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杜云,彭瑜,邵士凯,等. 基于改进粒子群优化的多无人机协同航迹规划[J]. 科学技术与工程, 2020, 20(32): 13258-13264.
DU Yun,PENG Yu,et al.Cooperative Path Planning of Multi-unmanned Aerial Vehicle Based on Improved Particle Swarm Optimization[J].Science Technology and Engineering,2020,20(32):13258-13264.
基于改进粒子群优化的多无人机协同航迹规划
Cooperative Path Planning of Multi-unmanned Aerial Vehicle Based on Improved Particle Swarm Optimization
投稿时间:2019-09-16  修订日期:2020-06-30
DOI:
中文关键词:  多无人机  协同航迹规划 改进粒子群算法  混沌初始化
英文关键词:multi-unmanned  aerial vehicle  coordinated flight  path planning  improved particle  swarm optimization  algorithm chaos  initialization
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
           
作者单位
杜云 河北科技大学
彭瑜 河北科技大学
邵士凯 河北科技大学
刘冰 河北科技大学
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中文摘要:
      由于航迹规划可以为多无人机飞行控制提供参考指令,且当前粒子群航迹规划算法存在收敛速度慢,成功率不高的缺点,故提出了一种综合改进粒子群的多无人机协同航迹规划算法,考虑了无人机性能约束、障碍与威胁约束、空间协同与时间协同约束。首先,通过对学习因子线性化调整,实现了粒子惯性和最优行为的平衡;其次,引入混沌初始化,改善了粒子分布质量;然后,基于遗传变异思想设计了取代策略,同时提出了调速机制,提升了算法收敛速度。最后,将综合改进粒子群算法进行仿真验证,规划结果成功率高、收敛速度快且航迹代价小,可见改进算法的有效性。
英文摘要:
      Because path planning can provide reference instructions for multi-unmanned aerial vehicle (UAV) flight control, and current particle swarm trajectory planning algorithms have the disadvantages of slow convergence and low success rate, a multi-UAV with comprehensive improvement of particle swarm is proposed. The collaborative path planning algorithm considers UAV performance constraints, obstacle and threat constraints, space collaboration and time collaboration constraints. Firstly, through the linear adjustment of the learning factors, the balance between particle inertia and optimal behavior was achieved; secondly, chaotic initialization was introduced to improve the quality of particle distribution; then, a replacement strategy was designed based on the idea of genetic mutation, and a speed regulation mechanism was also proposed , Which improves the convergence speed of the algorithm. Finally, a comprehensive improved particle swarm algorithm is used for simulation verification. The planning results have a high success rate, fast convergence speed, and low path cost, which shows the effectiveness of the improved algorithm.
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