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
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
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
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
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
Existing solutions for collaborative trajectory planning using multiple UAVs suffer from issues such as low accuracy,instability,and slow convergence.To address the aforementioned issues,this paper introduces a new me...Existing solutions for collaborative trajectory planning using multiple UAVs suffer from issues such as low accuracy,instability,and slow convergence.To address the aforementioned issues,this paper introduces a new method for multiple unmanned aerial vehicle(UAV)3D terrain cooperative trajectory planning based on the cuck0o search golden jackal optimization(CS-GJO)algorithm.A model for single UAV trajectory planning and a model for multi-UAV collaborative trajectory planning have been developed,and the problem of solving the models is restructured into an optimization problem.Building upon the original golden jackal optimization,the use of tent chaotic mapping aids in the generation of the golden jackal's inital population,thereby promoting population diversity.Subsequently,the position update strategy of the cuckoo search algorithm is combined for purpose of update the position information of individual golden jackals,effectively preventing the algorithm from getting stuck in local minima.Finally,the corresponding nonlinear control parameter were developed.The new parameters expedite the decrease in the convergence factor during the pre-exploration stage,resulting in an improved overall search speed of the algorithm.Moreover,they attenuate the decrease in the convergence factor during the post-exploration stage,thereby enhancing the algorithm's global search.The experimental results demonstrate that the CS-GJO algorithm efficiently and accurately accomplishes multi-UAV cooperative trajectory planning in a 3D environment.Compared with other comparative algorithms,the CS-GJO algorithm also has better stability,higher optimization accuracy,and faster convergence speed.展开更多
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.
基金supported by the Key Research and Development Program of Henan Province (No.241111222900)Natural Science Foundation of Henan (No.242300421716)+2 种基金Key Science and Technology Program of Henan Province (Nos.242102220044 and 242102210034)National Natural Science Foundation of China (No.62103379)Maker Space Incubation Project (No.2023ZCKJ102).
文摘Existing solutions for collaborative trajectory planning using multiple UAVs suffer from issues such as low accuracy,instability,and slow convergence.To address the aforementioned issues,this paper introduces a new method for multiple unmanned aerial vehicle(UAV)3D terrain cooperative trajectory planning based on the cuck0o search golden jackal optimization(CS-GJO)algorithm.A model for single UAV trajectory planning and a model for multi-UAV collaborative trajectory planning have been developed,and the problem of solving the models is restructured into an optimization problem.Building upon the original golden jackal optimization,the use of tent chaotic mapping aids in the generation of the golden jackal's inital population,thereby promoting population diversity.Subsequently,the position update strategy of the cuckoo search algorithm is combined for purpose of update the position information of individual golden jackals,effectively preventing the algorithm from getting stuck in local minima.Finally,the corresponding nonlinear control parameter were developed.The new parameters expedite the decrease in the convergence factor during the pre-exploration stage,resulting in an improved overall search speed of the algorithm.Moreover,they attenuate the decrease in the convergence factor during the post-exploration stage,thereby enhancing the algorithm's global search.The experimental results demonstrate that the CS-GJO algorithm efficiently and accurately accomplishes multi-UAV cooperative trajectory planning in a 3D environment.Compared with other comparative algorithms,the CS-GJO algorithm also has better stability,higher optimization accuracy,and faster convergence speed.
