Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always ...Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.展开更多
Path planning algorithm is the key point to UAV path planning scenario.Many traditional path planning methods still suffer from low convergence rate and insufficient robustness.In this paper,three main methods are con...Path planning algorithm is the key point to UAV path planning scenario.Many traditional path planning methods still suffer from low convergence rate and insufficient robustness.In this paper,three main methods are contributed to solving these problems.First,the improved artificial potential field(APF)method is adopted to accelerate the convergence process of the bat’s position update.Second,the optimal success rate strategy is proposed to improve the adaptive inertia weight of bat algorithm.Third chaos strategy is proposed to avoid falling into a local optimum.Compared with standard APF and chaos strategy in UAV path planning scenarios,the improved algorithm CPFIBA(The improved artificial potential field method combined with chaotic bat algorithm,CPFIBA)significantly increases the success rate of finding suitable planning path and decrease the convergence time.Simulation results show that the proposed algorithm also has great robustness for processing with path planning problems.Meanwhile,it overcomes the shortcomings of the traditional meta-heuristic algorithms,as their convergence process is the potential to fall into a local optimum.From the simulation,we can see also obverse that the proposed CPFIBA provides better performance than BA and DEBA in problems of UAV 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
Unmanned aerial vehicle(UAV)path planning plays an important role in power systems.In order to address the challenge in UAV path planning,an improved crested porcupine optimizer(ICPO)combining the Cauchy inverse cumul...Unmanned aerial vehicle(UAV)path planning plays an important role in power systems.In order to address the challenge in UAV path planning,an improved crested porcupine optimizer(ICPO)combining the Cauchy inverse cumulative distribution function and JAYA algorithm is proposed in this paper.First,the traditional random initialization is replaced by sine chaotic mapping,making the initial population more evenly distributed in the search space and improving the quality of the initial solution.Since the global search ability of the crested porcupine optimizer(CPO)is limited,the Cauchy inverse cumulative distribution strategy is introduced.In addition,as CPO is prone to fall into local optima in later stages,a weighted JAYA-CPO attack strategy is proposed to balance the global exploration and local exploitation,thereby improving the algorithm’s ability to escape from local optima.Finally,ICPO is compared with another 10 algorithms on the cec2017 and cec2020 test sets.The experimental results show that ICPO has excellent competitiveness and optimization performance.The ICPO algorithm is applied to the path planning problem of power inspection UAV and is compared with four algorithms.The results show that the algorithm can generate more feasible path trajectories across two terrains with varying complexity,demonstrating the effectiveness and significance of the ICPO algorithm for UAV power inspection path planning.展开更多
Efficient flight path design for unmanned aerial vehicles(UAVs)in urban environmental event monitoring remains a critical challenge,particularly in prioritizing high-risk zones within complex urban landscapes.Current ...Efficient flight path design for unmanned aerial vehicles(UAVs)in urban environmental event monitoring remains a critical challenge,particularly in prioritizing high-risk zones within complex urban landscapes.Current UAV path planning methodologies often inadequately account for environmental risk factors and exhibit limitations in balancing global and local optimization efficiency.To address these gaps,this study proposes a hybrid path planning framework integrating an improved Ant Colony Optimization(ACO)algorithm with an Orthogonal Jump Point Search(OJPS)algorithm.Firstly,a two-dimensional grid model is constructed to simulate urban environments,with key monitoring nodes selected based on grid-specific environmental risk values.Subsequently,the improved ACO algorithm is used for global path planning,and the OJPS algorithm is integrated to optimize the local path.The improved ACO algorithm introduces the risk value of environmental events,which is used to direct the UAV to the area with higher risk.In the OJPS algorithm,the path search direction is restricted to the orthogonal direction,which improves the computational efficiency of local path optimization.In order to evaluate the performance of the model,this paper utilizes the metrics of the average risk value of the path,the flight time,and the number of turns.The experimental results demonstrate that the proposed improved ACO algorithm performs well in the average risk value of the paths traveled within the first 5 min,within the first 8 min,and within the first 10 min,with improvements of 48.33%,26.10%,and 6.746%,respectively,over the Particle Swarm Optimization(PSO)algorithm and 70.33%,19.08%,and 10.246%,respectively,over theArtificial Rabbits Optimization(ARO)algorithm.TheOJPS algorithmdemonstrates superior performance in terms of flight time and number of turns,exhibiting a reduction of 40%,40%and 57.1%in flight time compared to the other three algorithms,and a reduction of 11.1%,11.1%and 33.8%in the number of turns compared to the other three algorithms.These results highlight the effectiveness of the proposed method in improving the UAV’s ability to respond efficiently to urban environmental events,offering significant implications for the future of UAV path planning in complex urban settings.展开更多
Optimization algorithms play a pivotal role in enhancing the performance and efficiency of systems across various scientific and engineering disciplines.To enhance the performance and alleviate the limitations of the ...Optimization algorithms play a pivotal role in enhancing the performance and efficiency of systems across various scientific and engineering disciplines.To enhance the performance and alleviate the limitations of the Northern Goshawk Optimization(NGO)algorithm,particularly its tendency towards premature convergence and entrapment in local optima during function optimization processes,this study introduces an advanced Improved Northern Goshawk Optimization(INGO)algorithm.This algorithm incorporates a multifaceted enhancement strategy to boost operational efficiency.Initially,a tent chaotic map is employed in the initialization phase to generate a diverse initial population,providing high-quality feasible solutions.Subsequently,after the first phase of the NGO’s iterative process,a whale fall strategy is introduced to prevent premature convergence into local optima.This is followed by the integration of T-distributionmutation strategies and the State Transition Algorithm(STA)after the second phase of the NGO,achieving a balanced synergy between the algorithm’s exploitation and exploration.This research evaluates the performance of INGO using 23 benchmark functions alongside the IEEE CEC 2017 benchmark functions,accompanied by a statistical analysis of the results.The experimental outcomes demonstrate INGO’s superior achievements in function optimization tasks.Furthermore,its applicability in solving engineering design problems was verified through simulations on Unmanned Aerial Vehicle(UAV)trajectory planning issues,establishing INGO’s capability in addressing complex optimization challenges.展开更多
基金supported by the National Natural Science Foundation of China(72571094,72271076,71871079)。
文摘Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.
基金This project is supported by National Science Foundation for Young Scientists of China(61701322)the Key Projects of Liaoning Natural Science Foundation(20170540700)+3 种基金the Key Projects of Liaoning Provincial Department of Education Science Foundation(L201702)Liaoning Natural Science Foundation(201502008,20102175)the Program for Liaoning Excellent Talents in University(LJQ2012011)the Liaoning Provincial Department of Education Science Foundation(L201630).
文摘Path planning algorithm is the key point to UAV path planning scenario.Many traditional path planning methods still suffer from low convergence rate and insufficient robustness.In this paper,three main methods are contributed to solving these problems.First,the improved artificial potential field(APF)method is adopted to accelerate the convergence process of the bat’s position update.Second,the optimal success rate strategy is proposed to improve the adaptive inertia weight of bat algorithm.Third chaos strategy is proposed to avoid falling into a local optimum.Compared with standard APF and chaos strategy in UAV path planning scenarios,the improved algorithm CPFIBA(The improved artificial potential field method combined with chaotic bat algorithm,CPFIBA)significantly increases the success rate of finding suitable planning path and decrease the convergence time.Simulation results show that the proposed algorithm also has great robustness for processing with path planning problems.Meanwhile,it overcomes the shortcomings of the traditional meta-heuristic algorithms,as their convergence process is the potential to fall into a local optimum.From the simulation,we can see also obverse that the proposed CPFIBA provides better performance than BA and DEBA in problems of UAV 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supported by the National Natural Sci-ence Foundation of China(No.62102373,No.62273243,and No.62473341)Henan Province Key R&D Project(No.241111210400)Joint Fund Key Project of science and Technology R&D Plan of Henan Province(No.235200810022).
文摘Unmanned aerial vehicle(UAV)path planning plays an important role in power systems.In order to address the challenge in UAV path planning,an improved crested porcupine optimizer(ICPO)combining the Cauchy inverse cumulative distribution function and JAYA algorithm is proposed in this paper.First,the traditional random initialization is replaced by sine chaotic mapping,making the initial population more evenly distributed in the search space and improving the quality of the initial solution.Since the global search ability of the crested porcupine optimizer(CPO)is limited,the Cauchy inverse cumulative distribution strategy is introduced.In addition,as CPO is prone to fall into local optima in later stages,a weighted JAYA-CPO attack strategy is proposed to balance the global exploration and local exploitation,thereby improving the algorithm’s ability to escape from local optima.Finally,ICPO is compared with another 10 algorithms on the cec2017 and cec2020 test sets.The experimental results show that ICPO has excellent competitiveness and optimization performance.The ICPO algorithm is applied to the path planning problem of power inspection UAV and is compared with four algorithms.The results show that the algorithm can generate more feasible path trajectories across two terrains with varying complexity,demonstrating the effectiveness and significance of the ICPO algorithm for UAV power inspection path planning.
基金supported by the Special Project forKey Fields of Ordinary Universities in Guangdong Province(Number:2023ZDZX1076).
文摘Efficient flight path design for unmanned aerial vehicles(UAVs)in urban environmental event monitoring remains a critical challenge,particularly in prioritizing high-risk zones within complex urban landscapes.Current UAV path planning methodologies often inadequately account for environmental risk factors and exhibit limitations in balancing global and local optimization efficiency.To address these gaps,this study proposes a hybrid path planning framework integrating an improved Ant Colony Optimization(ACO)algorithm with an Orthogonal Jump Point Search(OJPS)algorithm.Firstly,a two-dimensional grid model is constructed to simulate urban environments,with key monitoring nodes selected based on grid-specific environmental risk values.Subsequently,the improved ACO algorithm is used for global path planning,and the OJPS algorithm is integrated to optimize the local path.The improved ACO algorithm introduces the risk value of environmental events,which is used to direct the UAV to the area with higher risk.In the OJPS algorithm,the path search direction is restricted to the orthogonal direction,which improves the computational efficiency of local path optimization.In order to evaluate the performance of the model,this paper utilizes the metrics of the average risk value of the path,the flight time,and the number of turns.The experimental results demonstrate that the proposed improved ACO algorithm performs well in the average risk value of the paths traveled within the first 5 min,within the first 8 min,and within the first 10 min,with improvements of 48.33%,26.10%,and 6.746%,respectively,over the Particle Swarm Optimization(PSO)algorithm and 70.33%,19.08%,and 10.246%,respectively,over theArtificial Rabbits Optimization(ARO)algorithm.TheOJPS algorithmdemonstrates superior performance in terms of flight time and number of turns,exhibiting a reduction of 40%,40%and 57.1%in flight time compared to the other three algorithms,and a reduction of 11.1%,11.1%and 33.8%in the number of turns compared to the other three algorithms.These results highlight the effectiveness of the proposed method in improving the UAV’s ability to respond efficiently to urban environmental events,offering significant implications for the future of UAV path planning in complex urban settings.
基金supported by theKey Research and Development Project of Hubei Province(No.2023BAB094)the Key Project of Science and Technology Research Program of Hubei Educational Committee(No.D20211402)the Open Foundation of HubeiKey Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System(No.HBSEES202309).
文摘Optimization algorithms play a pivotal role in enhancing the performance and efficiency of systems across various scientific and engineering disciplines.To enhance the performance and alleviate the limitations of the Northern Goshawk Optimization(NGO)algorithm,particularly its tendency towards premature convergence and entrapment in local optima during function optimization processes,this study introduces an advanced Improved Northern Goshawk Optimization(INGO)algorithm.This algorithm incorporates a multifaceted enhancement strategy to boost operational efficiency.Initially,a tent chaotic map is employed in the initialization phase to generate a diverse initial population,providing high-quality feasible solutions.Subsequently,after the first phase of the NGO’s iterative process,a whale fall strategy is introduced to prevent premature convergence into local optima.This is followed by the integration of T-distributionmutation strategies and the State Transition Algorithm(STA)after the second phase of the NGO,achieving a balanced synergy between the algorithm’s exploitation and exploration.This research evaluates the performance of INGO using 23 benchmark functions alongside the IEEE CEC 2017 benchmark functions,accompanied by a statistical analysis of the results.The experimental outcomes demonstrate INGO’s superior achievements in function optimization tasks.Furthermore,its applicability in solving engineering design problems was verified through simulations on Unmanned Aerial Vehicle(UAV)trajectory planning issues,establishing INGO’s capability in addressing complex optimization challenges.
