In dynamic and uncertain reconnaissance missions,effective task assignment and path planning for multiple unmanned aerial vehicles(UAVs)present significant challenges.A stochastic multi-UAV reconnaissance scheduling p...In dynamic and uncertain reconnaissance missions,effective task assignment and path planning for multiple unmanned aerial vehicles(UAVs)present significant challenges.A stochastic multi-UAV reconnaissance scheduling problem is formulated as a combinatorial optimization task with nonlinear objectives and coupled constraints.To solve the non-deterministic polynomial(NP)-hard problem efficiently,a novel learning-enhanced pigeon-inspired optimization(L-PIO)algorithm is proposed.The algorithm integrates a Q-learning mechanism to dynamically regulate control parameters,enabling adaptive exploration–exploitation trade-offs across different optimization phases.Additionally,geometric abstraction techniques are employed to approximate complex reconnaissance regions using maximum inscribed rectangles and spiral path models,allowing for precise cost modeling of UAV paths.The formal objective function is developed to minimize global flight distance and completion time while maximizing reconnaissance priority and task coverage.A series of simulation experiments are conducted under three scenarios:static task allocation,dynamic task emergence,and UAV failure recovery.Comparative analysis with several updated algorithms demonstrates that L-PIO exhibits superior robustness,adaptability,and computational efficiency.The results verify the algorithm's effectiveness in addressing dynamic reconnaissance task planning in real-time multi-UAV applications.展开更多
Based on the modeling of robot working environment, the shortest distance matrix between points is solved by Floyd algorithm. With the objective of minimizing the sum of the fixed cost of robot and the cost of robot o...Based on the modeling of robot working environment, the shortest distance matrix between points is solved by Floyd algorithm. With the objective of minimizing the sum of the fixed cost of robot and the cost of robot operation, an integer programming model is established and a genetic algorithm for solving the model is designed. In order to make coordination to accomplish their respective tasks for each robot with high efficiency, this paper uses natural number encoding way. The objective function is based on penalty term constructed with the total number of collisions in the running path of robots. The fitness function is constructed by using the objective function with penalty term. Based on elitist retention strategy, a genetic algorithm with collision detection is designed. Using this algorithm for task allocation and path planning of multi-robot, it can effectively avoid or reduce the number of collisions in the process of multi-robot performing tasks. Finally, an example is used to validate the method.展开更多
This paper presents a singularity robust path planning for space manipulator to achieve base （satellite） attitude adjustment and end-effector task. The base attitude adjustment by the movement of manipulator will sa...This paper presents a singularity robust path planning for space manipulator to achieve base （satellite） attitude adjustment and end-effector task. The base attitude adjustment by the movement of manipulator will save propellant compared with conventional attitude control system. A task-priority reaction null-space control method is applied to achieve the primary task of adjusting attitude and secondary task of accomplishing end-effector task. Furthermore, the algorithm singularity is eliminated in the proposed algorithm compared with conventional reaction null-space algorithm. And the singular value filtering decomposition is introduced to dispose the dynamic singularity, the unit quaternion is also introduced to overcome representation singularity. Hence, a singularity robust path planning algorithm of space robot for base attitude adjustment is derived. A real time simulation system of the space robot under Linux/RTAl （realtime application interface） is developed to verify and test the feasibility and reliability of the method. The experimental results demonstrate the feasibility of online base attitude adjustment of space robot by the proposed algorithm.展开更多
With the development of operationally responsive space（ORS） and on-board processing techniques, the end users canreceive the observation data from the ORS satellite directly. Tosatisfy the demand for reducing the re...With the development of operationally responsive space（ORS） and on-board processing techniques, the end users canreceive the observation data from the ORS satellite directly. Tosatisfy the demand for reducing the requirements-tasking-effectscycle from one day to hours, the various resources of the wholedata acquisition chain （including satellites, ground stations, dataprocessing centers, users, etc.） should be taken into an overallconsideration, and the traditional batch task planning mode shouldbe transformed into the user-oriented task planning mode. Consideringthere are many approaches for data acquisition due tothe new techniques of ORS satellite, the data acquisition chaintask planning problem for ORS satellite can be seen as the multimodalroute planning problem. Thereby, a framework is presentedusing label-constrained shortest path technique with the conflictresolution. To apply this framework to solve the ORS satellite taskplanning problem, the preprocessing and the conflict resolutionstrategies are discussed in detail. Based on the above work, theuser-oriented data acquisition chain task planning algorithm forORS satellite is proposed. The exact solution can be obtainedin polynomial time using the proposed algorithm. The simulationexperiments validate the feasibility and the adaptability of the proposedapproach.展开更多
A new method for a cooperative multi-task allocation problem(CMTAP) is proposed in this paper,taking into account the multi-ship, multi-target, multi-task and multi-constraint characteristics in a multi-ship cooperati...A new method for a cooperative multi-task allocation problem(CMTAP) is proposed in this paper,taking into account the multi-ship, multi-target, multi-task and multi-constraint characteristics in a multi-ship cooperative driving(MCD) system. On the basis of the general CMTAP model, an MCD task assignment model is established. Furthermore, a genetic ant colony hybrid algorithm(GACHA) is proposed for this model using constraints, including timing constraints, multi-ship collaboration constraints and ship capacity constraints. This algorithm uses a genetic algorithm(GA) based on a task sequence, while the crossover and mutation operators are based on similar tasks. In order to reduce the dependence of the GA on the initial population, an ant colony algorithm(ACA) is used to produce the initial population. In order to meet the environmental constraints of ship navigation, the results of the task allocation and path planning are combined to generate an MCD task planning scheme. The results of a simulated experiment using simulated data show that the proposed method can make the assignment more optimized on the basis of satisfying the task assignment constraints and the ship navigation environment constraints. Moreover, the experimental results using real data also indicate that the proposed method can find the optimal solution rapidly, and thus improve the task allocation efficiency.展开更多
In recent years,sensor technology has been widely used in the defense and control of sensitive areas in cities,or in various scenarios such as early warning of forest fires,monitoring of forest pests and diseases,and ...In recent years,sensor technology has been widely used in the defense and control of sensitive areas in cities,or in various scenarios such as early warning of forest fires,monitoring of forest pests and diseases,and protection of endangered animals.Deploying sensors to collect data and then utilizing unmanned aerial vehicle(UAV)to collect the data stored in the sensors has replaced traditional manual data collection as the dominant method.The current strategies for efficient data collection in above scenarios are still imperfect,and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data collection.With regards this,this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering,multi-UAV mission area allocation,and sub-area data acquisition scheme optimization.The improvedα-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors,and the K-Dimensional method is used to form a multi-UAV cooperative workspace,and then,the genetic algorithm is used to trade-off the speed with the age of information(AoI)of the collected information and the energy consumption to form the multi-UAV data collection operation scheme.The combined optimization scheme in paper improves the performance by 95.56%and 58.21%,respectively,compared to the traditional baseline model.In order to verify the excellent generalization and applicability of the proposed method in real scenarios,the simulation test is conducted by introducing the digital elevation model data of the real terrain,and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of±10%.Then,the advantages and disadvantages of the present method with the existing mainstream schemes are tested,and the results show that the present method has a huge advantage in terms of space and time complexity,and at the same time,the accuracy for data extraction is relatively improved by 10.46%and 12.71%.Finally,by eliminating the clustering process and the subtask assignment process,the AoI performance decreases by 3.46×and 4.45×,and the energy performance decreases by 3.52×and 4.47×.This paper presents a comprehensive and detailed proactive optimization of the existing challenges faced in the field of data acquisition by means of a series of combinatorial optimizations.展开更多
Path planning and task allocation are the key technologies of multi-machine collaboration.Current approaches focus on field operations,but actually orchard operations are also a promising area.In order to improve the ...Path planning and task allocation are the key technologies of multi-machine collaboration.Current approaches focus on field operations,but actually orchard operations are also a promising area.In order to improve the efficiency of orchard mowing,a cooperative operation scheduling method was proposed for multiple mowing robots in the dwarf dense planting orchards.It aims to optimize the non-working time of the robot in the intra-plot paths and inter-plot routes.Firstly,a genetic algorithm with multi-mutation and improved circle algorithm(MC-GA)was proposed for path planning.Subsequently,an ant colony optimization algorithm with mixed operator(Mix-ACO)was proposed for task allocation.With regard to the shortage of robots,a local search algorithm was designed to reassign work routes.Simulation experiment results show that MC-GA can significantly reduce the total turning time and the number of reverses for the robot.Mix-ACO can effectively allocate tasks by generating multiple work routes and reduce the total transfer time for the robot fleet.When the number of work routes exceeds the number of mowing robots,the local search algorithm can reasonably reallocate multiple routes to robots,reducing the difference in task completion time of the robot fleet.Field experiment results indicate that compared with the reciprocating method,SADG,and GA,MC-GA can reduce fuel consumption rate by 1.55%-8.69%and operation time by 84-776 s.Compared with ACO,Mix-ACO can reduce the total transfer time by 130 s.The research results provide a more reasonable scheduling method for the cooperative operation of multiple mowing robots.展开更多
针对现有多无人机任务分配模型以线性距离为度量,忽略地形、威胁源等环境约束,以及传统秃鹰优化算法存在种群多样性不足、易陷局部最优等缺陷,提出一种基于秃鹰⁃天鹰混合优化(Hybrid Bald Eagle⁃Aquila Optimization,HBAO)的多无人机任...针对现有多无人机任务分配模型以线性距离为度量,忽略地形、威胁源等环境约束,以及传统秃鹰优化算法存在种群多样性不足、易陷局部最优等缺陷,提出一种基于秃鹰⁃天鹰混合优化(Hybrid Bald Eagle⁃Aquila Optimization,HBAO)的多无人机任务分配方法.首先,构建融合三维地形、威胁源及无人机物理约束的多旅行商任务分配模型,通过代价函数实现任务分配与航迹规划的紧耦合;然后,设计任务分配编码,改进优化策略,将天鹰优化算法的扩展⁃缩小搜索策略融入秃鹰算法的全局搜索阶段以提升探索效率,并引入折射反向学习机制增强种群多样性,有效平衡算法开发与探索能力;最后,设计双模型实验来验证算法效能.结果表明,提出的HBAO算法在复杂战场环境下求解精度和收敛速度较高,其综合性能优于五种对比算法,并且全局代价显著降低,能生成低能耗、高适应性的任务分配方案.展开更多
针对电力巡检场景的多无人机任务分配与路径规划问题,提出了一种最大航程约束的多无人机任务分配与路径规划算法。首先基于市场拍卖的方法引入了跳点搜索算法(jump point search,JPS)算法预估无人机到巡检任务以及巡检任务到巡检任务之...针对电力巡检场景的多无人机任务分配与路径规划问题,提出了一种最大航程约束的多无人机任务分配与路径规划算法。首先基于市场拍卖的方法引入了跳点搜索算法(jump point search,JPS)算法预估无人机到巡检任务以及巡检任务到巡检任务之间的最短避障路径长度,有效解决了任务分配和路径规划的耦合问题;其次,在优化目标函数中融合了无人机的最大航程约束,确保无人机可以在其最大续航能力内完成所分配到的巡检任务;最后,提出了一种懒惰的拍卖策略,在保证任务分配求解质量不变的情况下进一步提高了算法求解的收敛速度。实验结果表明,提出的方法相比于现有方法更符合续航能力有限的旋翼无人机作业需求,同时求解任务分配的时间最大减少了近40%。实验结果证明了所提方法的实用性和有效性。展开更多
The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multi...The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning is proposed in this paper.The improved multimodal multi-objective evolutionary algorithm is used to solve multi-robot task allo-cation problems.Moreover,a deep reinforcement learning strategy is used in the last generation to provide a high-quality path for each assigned robot via an end-to-end manner.Comparisons with three popular multimodal multi-objective evolutionary algorithms on three different scenarios of multi-robot task allocation problems are carried out to verify the performance of the proposed algorithm.The experimental test results show that the proposed algorithm can generate sufficient equivalent schemes to improve the availability and robustness of multi-robot collaborative systems in uncertain environments,and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems.展开更多
基金supported by the National Natural Science Foundation of China(Nos.T2121003,U24B20156)Open Fund of the National Key Laboratory of Helicopter Aeromechanics(No.2024-ZSJ-LB-02-06)。
文摘In dynamic and uncertain reconnaissance missions,effective task assignment and path planning for multiple unmanned aerial vehicles(UAVs)present significant challenges.A stochastic multi-UAV reconnaissance scheduling problem is formulated as a combinatorial optimization task with nonlinear objectives and coupled constraints.To solve the non-deterministic polynomial(NP)-hard problem efficiently,a novel learning-enhanced pigeon-inspired optimization(L-PIO)algorithm is proposed.The algorithm integrates a Q-learning mechanism to dynamically regulate control parameters,enabling adaptive exploration–exploitation trade-offs across different optimization phases.Additionally,geometric abstraction techniques are employed to approximate complex reconnaissance regions using maximum inscribed rectangles and spiral path models,allowing for precise cost modeling of UAV paths.The formal objective function is developed to minimize global flight distance and completion time while maximizing reconnaissance priority and task coverage.A series of simulation experiments are conducted under three scenarios:static task allocation,dynamic task emergence,and UAV failure recovery.Comparative analysis with several updated algorithms demonstrates that L-PIO exhibits superior robustness,adaptability,and computational efficiency.The results verify the algorithm's effectiveness in addressing dynamic reconnaissance task planning in real-time multi-UAV applications.
文摘Based on the modeling of robot working environment, the shortest distance matrix between points is solved by Floyd algorithm. With the objective of minimizing the sum of the fixed cost of robot and the cost of robot operation, an integer programming model is established and a genetic algorithm for solving the model is designed. In order to make coordination to accomplish their respective tasks for each robot with high efficiency, this paper uses natural number encoding way. The objective function is based on penalty term constructed with the total number of collisions in the running path of robots. The fitness function is constructed by using the objective function with penalty term. Based on elitist retention strategy, a genetic algorithm with collision detection is designed. Using this algorithm for task allocation and path planning of multi-robot, it can effectively avoid or reduce the number of collisions in the process of multi-robot performing tasks. Finally, an example is used to validate the method.
基金supported by National Program on Key Basic Research Project(973 Program,No.2013CB733103)the Program for New Century Excellent Talents in University(No.NCET-10-0058)
文摘This paper presents a singularity robust path planning for space manipulator to achieve base （satellite） attitude adjustment and end-effector task. The base attitude adjustment by the movement of manipulator will save propellant compared with conventional attitude control system. A task-priority reaction null-space control method is applied to achieve the primary task of adjusting attitude and secondary task of accomplishing end-effector task. Furthermore, the algorithm singularity is eliminated in the proposed algorithm compared with conventional reaction null-space algorithm. And the singular value filtering decomposition is introduced to dispose the dynamic singularity, the unit quaternion is also introduced to overcome representation singularity. Hence, a singularity robust path planning algorithm of space robot for base attitude adjustment is derived. A real time simulation system of the space robot under Linux/RTAl （realtime application interface） is developed to verify and test the feasibility and reliability of the method. The experimental results demonstrate the feasibility of online base attitude adjustment of space robot by the proposed algorithm.
基金supported by the National Natural Science Foundation of China(6110118461174159)
文摘With the development of operationally responsive space（ORS） and on-board processing techniques, the end users canreceive the observation data from the ORS satellite directly. Tosatisfy the demand for reducing the requirements-tasking-effectscycle from one day to hours, the various resources of the wholedata acquisition chain （including satellites, ground stations, dataprocessing centers, users, etc.） should be taken into an overallconsideration, and the traditional batch task planning mode shouldbe transformed into the user-oriented task planning mode. Consideringthere are many approaches for data acquisition due tothe new techniques of ORS satellite, the data acquisition chaintask planning problem for ORS satellite can be seen as the multimodalroute planning problem. Thereby, a framework is presentedusing label-constrained shortest path technique with the conflictresolution. To apply this framework to solve the ORS satellite taskplanning problem, the preprocessing and the conflict resolutionstrategies are discussed in detail. Based on the above work, theuser-oriented data acquisition chain task planning algorithm forORS satellite is proposed. The exact solution can be obtainedin polynomial time using the proposed algorithm. The simulationexperiments validate the feasibility and the adaptability of the proposedapproach.
基金the National Science and Technology Support Program(No.2015BAG20B05)
文摘A new method for a cooperative multi-task allocation problem(CMTAP) is proposed in this paper,taking into account the multi-ship, multi-target, multi-task and multi-constraint characteristics in a multi-ship cooperative driving(MCD) system. On the basis of the general CMTAP model, an MCD task assignment model is established. Furthermore, a genetic ant colony hybrid algorithm(GACHA) is proposed for this model using constraints, including timing constraints, multi-ship collaboration constraints and ship capacity constraints. This algorithm uses a genetic algorithm(GA) based on a task sequence, while the crossover and mutation operators are based on similar tasks. In order to reduce the dependence of the GA on the initial population, an ant colony algorithm(ACA) is used to produce the initial population. In order to meet the environmental constraints of ship navigation, the results of the task allocation and path planning are combined to generate an MCD task planning scheme. The results of a simulated experiment using simulated data show that the proposed method can make the assignment more optimized on the basis of satisfying the task assignment constraints and the ship navigation environment constraints. Moreover, the experimental results using real data also indicate that the proposed method can find the optimal solution rapidly, and thus improve the task allocation efficiency.
基金National Key R&D Program of China(2022YFF1302700)Xiong’an New Area Science and Technology Innovation Special Project of Ministry of Science and Technology of China(2023XAGG0065)+2 种基金Ant Group through CCF-Ant Research Fund(CCF-AFSG RF20220214)Outstanding Youth Team Project of Central Universities(QNTD202308)Beijing Forestry University National Training Program of Innovation and Entrepreneurship for Undergraduates(202310022097).
文摘In recent years,sensor technology has been widely used in the defense and control of sensitive areas in cities,or in various scenarios such as early warning of forest fires,monitoring of forest pests and diseases,and protection of endangered animals.Deploying sensors to collect data and then utilizing unmanned aerial vehicle(UAV)to collect the data stored in the sensors has replaced traditional manual data collection as the dominant method.The current strategies for efficient data collection in above scenarios are still imperfect,and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data collection.With regards this,this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering,multi-UAV mission area allocation,and sub-area data acquisition scheme optimization.The improvedα-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors,and the K-Dimensional method is used to form a multi-UAV cooperative workspace,and then,the genetic algorithm is used to trade-off the speed with the age of information(AoI)of the collected information and the energy consumption to form the multi-UAV data collection operation scheme.The combined optimization scheme in paper improves the performance by 95.56%and 58.21%,respectively,compared to the traditional baseline model.In order to verify the excellent generalization and applicability of the proposed method in real scenarios,the simulation test is conducted by introducing the digital elevation model data of the real terrain,and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of±10%.Then,the advantages and disadvantages of the present method with the existing mainstream schemes are tested,and the results show that the present method has a huge advantage in terms of space and time complexity,and at the same time,the accuracy for data extraction is relatively improved by 10.46%and 12.71%.Finally,by eliminating the clustering process and the subtask assignment process,the AoI performance decreases by 3.46×and 4.45×,and the energy performance decreases by 3.52×and 4.47×.This paper presents a comprehensive and detailed proactive optimization of the existing challenges faced in the field of data acquisition by means of a series of combinatorial optimizations.
基金funded by the earmarked fund for CARS(CARS-27)supported by the Earmarked Fund for the Hebei Apple Innovation Team of the Modern Agro-industry Technology Research System(Grant No.HBCT2024150202).
文摘Path planning and task allocation are the key technologies of multi-machine collaboration.Current approaches focus on field operations,but actually orchard operations are also a promising area.In order to improve the efficiency of orchard mowing,a cooperative operation scheduling method was proposed for multiple mowing robots in the dwarf dense planting orchards.It aims to optimize the non-working time of the robot in the intra-plot paths and inter-plot routes.Firstly,a genetic algorithm with multi-mutation and improved circle algorithm(MC-GA)was proposed for path planning.Subsequently,an ant colony optimization algorithm with mixed operator(Mix-ACO)was proposed for task allocation.With regard to the shortage of robots,a local search algorithm was designed to reassign work routes.Simulation experiment results show that MC-GA can significantly reduce the total turning time and the number of reverses for the robot.Mix-ACO can effectively allocate tasks by generating multiple work routes and reduce the total transfer time for the robot fleet.When the number of work routes exceeds the number of mowing robots,the local search algorithm can reasonably reallocate multiple routes to robots,reducing the difference in task completion time of the robot fleet.Field experiment results indicate that compared with the reciprocating method,SADG,and GA,MC-GA can reduce fuel consumption rate by 1.55%-8.69%and operation time by 84-776 s.Compared with ACO,Mix-ACO can reduce the total transfer time by 130 s.The research results provide a more reasonable scheduling method for the cooperative operation of multiple mowing robots.
文摘针对现有多无人机任务分配模型以线性距离为度量,忽略地形、威胁源等环境约束,以及传统秃鹰优化算法存在种群多样性不足、易陷局部最优等缺陷,提出一种基于秃鹰⁃天鹰混合优化(Hybrid Bald Eagle⁃Aquila Optimization,HBAO)的多无人机任务分配方法.首先,构建融合三维地形、威胁源及无人机物理约束的多旅行商任务分配模型,通过代价函数实现任务分配与航迹规划的紧耦合;然后,设计任务分配编码,改进优化策略,将天鹰优化算法的扩展⁃缩小搜索策略融入秃鹰算法的全局搜索阶段以提升探索效率,并引入折射反向学习机制增强种群多样性,有效平衡算法开发与探索能力;最后,设计双模型实验来验证算法效能.结果表明,提出的HBAO算法在复杂战场环境下求解精度和收敛速度较高,其综合性能优于五种对比算法,并且全局代价显著降低,能生成低能耗、高适应性的任务分配方案.
文摘针对电力巡检场景的多无人机任务分配与路径规划问题,提出了一种最大航程约束的多无人机任务分配与路径规划算法。首先基于市场拍卖的方法引入了跳点搜索算法(jump point search,JPS)算法预估无人机到巡检任务以及巡检任务到巡检任务之间的最短避障路径长度,有效解决了任务分配和路径规划的耦合问题;其次,在优化目标函数中融合了无人机的最大航程约束,确保无人机可以在其最大续航能力内完成所分配到的巡检任务;最后,提出了一种懒惰的拍卖策略,在保证任务分配求解质量不变的情况下进一步提高了算法求解的收敛速度。实验结果表明,提出的方法相比于现有方法更符合续航能力有限的旋翼无人机作业需求,同时求解任务分配的时间最大减少了近40%。实验结果证明了所提方法的实用性和有效性。
基金the Shanghai Pujiang Program (No.22PJD030),the National Natural Science Foundation of China (Nos.61603244 and 71904116)the National Natural Science Foundation of China-Shandong Joint Fund (No.U2006228)。
文摘The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning is proposed in this paper.The improved multimodal multi-objective evolutionary algorithm is used to solve multi-robot task allo-cation problems.Moreover,a deep reinforcement learning strategy is used in the last generation to provide a high-quality path for each assigned robot via an end-to-end manner.Comparisons with three popular multimodal multi-objective evolutionary algorithms on three different scenarios of multi-robot task allocation problems are carried out to verify the performance of the proposed algorithm.The experimental test results show that the proposed algorithm can generate sufficient equivalent schemes to improve the availability and robustness of multi-robot collaborative systems in uncertain environments,and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems.
