more efficient distribution of roadmap nodes in 3D space. According to the connectivity evaluation, the roadmap is used to plan a feasible path by using A＊ algorithm. Experimental results indicate that the proposed algorithm is valid in complex 3D environments.
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.
基金Supported by National Natural Science Foundation of China (50875165)
文摘Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D path planning, a modified ant colony optimization is proposed in this paper. Firstly, the grid method for environment modeling is adopted. Heuristic information is connected with the planning space. A semi-iterative global pheromone update mechanism is proposed. Secondly, the optimal ants mutate the paths to improve the diversity of the algorithm after a defined iterative number. Thirdly, co-evolutionary algorithm is used. Finally, the simulation result shows the effectiveness of the proposed algorithm in solving the problem of 3D pipe path planning.
基金supported by the Postgraduate Research&Practice Innovation Program of Nanjing University of Aeronautics and Astronautics(NUAA)(No.xcxjh20251502)。
文摘Online three-dimensional(3D)path planning in dynamic environments is a fundamental problem for achieving autonomous navigation of unmanned aerial vehicles(UAVs).However,existing methods struggle to model traversable dynamic gaps,resulting in conservative and suboptimal trajectories.To address these challenges,this paper proposes a hierarchical reinforcement learning(RL)framework that integrates global path guidance,local trajectory generation,predictive safety evaluation,and neural network-based decision-making.Specifically,the global planner provides long-term navigation guidance,and the local module then utilizes an improved 3D dynamic window approach(DWA)to generate dynamically feasible candidate trajectories.To enhance safety in dense dynamic scenarios,the algorithm introduces a predictive axis-aligned bounding box(AABB)strategy to model the future occupancy of obstacles,combined with convex hull verification for efficient trajectory safety assessment.Furthermore,a double deep Q-network(DDQN)is employed with structured feature encoding,enabling the neural network to reliably select the optimal trajectory from the candidate set,thereby improving robustness and generalization.Comparative experiments conducted in a high-fidelity simulation environment show that the algorithm outperforms existing algorithms,reducing the average number of collisions to 0.2 while shortening the average task completion time by approximately 15%,and achieving a success rate of 97%.
基金Project(60925011) supported by the National Natural Science Foundation for Distinguished Young Scholars of ChinaProject(9140A06040510BQXXXX) supported by Advanced Research Foundation of General Armament Department,China
文摘To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned aerial vehicle (UAV) low-altitude penetration,a novel route planning method was proposed.First and foremost,a coevolutionary multi-agent genetic algorithm (CE-MAGA) was formed by introducing coevolutionary mechanism to multi-agent genetic algorithm (MAGA),an efficient global optimization algorithm.A dynamic route representation form was also adopted to improve the flight route accuracy.Moreover,an efficient constraint handling method was used to simplify the treatment of multi-constraint and reduce the time-cost of planning computation.Simulation and corresponding analysis show that the planning results of CE-MAGA have better performance on terrain following,terrain avoidance,threat avoidance (TF/TA2) and lower route costs than other existing algorithms.In addition,feasible flight routes can be acquired within 2 s,and the convergence rate of the whole evolutionary process is very fast.
文摘Complete and efficient detection of unknown targets is the most popular application of UAV swarms. Under most situations, targets have directional characteristics so that they can only be successfully detected within specific angles. In such cases, how to coordinate UAVs and allocate optimal paths for them to efficiently detect all the targets is the primary issue to be solved. In this paper, an intelligent target detection method is proposed for UAV swarms to achieve real-time detection requirements. First, a target-feature-information-based disintegration method is built up to divide the search space into a set of cubes. Theoretically, when the cubes are traversed, all the targets can be detected. Then, a Kuhn-Munkres(KM)-algorithm-based path planning method is proposed for UAVs to traverse the cubes. Finally, to further improve search efficiency, a 3 D realtime probability map is established over the search space which estimates the possibility of detecting new targets at each point. This map is adopted to modify the weights in KM algorithm, thereby optimizing the UAVs’ paths during the search process. Simulation results show that with the proposed method, all targets, with detection angle limitations, can be found by UAVs. Moreover, by implementing the 3 D probability map, the search efficiency is improved by 23.4%–78.1%.
基金supported by National Natural Science Foundation of China(Nos.51475281 and 51375292)National Youth Foundation of China(No.51105239)
文摘Recently in the area of biological manufacturing and rapid prototyping manufacturing, the bone scaffolds based on the additive manufacturing in repairing bone defects have been paid more and more attention. In the process of preparation, path planning directly affects the structure, performance as well as the final bone cell culture conditions. Due to the special natural bone scaffold structural characteristic, the traditional rapid prototyping(RP) path planning is not fully suitable for the preparation of bone scaffolds. In this paper, based on the 3D printing extrusion forming technology, a method of path planning for osteochondral integrated scaffolds with gradient structure is put forward, which provides a theoretical basis for bone-scaffold modeling and practical preparation. The implementation of the path planning processing system makes it possible to process data automatically from the initial stereo lithography(STL) model of the actual bone defect part by computer X-ray tomography technique(CT) scan or modeling,to generate the path code and to generate the final machining information after post-processing. This work provides some guidelines for independent research and development of automation equipment for biological manufacturing preparation and software technology.The experiment and test results have verified the validity of the path planning method and the good properties of the bone scaffolds with gradient structures.
基金funded by the National Science Foundation of China,Nos.51674121 and 61702184the Returned Overseas Scholar Funding of Hebei Province,No.C2015005014the Hebei Key Laboratory of Science and Application,and Tangshan Innovation Team Project,No.18130209B.
文摘Based on patient computerized tomography data,we segmented a region containing an intracranial hematoma using the threshold method and reconstructed the 3D hematoma model.To improve the efficiency and accuracy of identifying puncture points,a point-cloud search arithmetic method for modified adaptive weighted particle swarm optimization is proposed and used for optimal external axis extraction.According to the characteristics of the multitube drainage tube and the clinical needs of puncture for intracranial hematoma removal,the proposed algorithm can provide an optimal route for a drainage tube for the hematoma,the precise position of the puncture point,and preoperative planning information,which have considerable instructional significance for clinicians.
