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
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
A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented.It makes full use of information sharing and retains diversity from the...A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented.It makes full use of information sharing and retains diversity from the principle of particle swarm optimization (PSO) and artificial immune system (AIS).The algorithm was analyzed in detail and proper swarm size,evolving generations,gene-exchange individual order,and gene-exchange proportion in molecule were obtained for better algorithm performances.According to the test results,the appropriate parameters are about 50 swarm individuals,over 3 000 evolving generations,20%-25% gene-exchange proportion in molecule with gene-exchange taking place between better fitness affinity individuals.The algorithm is practical and effective in maximizing the coverage probability with given number of sensors and minimizing sensor numbers with required coverage probability in sensor placement.It can reach a better result quickly,especially with the proper calculation parameters.展开更多
This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions.This method analyzes and proves the properties of different deployment ...This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions.This method analyzes and proves the properties of different deployment patterns in the optimal deployment sequence.Based on these properties and considering location restrictions,it introduces an optimization model of arc barrier coverage and aims to minimize the total deployment cost of heterogeneous multistatic radars.To overcome the non-convexity of the model and the non-analytical nature of the objective function,an algorithm combining integer line programming and the cuckoo search algorithm(CSA)is proposed.The proposed algorithm can determine the number of receivers and transmitters in each optimal deployment squence to minimize the total placement cost.Simulations are conducted in different conditions to verify the effectiveness of the proposed method.展开更多
为满足配用电场景差异化业务需求,综合考虑网络的覆盖率和连通性,构建无线与电力线通信(power line communication,PLC)融合通信覆盖模型,提出基于混合策略的改进多目标麻雀覆盖优化算法。首先,建立无线与电力线融合通信多目标覆盖优化...为满足配用电场景差异化业务需求,综合考虑网络的覆盖率和连通性,构建无线与电力线通信(power line communication,PLC)融合通信覆盖模型,提出基于混合策略的改进多目标麻雀覆盖优化算法。首先,建立无线与电力线融合通信多目标覆盖优化问题。其次,利用Tent映射与透镜成像反向学习相结合的种群初始化方法增加种群的多样性。同时,提出双阶段的自适应收敛因子调整麻雀算法运行过程中探索者和追随者的比例,改进现有越界处理策略,引入交叉变异策略,提高算法全局搜索能力以及搜索精度。最后,引入快速非支配排序、拥挤度策略和最优外部存档,将单目标麻雀算法改为多目标算法。仿真结果表明,相较于多目标遗传算法、多目标粒子群算法、多目标进化算法,所提改进的多目标麻雀算法对目标区域覆盖率分别提升了5.2%、3.17%和5.93%。此外,所提算法求得的Pareto最优解集为无线与电力线融合通信网络节点部署提供了多种方案,对配用电通信网规划和建设具有重要指导意义。展开更多
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Project(2008BA00400)supported by the Foundation of Department of Science and Technology of Jiangxi Province,China
文摘A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented.It makes full use of information sharing and retains diversity from the principle of particle swarm optimization (PSO) and artificial immune system (AIS).The algorithm was analyzed in detail and proper swarm size,evolving generations,gene-exchange individual order,and gene-exchange proportion in molecule were obtained for better algorithm performances.According to the test results,the appropriate parameters are about 50 swarm individuals,over 3 000 evolving generations,20%-25% gene-exchange proportion in molecule with gene-exchange taking place between better fitness affinity individuals.The algorithm is practical and effective in maximizing the coverage probability with given number of sensors and minimizing sensor numbers with required coverage probability in sensor placement.It can reach a better result quickly,especially with the proper calculation parameters.
基金supported by the National Natural Science Foundation of China(61971470).
文摘This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions.This method analyzes and proves the properties of different deployment patterns in the optimal deployment sequence.Based on these properties and considering location restrictions,it introduces an optimization model of arc barrier coverage and aims to minimize the total deployment cost of heterogeneous multistatic radars.To overcome the non-convexity of the model and the non-analytical nature of the objective function,an algorithm combining integer line programming and the cuckoo search algorithm(CSA)is proposed.The proposed algorithm can determine the number of receivers and transmitters in each optimal deployment squence to minimize the total placement cost.Simulations are conducted in different conditions to verify the effectiveness of the proposed method.
文摘为满足配用电场景差异化业务需求,综合考虑网络的覆盖率和连通性,构建无线与电力线通信(power line communication,PLC)融合通信覆盖模型,提出基于混合策略的改进多目标麻雀覆盖优化算法。首先,建立无线与电力线融合通信多目标覆盖优化问题。其次,利用Tent映射与透镜成像反向学习相结合的种群初始化方法增加种群的多样性。同时,提出双阶段的自适应收敛因子调整麻雀算法运行过程中探索者和追随者的比例,改进现有越界处理策略,引入交叉变异策略,提高算法全局搜索能力以及搜索精度。最后,引入快速非支配排序、拥挤度策略和最优外部存档,将单目标麻雀算法改为多目标算法。仿真结果表明,相较于多目标遗传算法、多目标粒子群算法、多目标进化算法,所提改进的多目标麻雀算法对目标区域覆盖率分别提升了5.2%、3.17%和5.93%。此外,所提算法求得的Pareto最优解集为无线与电力线融合通信网络节点部署提供了多种方案,对配用电通信网规划和建设具有重要指导意义。
