基于高程—四叉树模型和改进蚁群算法的路径规划  

Path Planning Based on Elevation-Quadtree Model and Improved Ant Colony Algorithm

作　　者：李建军[1] 高振军[1] 侯风华[1] 

机构地区：[1]信息工程大学,河南郑州450001

出　　处：《信息工程大学学报》2014年第6期723-728,742,共7页Journal of Information Engineering University

摘　　要：基于蚁群算法的无人车大区域路径规划方法大多存在速度慢、环境适应能力差等问题,构造了一种高程—四叉树模型,在完整记录区域信息的基础上对信息量进行有效压缩;设计了一种新的寻优启发函数,提高了路径规划的准确度;通过自适应调整挥发系数,避免搜索陷入局部最优。仿真实验结果表明,相比于传统蚁群算法,文章方法得出的最优路径更加准确,且算法复杂度低,收敛速度快。The large-scale path planning for unmanned vehicles based on the ant colony algorithm mostly is slow in planning speed and poor in adaptation to the environment. To address this prob- lem, this paper constructs a height-quadtree model, which compresses the amount of information ef- fectively based on the complete record of regional information. The paper designs a new heuristic function for optimization, improves the accuracy of path planning. By adjusting adaptively the volat- ilization coefficient, it prevents the search from being trapped into the local optimum. The simulation results show that, compared to the traditional ant colony algorithm, the optimal path obtained in this paper is more accurate, in addition, the algorithm is low in complexity and fast in convergence speed.

关 键 词：路径规划 高程-四叉树模型 蚁群算法 启发函数 挥发系数 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]