[1]李晓萌,王道波,郭继凯,等.基于某种生物启发式算法的无人机航迹规划[J].机械与电子,2018,(11):15-19.
 LI Xiaomeng,WANG Daobo,GUO Jikai,et al.Path Planning of UAV Based on a Biological Heuristic Algorithm[J].Machinery & Electronics,2018,(11):15-19.
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基于某种生物启发式算法的无人机航迹规划
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2018年11期
页码:
15-19
栏目:
设计与研究
出版日期:
2018-11-24

文章信息/Info

Title:
Path Planning of UAV Based on a Biological Heuristic Algorithm
文章编号:
1001-2257(2018)11-0015-05
作者:
李晓萌王道波郭继凯杨 华王博航
(南京航空航天大学自动化学院,江苏 南京 211106)
Author(s):
LI Xiaomeng WANG Daobo GUO Jikai YANG Hua WANG Bohang
(College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)
关键词:
无人机 航迹规划 威胁模型 SSA算法
Keywords:
UAV path planning threat model SSA algorithm
分类号:
V249.1
文献标志码:
A
摘要:
无人机航迹规划问题是无人机实现自主飞行的重点和难点,本文提出了一种基于海洋生物启发式的算法,并将其应用于无人机航迹规划中。首先对飞行过程中的约束条件与威胁环境进行分析与建模,使用合适的规划空间方法,将种群初始化,根据位置更新策略不断进行种群迭代,使种群逐渐向最优解进化。仿真实验表明,该算法可有效提高全局搜索能力,避免陷入局部最优解,算法具有快速收敛和可靠性的特点,为无人机航迹规划研究提供了新思路。
Abstract:
Path planning is the difficult issue for UAV to achieve autonomous flight. An algorithm based on Marine biological heuristic was proposed and applied to UAV path planning. Firstly, the constraints and threat environment in the flight process were analyzed and modeled, the population was initialized by using the appropriate planning space method, and the population iteration was continuously performed according to the location update strategy to make the population gradually evolve to the optimal solution. The simulation results show that the algorithm can effectively improve the global search ability and avoid falling into the local optimal solution. The algorithm has the characteristics of fast convergence and reliability, and provides a new idea for the UAV path planning research.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-07-09
作者简介:李晓萌(1992-),女,安徽亳州人,硕士研究生,研究方向为导航、制导与控制; 王道波(1957-),男,河北易县人,教授,博士研究生导师,研究方向为无人机飞行控制与仿真、机电模拟技术、电动伺服控制技术、液压伺服技术。
更新日期/Last Update: 2018-11-24