This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously a...This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.展开更多
Combat effectiveness of unmanned aerial vehicle(UAV)formations can be severely affected by the mission execution reliability.During the practical execution phase,there are inevitable risks where UAVs being destroyed o...Combat effectiveness of unmanned aerial vehicle(UAV)formations can be severely affected by the mission execution reliability.During the practical execution phase,there are inevitable risks where UAVs being destroyed or targets failed to be executed.To improve the mission reliability,a resilient mission planning framework integrates task pre-and re-assignment modules is developed in this paper.In the task pre-assignment phase,to guarantee the mission reliability,probability constraints regarding the minimum mission success rate are imposed to establish a multi-objective optimization model.And an improved genetic algorithm with the multi-population mechanism and specifically designed evolutionary operators is used for efficient solution.As in the task-reassignment phase,possible trigger events are first analyzed.A real-time contract net protocol-based algorithm is then proposed to address the corresponding emergency scenario.And the dual objective used in the former phase is adapted into a single objective to keep a consistent combat intention.Three cases of different scales demonstrate that the two modules cooperate well with each other.On the one hand,the pre-assignment module can generate high-reliability mission schedules as an elaborate mathematical model is introduced.On the other hand,the re-assignment module can efficiently respond to various emergencies and adjust the original schedule within a millisecond.The corresponding animation is accessible at bilibili.com/video/BV12t421w7EE for better illustration.展开更多
In the last decade,space solar power satellites(SSPSs)have been conceived to support net-zero carbon emissions and have attracted considerable attention.Electric energy is transmitted to the ground via a microwave pow...In the last decade,space solar power satellites(SSPSs)have been conceived to support net-zero carbon emissions and have attracted considerable attention.Electric energy is transmitted to the ground via a microwave power beam,a technology known as microwave power transmission(MPT).Due to the vast transmission distance of tens of thousands of kilometers,the power transmitting antenna array must span up to 1 kilometer in diameter.At the same time,the size of the rectifying array on the ground should extend over a few kilometers.This makes the MPT system of SSPSs significantly larger than the existing aerospace engineering system.To design and operate a rational MPT system,comprehensive optimization is required.Taking the space MPT system engineering into consideration,a novel multi-objective optimization function is proposed and further analyzed.The multi-objective optimization problem is modeled mathematically.Beam collection efficiency(BCE)is the primary factor,followed by the thermal management capability.Some tapers,designed to solve the conflict between BCE and the thermal problem,are reviewed.In addition to these two factors,rectenna design complexity is included as a functional factor in the optimization objective.Weight coefficients are assigned to these factors to prioritize them.Radiating planar arrays with different aperture illumination fields are studied,and their performances are compared using the multi-objective optimization function.Transmitting array size,rectifying array size,transmission distance,and transmitted power remaine constant in various cases,ensuring fair comparisons.The analysis results show that the proposed optimization function is effective in optimizing and selecting the MPT system architecture.It is also noted that the multi-objective optimization function can be expanded to include other factors in the future.展开更多
The lack of systematic and scientific top-level arrangement in the field of civil aircraft flight test leads to the problems of long duration and high cost.Based on the flight test activity,mathematical models of flig...The lack of systematic and scientific top-level arrangement in the field of civil aircraft flight test leads to the problems of long duration and high cost.Based on the flight test activity,mathematical models of flight test duration and cost are established to set up the framework of flight test process.The top-level arrangement for flight test is optimized by multi-objective algorithm to reduce the duration and cost of flight test.In order to verify the necessity and validity of the mathematical models and the optimization algorithm of top-level arrangement,real flight test data is used to make an example calculation.Results show that the multi-objective optimization results of the top-level flight arrangement are better than the initial arrangement data,which can shorten the duration,reduce the cost,and improve the efficiency of flight test.展开更多
Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and miss...Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and mission trajectory planning method is proposed to meet the requirements of cross-domain unmanned swarm mission planning.Firstly,the different performances of cross-domain heterogeneous platforms and mission requirements of targets are characterised by using a collection of operational resources.Secondly,an algorithmic framework for joint target assignment and mission trajectory planning is proposed,in which the initial planning of the trajectory is performed in the target assignment phase,while the trajectory is further optimised afterwards.Next,the estimation of the distribution algorithms is combined with the genetic algorithm to solve the objective function.Finally,the algorithm is numerically simulated by specific cases.Simulation results indicate that the proposed algorithm can perform effective task assignment and trajectory planning for cross-domain unmanned swarms.Furthermore,the solution performance of the hybrid estimation of distribution algorithm(EDA)-genetic algorithm(GA)algorithm is better than that of GA and EDA.展开更多
Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threat...Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threaten flight safety and mission success.Traditional path planning methods typically depend solely on the distribution of static obstacles to generate collision-free paths,without accounting for constraints imposed by enemy detection and strike capabilities.Such a simplified approach can yield safety-compromising routes in highly complex urban airspace.To address these limitations,this study proposes a multi-parameter path planning method based on reachable airspace visibility graphs,which integrates UAV performance constraints,environmental limitations,and exposure risks.An innovative heuristic algorithm is developed to balance operational safety and efficiency by both exposure risks and path length.In the case study set in a typical mixed-use urban area,analysis of airspace visibility graphs reveals significant variations in exposure risk at different regions and altitudes due to building encroachments.Path optimization results indicate that the method can effectively generate covert and efficient flight paths by dynamically adjusting the exposure index,which represents the likelihood of enemy detection,and the path length,which corresponds to mission execution time.展开更多
A three-dimensional path-planning approach has been developed to coordinate multiple fixed-wing unmanned aerial vehicles(UAVs)while avoiding collisions.The hierarchical path-planning architecture that divides the path...A three-dimensional path-planning approach has been developed to coordinate multiple fixed-wing unmanned aerial vehicles(UAVs)while avoiding collisions.The hierarchical path-planning architecture that divides the path-planning process into two layers is proposed by designing the velocityobstacle strategy for satisfying timeliness and effectiveness.The upper-level layer focuses on creating an efficient Dubins initial path considering the dynamic constraints of the fixed wing.Subsequently,the lower-level layer detects potential collisions and adjusts its flight paths to avoid collisions by using the threedimensional velocity obstacle method,which describes the maneuvering space of collision avoidance as the intersection space of half space.To further handle the dynamic and collisionavoidance constraints,a priority mechanism is designed to ensure that the adjusted path is still feasible for fixed-wing UAVs.Simulation experiments demonstrate the effectiveness of the proposed method.展开更多
To solve the problem of multi-platform collaborative use in anti-ship missile (ASM) path planning, this paper pro-posed multi-operator real-time constraints particle swarm opti-mization (MRC-PSO) algorithm. MRC-PSO al...To solve the problem of multi-platform collaborative use in anti-ship missile (ASM) path planning, this paper pro-posed multi-operator real-time constraints particle swarm opti-mization (MRC-PSO) algorithm. MRC-PSO algorithm utilizes a semi-rasterization environment modeling technique and inte-grates the geometric gradient law of ASMs which distinguishes itself from other collaborative path planning algorithms by fully considering the coupling between collaborative paths. Then, MRC-PSO algorithm conducts chunked stepwise recursive evo-lution of particles while incorporating circumvent, coordination, and smoothing operators which facilitates local selection opti-mization of paths, gradually reducing algorithmic space, accele-rating convergence, and enhances path cooperativity. Simula-tion experiments comparing the MRC-PSO algorithm with the PSO algorithm, genetic algorithm and operational area cluster real-time restriction (OACRR)-PSO algorithm, which demon-strate that the MRC-PSO algorithm has a faster convergence speed, and the average number of iterations is reduced by approximately 75%. It also proves that it is equally effective in resolving complex scenarios involving multiple obstacles. More-over it effectively addresses the problem of path crossing and can better satisfy the requirements of multi-platform collabora-tive path planning. The experiments are conducted in three col-laborative operation modes, namely, three-to-two, three-to-three, and four-to-two, and the outcomes demonstrate that the algorithm possesses strong universality.展开更多
The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy b...The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.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
With the rapid development of low-altitude economy and unmanned aerial vehicles (UAVs) deployment technology, aerial-ground collaborative delivery (AGCD) is emerging as a novel mode of last-mile delivery, where the ve...With the rapid development of low-altitude economy and unmanned aerial vehicles (UAVs) deployment technology, aerial-ground collaborative delivery (AGCD) is emerging as a novel mode of last-mile delivery, where the vehicle and its onboard UAVs are utilized efficiently. Vehicles not only provide delivery services to customers but also function as mobile ware-houses and launch/recovery platforms for UAVs. This paper addresses the vehicle routing problem with UAVs considering time window and UAV multi-delivery (VRPU-TW&MD). A mixed integer linear programming (MILP) model is developed to mini-mize delivery costs while incorporating constraints related to UAV energy consumption. Subsequently, a micro-evolution aug-mented large neighborhood search (MEALNS) algorithm incor-porating adaptive large neighborhood search (ALNS) and micro-evolution mechanism is proposed. Numerical experiments demonstrate the effectiveness of both the model and algorithm in solving the VRPU-TW&MD. The impact of key parameters on delivery performance is explored by sensitivity analysis.展开更多
Complex multi-area collaborative coverage path planning in dynamic environments poses a significant challenge for multi-fixed-wing UAVs(multi-UAV).This study establishes a comprehensive framework that incorporates UAV...Complex multi-area collaborative coverage path planning in dynamic environments poses a significant challenge for multi-fixed-wing UAVs(multi-UAV).This study establishes a comprehensive framework that incorporates UAV capabilities,terrain,complex areas,and mission dynamics.A novel dynamic collaborative path planning algorithm is introduced,designed to ensure complete coverage of designated areas.This algorithm meticulously optimizes the operation,entry,and transition paths for each UAV,while also establishing evaluation metrics to refine coverage sequences for each area.Additionally,a three-dimensional path is computed utilizing an altitude descent method,effectively integrating twodimensional coverage paths with altitude constraints.The efficacy of the proposed approach is validated through digital simulations and mixed-reality semi-physical experiments across a variety of dynamic scenarios,including both single-area and multi-area coverage by multi-UAV.Results show that the coverage paths generated by this method significantly reduce both computation time and path length,providing a reliable solution for dynamic multi-UAV mission planning in semi-physical environments.展开更多
Unmanned aerial vehicle(UAV)was introduced as a novel traffic device to collect road traffic information and its cruise route planning problem was considered.Firstly,a multi-objective optimization model was proposed a...Unmanned aerial vehicle(UAV)was introduced as a novel traffic device to collect road traffic information and its cruise route planning problem was considered.Firstly,a multi-objective optimization model was proposed aiming at minimizing the total cruise distance and the number of UAVs used,which used UAV maximum cruise distance,the number of UAVs available and time window of each monitored target as constraints.Then,a novel multi-objective evolutionary algorithm was proposed.Next,a case study with three time window scenarios was implemented.The results show that both the total cruise distance and the number of UAVs used continue to increase with the time window constraint becoming narrower.Compared with the initial optimal solutions,the optimal total cruise distance and the number of UAVs used fall by an average of 30.93% and 31.74%,respectively.Finally,some concerns using UAV to collect road traffic information were discussed.展开更多
In the past few decades, applications of geostationary orbit (GEO) satellites have attracted increasing attention, and with the development of optical technologies, GEO optical satellites have become popular worldwide...In the past few decades, applications of geostationary orbit (GEO) satellites have attracted increasing attention, and with the development of optical technologies, GEO optical satellites have become popular worldwide. This paper proposes a general working pattern for a GEO optical satellite, as well as a target observation mission planning model. After analyzing the requirements of users and satellite control agencies, two objectives are simultaneously considered: maximization of total profit and minimization of satellite attitude maneuver angle. An NSGA-II based multi-objective optimization algorithm is proposed, which contains some heuristic principles in the initialization phase and mutation operator, and is embedded with a traveling salesman problem (TSP) optimization. The validity and performance of the proposed method are verified by extensive numerical simulations that include several types of point target distributions.展开更多
The rapid evolution of unmanned aerial vehicle(UAV)technology and autonomous capabilities has positioned UAV as promising last-mile delivery means.Vehicle and onboard UAV collaborative delivery is introduced as a nove...The rapid evolution of unmanned aerial vehicle(UAV)technology and autonomous capabilities has positioned UAV as promising last-mile delivery means.Vehicle and onboard UAV collaborative delivery is introduced as a novel delivery mode.Spatiotemporal collaboration,along with energy consumption with payload and wind conditions play important roles in delivery route planning.This paper introduces the traveling salesman problem with time window and onboard UAV(TSPTWOUAV)and emphasizes the consideration of real-world scenarios,focusing on time collaboration and energy consumption with wind and payload.To address this,a mixed integer linear programming(MILP)model is formulated to minimize the energy consumption costs of vehicle and UAV.Furthermore,an adaptive large neighborhood search(ALNS)algorithm is applied to identify high-quality solutions efficiently.The effectiveness of the proposed model and algorithm is validated through numerical tests on real geographic instances and sensitivity analysis of key parameters is conducted.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
Homing trajectory planning is a core task of autonomous homing of parafoil system.This work analyzes and establishes a simplified kinematic mathematical model,and regards the homing trajectory planning problem as a ki...Homing trajectory planning is a core task of autonomous homing of parafoil system.This work analyzes and establishes a simplified kinematic mathematical model,and regards the homing trajectory planning problem as a kind of multi-objective optimization problem.Being different from traditional ways of transforming the multi-objective optimization into a single objective optimization by weighting factors,this work applies an improved non-dominated sorting genetic algorithm Ⅱ(NSGA Ⅱ) to solve it directly by means of optimizing multi-objective functions simultaneously.In the improved NSGA Ⅱ,the chaos initialization and a crowding distance based population trimming method were introduced to overcome the prematurity of population,the penalty function was used in handling constraints,and the optimal solution was selected according to the method of fuzzy set theory.Simulation results of three different schemes designed according to various practical engineering requirements show that the improved NSGA Ⅱ can effectively obtain the Pareto optimal solution set under different weighting with outstanding convergence and stability,and provide a new train of thoughts to design homing trajectory of parafoil system.展开更多
Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a gro...Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a ground threat prediction-based path planning method is proposed based on artificial bee colony(ABC)algorithm by collaborative thinking strategy.Firstly,a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats.The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time.Then,a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats.By adding the collision warning mechanism to the path planning model,the flight path could be dynamically adjusted according to changing no-fly zones.Furthermore,a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy.The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution,and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy,which makes the optimization performance of ABC algorithm more controllable and efficient.Finally,simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.展开更多
For the deep understanding on combustion of ammonia/diesel,this study develops a reduced mechanism of ammonia/diesel with 227 species and 937 reactions.The sub-mechanism on ammonia/interactions of N-based and C-based ...For the deep understanding on combustion of ammonia/diesel,this study develops a reduced mechanism of ammonia/diesel with 227 species and 937 reactions.The sub-mechanism on ammonia/interactions of N-based and C-based species(N—C)/NOx is optimized using the Non-dominated Sorting Genetic Algorithm II(NSGA-II)with 200 generations.The optimized mechanism(named as 937b)is validated against combustion characteristics of ammonia/methane(which is used to examine the accuracy of N—C interactions)and ammonia/diesel blends.The ignition delay times(IDTs),the laminar flame speeds and most of key intermediate species during the combustion of ammonia/methane blends can be accurately simulated by 937b under a wide range of conditions.As for ammonia/diesel blends with various diesel energy fractions,reasonable predictions on the IDTs under pressures from 1.0 MPa to5.0 MPa as well as the laminar flame speeds are also achieved by 937b.In particular,with regard to the IDT simulations of ammonia/diesel blends,937b makes progress in both aspects of overall accuracy and computational efficiency,compared to a detailed ammonia/diesel mechanism.Further kinetic analysis reveals that the reaction pathway of ammonia during the combustion of ammonia/diesel blend mainly differs in the tendencies of oxygen additions to NH_2 and NH with different equivalence ratios.展开更多
文摘This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.
基金supported by the National Key Research and Development Plan(Grant No.2021YFB3302501)the National Natural Science Foundation of China(Grant Nos.12102077,12161076,U2241263).
文摘Combat effectiveness of unmanned aerial vehicle(UAV)formations can be severely affected by the mission execution reliability.During the practical execution phase,there are inevitable risks where UAVs being destroyed or targets failed to be executed.To improve the mission reliability,a resilient mission planning framework integrates task pre-and re-assignment modules is developed in this paper.In the task pre-assignment phase,to guarantee the mission reliability,probability constraints regarding the minimum mission success rate are imposed to establish a multi-objective optimization model.And an improved genetic algorithm with the multi-population mechanism and specifically designed evolutionary operators is used for efficient solution.As in the task-reassignment phase,possible trigger events are first analyzed.A real-time contract net protocol-based algorithm is then proposed to address the corresponding emergency scenario.And the dual objective used in the former phase is adapted into a single objective to keep a consistent combat intention.Three cases of different scales demonstrate that the two modules cooperate well with each other.On the one hand,the pre-assignment module can generate high-reliability mission schedules as an elaborate mathematical model is introduced.On the other hand,the re-assignment module can efficiently respond to various emergencies and adjust the original schedule within a millisecond.The corresponding animation is accessible at bilibili.com/video/BV12t421w7EE for better illustration.
文摘In the last decade,space solar power satellites(SSPSs)have been conceived to support net-zero carbon emissions and have attracted considerable attention.Electric energy is transmitted to the ground via a microwave power beam,a technology known as microwave power transmission(MPT).Due to the vast transmission distance of tens of thousands of kilometers,the power transmitting antenna array must span up to 1 kilometer in diameter.At the same time,the size of the rectifying array on the ground should extend over a few kilometers.This makes the MPT system of SSPSs significantly larger than the existing aerospace engineering system.To design and operate a rational MPT system,comprehensive optimization is required.Taking the space MPT system engineering into consideration,a novel multi-objective optimization function is proposed and further analyzed.The multi-objective optimization problem is modeled mathematically.Beam collection efficiency(BCE)is the primary factor,followed by the thermal management capability.Some tapers,designed to solve the conflict between BCE and the thermal problem,are reviewed.In addition to these two factors,rectenna design complexity is included as a functional factor in the optimization objective.Weight coefficients are assigned to these factors to prioritize them.Radiating planar arrays with different aperture illumination fields are studied,and their performances are compared using the multi-objective optimization function.Transmitting array size,rectifying array size,transmission distance,and transmitted power remaine constant in various cases,ensuring fair comparisons.The analysis results show that the proposed optimization function is effective in optimizing and selecting the MPT system architecture.It is also noted that the multi-objective optimization function can be expanded to include other factors in the future.
基金supported by the National Natural Science Foundation of China(62073267,61903305)the Fundamental Research Funds for the Central Universities(HXGJXM202214).
文摘The lack of systematic and scientific top-level arrangement in the field of civil aircraft flight test leads to the problems of long duration and high cost.Based on the flight test activity,mathematical models of flight test duration and cost are established to set up the framework of flight test process.The top-level arrangement for flight test is optimized by multi-objective algorithm to reduce the duration and cost of flight test.In order to verify the necessity and validity of the mathematical models and the optimization algorithm of top-level arrangement,real flight test data is used to make an example calculation.Results show that the multi-objective optimization results of the top-level flight arrangement are better than the initial arrangement data,which can shorten the duration,reduce the cost,and improve the efficiency of flight test.
文摘Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and mission trajectory planning method is proposed to meet the requirements of cross-domain unmanned swarm mission planning.Firstly,the different performances of cross-domain heterogeneous platforms and mission requirements of targets are characterised by using a collection of operational resources.Secondly,an algorithmic framework for joint target assignment and mission trajectory planning is proposed,in which the initial planning of the trajectory is performed in the target assignment phase,while the trajectory is further optimised afterwards.Next,the estimation of the distribution algorithms is combined with the genetic algorithm to solve the objective function.Finally,the algorithm is numerically simulated by specific cases.Simulation results indicate that the proposed algorithm can perform effective task assignment and trajectory planning for cross-domain unmanned swarms.Furthermore,the solution performance of the hybrid estimation of distribution algorithm(EDA)-genetic algorithm(GA)algorithm is better than that of GA and EDA.
基金supported by the Ministry of Industry and Information Technology(No.23100002022102001)。
文摘Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threaten flight safety and mission success.Traditional path planning methods typically depend solely on the distribution of static obstacles to generate collision-free paths,without accounting for constraints imposed by enemy detection and strike capabilities.Such a simplified approach can yield safety-compromising routes in highly complex urban airspace.To address these limitations,this study proposes a multi-parameter path planning method based on reachable airspace visibility graphs,which integrates UAV performance constraints,environmental limitations,and exposure risks.An innovative heuristic algorithm is developed to balance operational safety and efficiency by both exposure risks and path length.In the case study set in a typical mixed-use urban area,analysis of airspace visibility graphs reveals significant variations in exposure risk at different regions and altitudes due to building encroachments.Path optimization results indicate that the method can effectively generate covert and efficient flight paths by dynamically adjusting the exposure index,which represents the likelihood of enemy detection,and the path length,which corresponds to mission execution time.
基金supported by the National Science Fund for Distinguished Young Scholars(52425211)BIT Research Fund Program for Young Scholars(XSQD-202201005).
文摘A three-dimensional path-planning approach has been developed to coordinate multiple fixed-wing unmanned aerial vehicles(UAVs)while avoiding collisions.The hierarchical path-planning architecture that divides the path-planning process into two layers is proposed by designing the velocityobstacle strategy for satisfying timeliness and effectiveness.The upper-level layer focuses on creating an efficient Dubins initial path considering the dynamic constraints of the fixed wing.Subsequently,the lower-level layer detects potential collisions and adjusts its flight paths to avoid collisions by using the threedimensional velocity obstacle method,which describes the maneuvering space of collision avoidance as the intersection space of half space.To further handle the dynamic and collisionavoidance constraints,a priority mechanism is designed to ensure that the adjusted path is still feasible for fixed-wing UAVs.Simulation experiments demonstrate the effectiveness of the proposed method.
基金supported by Hunan Provincial Natural Science Foundation(2024JJ5173,2023JJ50047)Hunan Provincial Department of Education Scientific Research Project(23A0494)Hunan Provincial Innovation Foundation for Postgraduate(CX20231221).
文摘To solve the problem of multi-platform collaborative use in anti-ship missile (ASM) path planning, this paper pro-posed multi-operator real-time constraints particle swarm opti-mization (MRC-PSO) algorithm. MRC-PSO algorithm utilizes a semi-rasterization environment modeling technique and inte-grates the geometric gradient law of ASMs which distinguishes itself from other collaborative path planning algorithms by fully considering the coupling between collaborative paths. Then, MRC-PSO algorithm conducts chunked stepwise recursive evo-lution of particles while incorporating circumvent, coordination, and smoothing operators which facilitates local selection opti-mization of paths, gradually reducing algorithmic space, accele-rating convergence, and enhances path cooperativity. Simula-tion experiments comparing the MRC-PSO algorithm with the PSO algorithm, genetic algorithm and operational area cluster real-time restriction (OACRR)-PSO algorithm, which demon-strate that the MRC-PSO algorithm has a faster convergence speed, and the average number of iterations is reduced by approximately 75%. It also proves that it is equally effective in resolving complex scenarios involving multiple obstacles. More-over it effectively addresses the problem of path crossing and can better satisfy the requirements of multi-platform collabora-tive path planning. The experiments are conducted in three col-laborative operation modes, namely, three-to-two, three-to-three, and four-to-two, and the outcomes demonstrate that the algorithm possesses strong universality.
基金supported by the National Natural Science Foundation of China(71901212)the Science and Technology Innovation Program of Hunan Province(2020RC4046).
文摘The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.
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 Fundamental Research Funds for the Central Universities(2024JBZX038)the National Natural Science Foundation of China(62076023).
文摘With the rapid development of low-altitude economy and unmanned aerial vehicles (UAVs) deployment technology, aerial-ground collaborative delivery (AGCD) is emerging as a novel mode of last-mile delivery, where the vehicle and its onboard UAVs are utilized efficiently. Vehicles not only provide delivery services to customers but also function as mobile ware-houses and launch/recovery platforms for UAVs. This paper addresses the vehicle routing problem with UAVs considering time window and UAV multi-delivery (VRPU-TW&MD). A mixed integer linear programming (MILP) model is developed to mini-mize delivery costs while incorporating constraints related to UAV energy consumption. Subsequently, a micro-evolution aug-mented large neighborhood search (MEALNS) algorithm incor-porating adaptive large neighborhood search (ALNS) and micro-evolution mechanism is proposed. Numerical experiments demonstrate the effectiveness of both the model and algorithm in solving the VRPU-TW&MD. The impact of key parameters on delivery performance is explored by sensitivity analysis.
基金National Natural Science Foundation of China(Grant No.52472417)to provide fund for conducting experiments.
文摘Complex multi-area collaborative coverage path planning in dynamic environments poses a significant challenge for multi-fixed-wing UAVs(multi-UAV).This study establishes a comprehensive framework that incorporates UAV capabilities,terrain,complex areas,and mission dynamics.A novel dynamic collaborative path planning algorithm is introduced,designed to ensure complete coverage of designated areas.This algorithm meticulously optimizes the operation,entry,and transition paths for each UAV,while also establishing evaluation metrics to refine coverage sequences for each area.Additionally,a three-dimensional path is computed utilizing an altitude descent method,effectively integrating twodimensional coverage paths with altitude constraints.The efficacy of the proposed approach is validated through digital simulations and mixed-reality semi-physical experiments across a variety of dynamic scenarios,including both single-area and multi-area coverage by multi-UAV.Results show that the coverage paths generated by this method significantly reduce both computation time and path length,providing a reliable solution for dynamic multi-UAV mission planning in semi-physical environments.
基金Project(2009AA11Z220)supported by the National High Technology Research and Development Program of China
文摘Unmanned aerial vehicle(UAV)was introduced as a novel traffic device to collect road traffic information and its cruise route planning problem was considered.Firstly,a multi-objective optimization model was proposed aiming at minimizing the total cruise distance and the number of UAVs used,which used UAV maximum cruise distance,the number of UAVs available and time window of each monitored target as constraints.Then,a novel multi-objective evolutionary algorithm was proposed.Next,a case study with three time window scenarios was implemented.The results show that both the total cruise distance and the number of UAVs used continue to increase with the time window constraint becoming narrower.Compared with the initial optimal solutions,the optimal total cruise distance and the number of UAVs used fall by an average of 30.93% and 31.74%,respectively.Finally,some concerns using UAV to collect road traffic information were discussed.
基金supported by the National Natural Science Foundation of China(7150118061473301)
文摘In the past few decades, applications of geostationary orbit (GEO) satellites have attracted increasing attention, and with the development of optical technologies, GEO optical satellites have become popular worldwide. This paper proposes a general working pattern for a GEO optical satellite, as well as a target observation mission planning model. After analyzing the requirements of users and satellite control agencies, two objectives are simultaneously considered: maximization of total profit and minimization of satellite attitude maneuver angle. An NSGA-II based multi-objective optimization algorithm is proposed, which contains some heuristic principles in the initialization phase and mutation operator, and is embedded with a traveling salesman problem (TSP) optimization. The validity and performance of the proposed method are verified by extensive numerical simulations that include several types of point target distributions.
基金Fundamental Research Funds for the Central Universities(2024JBZX038)National Natural Science F oundation of China(62076023)。
文摘The rapid evolution of unmanned aerial vehicle(UAV)technology and autonomous capabilities has positioned UAV as promising last-mile delivery means.Vehicle and onboard UAV collaborative delivery is introduced as a novel delivery mode.Spatiotemporal collaboration,along with energy consumption with payload and wind conditions play important roles in delivery route planning.This paper introduces the traveling salesman problem with time window and onboard UAV(TSPTWOUAV)and emphasizes the consideration of real-world scenarios,focusing on time collaboration and energy consumption with wind and payload.To address this,a mixed integer linear programming(MILP)model is formulated to minimize the energy consumption costs of vehicle and UAV.Furthermore,an adaptive large neighborhood search(ALNS)algorithm is applied to identify high-quality solutions efficiently.The effectiveness of the proposed model and algorithm is validated through numerical tests on real geographic instances and sensitivity analysis of key parameters is conducted.
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Project(61273138)supported by the National Natural Science Foundation of ChinaProject(14JCZDJC39300)supported by the Key Fund of Tianjin,China
文摘Homing trajectory planning is a core task of autonomous homing of parafoil system.This work analyzes and establishes a simplified kinematic mathematical model,and regards the homing trajectory planning problem as a kind of multi-objective optimization problem.Being different from traditional ways of transforming the multi-objective optimization into a single objective optimization by weighting factors,this work applies an improved non-dominated sorting genetic algorithm Ⅱ(NSGA Ⅱ) to solve it directly by means of optimizing multi-objective functions simultaneously.In the improved NSGA Ⅱ,the chaos initialization and a crowding distance based population trimming method were introduced to overcome the prematurity of population,the penalty function was used in handling constraints,and the optimal solution was selected according to the method of fuzzy set theory.Simulation results of three different schemes designed according to various practical engineering requirements show that the improved NSGA Ⅱ can effectively obtain the Pareto optimal solution set under different weighting with outstanding convergence and stability,and provide a new train of thoughts to design homing trajectory of parafoil system.
文摘Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a ground threat prediction-based path planning method is proposed based on artificial bee colony(ABC)algorithm by collaborative thinking strategy.Firstly,a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats.The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time.Then,a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats.By adding the collision warning mechanism to the path planning model,the flight path could be dynamically adjusted according to changing no-fly zones.Furthermore,a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy.The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution,and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy,which makes the optimization performance of ABC algorithm more controllable and efficient.Finally,simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.
基金the National Natural Science Foundation of China(project code:52202470)Jilin Province Natural Science Foundation(project codes:20220101205JC,20220101212JC)+2 种基金Jilin Province Specific Project of Industrial Technology Research&Development(project code:2020C025-2)2021 Interdisciplinary Integration and Innovation Project of Jilin University(project code:XJRCYB07)Free Exploration Project of Changsha Automotive Innovation Research Institute of Jilin University(project code:CAIRIZT20220202)。
文摘For the deep understanding on combustion of ammonia/diesel,this study develops a reduced mechanism of ammonia/diesel with 227 species and 937 reactions.The sub-mechanism on ammonia/interactions of N-based and C-based species(N—C)/NOx is optimized using the Non-dominated Sorting Genetic Algorithm II(NSGA-II)with 200 generations.The optimized mechanism(named as 937b)is validated against combustion characteristics of ammonia/methane(which is used to examine the accuracy of N—C interactions)and ammonia/diesel blends.The ignition delay times(IDTs),the laminar flame speeds and most of key intermediate species during the combustion of ammonia/methane blends can be accurately simulated by 937b under a wide range of conditions.As for ammonia/diesel blends with various diesel energy fractions,reasonable predictions on the IDTs under pressures from 1.0 MPa to5.0 MPa as well as the laminar flame speeds are also achieved by 937b.In particular,with regard to the IDT simulations of ammonia/diesel blends,937b makes progress in both aspects of overall accuracy and computational efficiency,compared to a detailed ammonia/diesel mechanism.Further kinetic analysis reveals that the reaction pathway of ammonia during the combustion of ammonia/diesel blend mainly differs in the tendencies of oxygen additions to NH_2 and NH with different equivalence ratios.
