摘要
传统的标准蚂蚁算法及A*算法求解无人机多目标三维航迹规划存在需设置导航节点及构建VORONO I图等缺陷,针对这一问题,提出了一种改进的蚂蚁算法.该算法将导引因子引入到状态转移策略中,减少了蚂蚁局部搜索的盲目性,确保蚂蚁形成有效航迹,解决了将该算法应用于航迹规划的两个构造难题,即航迹节点不固定和局部搜索难以到达目标节点这两个难题.将雷达、导弹、高炮及大气威胁模型的最大作用距离和有效作用距离等约束条件引入代价函数及启发因子计算中,解决了航迹规划多约束求解困难等问题.仿真结果表明,该算法构造合理,蚂蚁算法无需导航节点及VORONO I图便可自主寻找到目标节点,且收敛速度满足航迹规划要求,生成的航迹代价较小.
The traditional standard ant and A＊ algorithms need to set the navigation node and establish the VORONOI map in solving the multi-objective 3D path planning of unmanned aerial vehicle（UAV）.To solve the above-mentioned problem,an improved ant algorithm was proposed.In the proposed algorithm,the guidance factor was introduced into the state transition strategy to reduce the blindness of local ant search,to ensure the formation of effective flight path and to solve two difficult problems in the application of the present algorithm for path planning,namely the non-fixing of path node and hard arriving of local search at target node.The constraint conditions such as maximum and effective action distances of radar,missile,artillery and atmosphere threat model were introduced into the calculation of both cost function and heuristic factor to solve the difficult multi-constrained solution of path planning.The simulation results show that the proposed algorithm has the reasonable structure,and can autonomously search out the target node without both navigation node and VORONOI map.Moreover,the convergence speed of the algorithm can meet the demand of path planning with less path cost.
出处
《沈阳工业大学学报》
EI
CAS
2011年第5期570-575,共6页
Journal of Shenyang University of Technology
基金
国家自然科学基金资助项目(60974105)
航空科学基金资助项目(2009ZC52041)
南京航空航天大学基本科研业务费专项研究基金资助项目(NP2011005)
关键词
蚂蚁算法
航迹规划
无人机
威胁代价
油耗代价
导引因子
导航节点
VORONOI图
ant algorithm
path planning
unmanned aerial vehicle
threat cost
fuel consumption cost
guidance factor
navigation node
VORONOI map
