基于蚁群算法的改进A*算法研究

基于蚁群算法的改进A*算法研究(任务书,开题报告,外文翻译,文献检索摘要,论文25000字)
摘  要
本文在无人驾驶的背景下，基于matlab平台对A*规划算法展开了系统而完整的研究，详细介绍了A*算法的发展由来和进化路线，并对路径搜索算法也展开了介绍，并指出了A*算法存在的启发性不足导致路线冗余，鲁棒性不强，复杂地图处理能力弱这三大问题，同时介绍了生物智能算法在路径搜索问题上的应用，并详细研究了蚁群与路径搜寻的关系，最终融合了蚁群算法与A*算法提出了自己的算法流程，并运用自己的改进方法修正了本文所分析出的问题。
研究结果表明本文借助蚁群算法对于路线的局部优化能力较有效的改正了A*算法的路线冗余问题和鲁棒性弱的缺点，同时A*算法的全局性强的特性又弥补了蚁群的收敛缺点，两个算法互相弥补得到了较好的融合。
本文的特色在于创意性的将A*与生物智能算法相融合，同时把改进方向放在了冗余路线优化上，与现有的研究方向相区分又紧密联系。

关键词：A*算法；蚁群算法；冗余路线优化
 
Abstract
In the background of unmanned driving, this paper systematically and commylxetely studies the A* mylxanning algorithm based on matlab mylxatform, introduces the development origin and evolutionary luxian of A* algorithm in dqfjzil, and introduces the path search algorithm, and points out The insufficiency of A* algorithm leads to three major problems: luxian redundancy, weak robustness and weak commylxex map processing ability. At the same time, the apmylxication of bio-intelligence algorithm in path search problem is introduced, and ant colony is studied in dqfjzil. The relationship with the path search finally combines the ant colony algorithm and the A* algorithm to propose its own algorithm flow, and uses its own improved method to correct the problems analyzed in this paper.
The research results show that the ant colony algorithm can effectively correct the luxian redundancy problem and the weak robustness of the A* algorithm by means of the local optimization ability of the luxian. At the same time, the global strong feature of the A* algorithm makes up for the ant colony. The shortcomings of convergence, the two algorithms commylxement each other and get a better fusion.
The characteristic of this paper is the creative integration of A* and bio-intelligence algorithms, and the improvement direction is mylxaced on the optimization of redundant luxians, which is closely related to the existing research directions.
 
Key Words：A* algorithm; ant colony algorithm; redundant luxian optimization
 
目录
第1章 绪论.....................................................................................................................1
第2章 文献回顾.............................................................................................................4
第3章 算法改进.............................................................................................................6
  3.1 地图的栅格化........................................................................................................6
    3.1.1 地图概况.........................................................................................................6
    3.1.2 地图表示方法.................................................................................................7
    3.1.3 地图识别.........................................................................................................8
  3.2 启发函数改进.......................................................................................................10
    3.1.1 启发函数介绍................................................................................................10
    3.1.2 传统改进函数................................................................................................11
    3.1.3 启发改进函数................................................................................................12
  3.3 路线冗余改进.......................................................................................................15
    3.1.1 蚁群算法介绍................................................................................................15
    3.1.2 蚁群算法融合................................................................................................16
第4章 算法结果与分析................................................................................................22
  4.1 地图结果...............................................................................................................22
  4.2 A*算法处理结果...................................................................................................23
  4.3 蚁群算法处理结果...............................................................................................24
第5章 算法评价............................................................................................................31
第6章 算法落地平台及展望.........................................................................................33
参考文献
致谢