论文:2013,Vol:31,Issue(5):683-688
引用本文:
唐必伟, 方群, 朱战霞, 马卫华. 基于改进蚁群算法的无人飞行器二维航迹规划[J]. 西北工业大学
Tang Biwei, Fang Qun, Zhu Zhanxia, Ma Weihua. Effective 2D Route Planning of UAV Based on Improved Ant Colony Algorithm[J]. Northwestern polytechnical university

基于改进蚁群算法的无人飞行器二维航迹规划
唐必伟1,2, 方群1,2, 朱战霞1,2, 马卫华1,2
1. 西北工业大学 航天学院, 陕西 西安 710072;
2. 西北工业大学 航天飞行动力学技术国家级重点实验室, 陕西 西安 710072
摘要:
蚁群算法作为一种启发式仿生算法,在飞行器航迹规划中应用十分广泛。目前许多学者对基本蚁群算法进行了改进,其中包括信息素挥发系数的自适应调节,这种改进使得信息素挥发模式是固定的。为了更加真实模拟信息素挥发情况,并提高算法的实时性,提出了一种信息素挥发系数的随机自适应调节方法;并且通过引入飞行约束条件的限制来剔除不满足飞行约束条件限制的节点和这些节点所扩展出来的航迹,从而进一步提高算法实时性;另外通过数学上的几何方法剔除不满足飞行安全的节点和这些节点所扩展出来的航迹,得到满足威胁规避的航迹,即"零威胁"航迹;然后采用蚁群算法在这些"零威胁"航迹中优化搜索出一条能够使目标函数仅有航程唯一待优化量的最优航迹,即所谓"单因子"目标函数优化航迹。仿真分析结果表明:提出的"零威胁-单因子"方法不仅可以提高算法的收敛速度,还可以降低优化方法的难度,充分显示了该算法的优越性。
关键词:    无人驾驶飞行器    航迹规划    改进蚁群算法    随机自适应调节    零威胁-单因子   
Effective 2D Route Planning of UAV Based on Improved Ant Colony Algorithm
Tang Biwei1,2, Fang Qun1,2, Zhu Zhanxia1,2, Ma Weihua1,2
1. College of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China;
2. Science and Technology on Aerospace Flight Dynamics Laboratory, Xi'an 710072, China
Abstract:
Ant colony algorithm,as a kind of heuristic bionic algorithm,has been widely applied in aircraft route planning. At present,many scholars make great efforts to improve basic ant colony algorithm,including self-adapting of pheromone volatile coefficient,which makes pheromone volatilize in a fixed model. In order to simulate pheromone volatilizing conditions in a more reasonable way,as well as to improve the real-time performance of the algorithm,we propose a method called random self-adapting of pheromone volatile coefficient. By introducing flight constraint conditions of aircraft,we weed out the nodes that do not satisfy the flight constraint conditions,so as to further improve the real-time performance. In addition,we use mathematical geometric method to eliminate the nodes and their extended out tracks which do not satisfy the flight safety. The remaining candidate routes are named "zero threat"routes. Then the ants start to search among the "zero threat"routes to find out a best route which is the shortest route. The "zero threat"routes make the objective function have only a single factor. The simulation results and their analysis show preliminarily that the proposed "zero threat-single factor"method can not only improve the algorithm convergence speed,but also can reduce the difficulty of optimization methods,thus fully displaying its superiority.
Key words:    aircraft    algorithms    computer simulation    schematic diagrams    unmanned vehicles    improved ant colony algorithm    random adaptive algorithm    route planning    zero threat-single factor   
收稿日期: 2012-09-04     修回日期:
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作者简介: 唐必伟(1987-),西北工业大学博士研究生,主要从事无人驾驶飞行器航迹规划研究与轨迹跟踪控制的研究。
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