Collaborative coverage path planning(CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle(UAV) collaborative CCPP algorithm ...Collaborative coverage path planning(CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle(UAV) collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment.Due to flexible control of small UAVs, it can be considered that all UAVs fly at the same altitude, that is, they perform search tasks on a two-dimensional plane. Based on the agents’ motion characteristics and environmental information, a mathematical model of CCPP problem is established. The minimum time for UAVs to complete the CCPP is the objective function, and complete coverage constraint, no-fly constraint, collision avoidance constraint, and communication constraint are considered. Four motion strategies and two communication strategies are designed. Then a distributed CCPP algorithm is designed based on hybrid strategies. Simulation results compared with patternbased genetic algorithm(PBGA) and random search method show that the proposed method has stronger real-time performance and better scalability and can complete the complete CCPP task more efficiently and stably.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
We propose a contraction transformation algorithm to plan a complete coverage trajectory for a mobile robot to ac-complish specific types of missions based on the Arnold dynamical system. First, we construct a chaotic...We propose a contraction transformation algorithm to plan a complete coverage trajectory for a mobile robot to ac-complish specific types of missions based on the Arnold dynamical system. First, we construct a chaotic mobile robot by com-bining the variable z of the Arnold equation and the kinematic equation of the robot. Second, we construct the candidate sets including the initial points with a relatively high coverage rate of the constructed mobile robot. Then the trajectory is contracted to the current position of the robot based on the designed contraction transformation strategy, to form a continuous complete cov-erage trajectory to execute the specific types of missions. Compared with the traditional method, the designed algorithm requires no obstacle avoidance to the boundary of the given workplace, possesses a high coverage rate, and keeps the chaotic characteristics of the produced coverage trajectory relatively unchanged, which enables the robot to accomplish special missions with features of completeness, randomness, or unpredictability.展开更多
We propose a novel parameter value selection strategy for the Lüsystem to construct a chaotic robot to accomplish the complete coverage path planning(CCPP)task.The algorithm can meet the requirements of high rand...We propose a novel parameter value selection strategy for the Lüsystem to construct a chaotic robot to accomplish the complete coverage path planning(CCPP)task.The algorithm can meet the requirements of high randomness and coverage rate to perform specific types of missions.First,we roughly determine the value range of the parameter of the Lüsystem to meet the requirement of being a dissipative system.Second,we calculate the Lyapunov exponents to narrow the value range further.Next,we draw the phase planes of the system to approximately judge the topological distribution characteristics of its trajectories.Furthermore,we calculate the Pearson correlation coefficient of the variable for those good ones to judge its random characteristics.Finally,we construct a chaotic robot using variables with the determined parameter values and simulate and test the coverage rate to study the relationship between the coverage rate and the random characteristics of the variables.The above selection strategy gradually narrows the value range of the system parameter according to the randomness requirement of the coverage trajectory.Using the proposed strategy,proper variables can be chosen with a larger Lyapunov exponent to construct a chaotic robot with a higher coverage rate.Another chaotic system,the Lorenz system,is used to verify the feasibility and effectiveness of the designed strategy.The proposed strategy for enhancing the coverage rate of the mobile robot can improve the efficiency of accomplishing CCPP tasks under specific types of missions.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
基金supported by the National Natural Science Foundation of China (61903036, 61822304)Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100)。
文摘Collaborative coverage path planning(CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle(UAV) collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment.Due to flexible control of small UAVs, it can be considered that all UAVs fly at the same altitude, that is, they perform search tasks on a two-dimensional plane. Based on the agents’ motion characteristics and environmental information, a mathematical model of CCPP problem is established. The minimum time for UAVs to complete the CCPP is the objective function, and complete coverage constraint, no-fly constraint, collision avoidance constraint, and communication constraint are considered. Four motion strategies and two communication strategies are designed. Then a distributed CCPP algorithm is designed based on hybrid strategies. Simulation results compared with patternbased genetic algorithm(PBGA) and random search method show that the proposed method has stronger real-time performance and better scalability and can complete the complete CCPP task more efficiently and stably.
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 supported by the National Natural Science Foundation of China(Nos.61473179,61602280,and 61573213)the Natural Science Foundation of Shandong Province,China(Nos.ZR2017MF047,ZR2015CM016,and ZR2014FM007)the Shandong University of Technology&Zibo City Integration Development Project,China(No.2018ZBXC295)。
文摘We propose a contraction transformation algorithm to plan a complete coverage trajectory for a mobile robot to ac-complish specific types of missions based on the Arnold dynamical system. First, we construct a chaotic mobile robot by com-bining the variable z of the Arnold equation and the kinematic equation of the robot. Second, we construct the candidate sets including the initial points with a relatively high coverage rate of the constructed mobile robot. Then the trajectory is contracted to the current position of the robot based on the designed contraction transformation strategy, to form a continuous complete cov-erage trajectory to execute the specific types of missions. Compared with the traditional method, the designed algorithm requires no obstacle avoidance to the boundary of the given workplace, possesses a high coverage rate, and keeps the chaotic characteristics of the produced coverage trajectory relatively unchanged, which enables the robot to accomplish special missions with features of completeness, randomness, or unpredictability.
基金Project supported by the National Natural Science Foundation of China(Nos.61973184 and 61473179)the Natural Science Foundation of Shandong Province,China(No.ZR2021MF072)。
文摘We propose a novel parameter value selection strategy for the Lüsystem to construct a chaotic robot to accomplish the complete coverage path planning(CCPP)task.The algorithm can meet the requirements of high randomness and coverage rate to perform specific types of missions.First,we roughly determine the value range of the parameter of the Lüsystem to meet the requirement of being a dissipative system.Second,we calculate the Lyapunov exponents to narrow the value range further.Next,we draw the phase planes of the system to approximately judge the topological distribution characteristics of its trajectories.Furthermore,we calculate the Pearson correlation coefficient of the variable for those good ones to judge its random characteristics.Finally,we construct a chaotic robot using variables with the determined parameter values and simulate and test the coverage rate to study the relationship between the coverage rate and the random characteristics of the variables.The above selection strategy gradually narrows the value range of the system parameter according to the randomness requirement of the coverage trajectory.Using the proposed strategy,proper variables can be chosen with a larger Lyapunov exponent to construct a chaotic robot with a higher coverage rate.Another chaotic system,the Lorenz system,is used to verify the feasibility and effectiveness of the designed strategy.The proposed strategy for enhancing the coverage rate of the mobile robot can improve the efficiency of accomplishing CCPP tasks under specific types of missions.
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.
