为了研究传统蚁群算法在水下智能机器人（AUV）的三维路径规划中由于算法本身的原因，初始信息素信息匮乏，路径搜索规划速度慢，容易陷入某个局部最优的状态等问题，提出一种改进型蚁群-粒子群融合算法，充分利用粒子群算法较强的全局搜索能力。在环境模型的基础上建立干扰模型，将障碍与探测作为突破点，提出代价函数用于蚁群算法的优化中。设计了新的信息素分布（包括更新和挥发）和启发函数，在转移概率公式中加入区域安全因素，提高了三维路径规划的准确度；为了提高三维路径规划的速度，在传统粒子群算法基础上优化权重函数。最后进行仿真，结果证明该方法有效可行。
The reason why the inadequate initial information, and slow to search path planning, easy to fall into a local optimal state, is that the optimization itself that the traditional ant colony in intelligent underwater robot (AUV). It was proposed an improved ant colony optimization and particle swarm optimization. That was made full use of the strong ability of ant colony optimization in the whole process. Not only on the basic of the environment model that was established the interference model, and was brook through the barrier and detection, but also the cost function was put forward for the optimization of ant colony optimization. The design of a new pheromone distribution (including updating and evaporation)and heuristic function. And we was added regional security factors in the transition probability formula that improved the accuracy of three-dimensional path planning. In order to improve the speed of three-dimensional path planning that was optimization of weight function in the traditional particle swarm algorithm. Simulation experiments prove the feasibility and validity of this method.
2018,40(10): 72-77 收稿日期：2018-04-04
DOI：10.3404/j.issn.1672-7649.2018.10.014
分类号：TP301.6
作者简介：付振秋(1989-),男,工程师,研究方向为控制工程与信息化
参考文献：
[1] 周兵, 杨凡, 徐琪. 基于ADAMS的转向机构的优化设计[J]. 湖南大学学报(自然科学版), 2008, 35(12):23-26. ZHOU Bing,YANG Fan,XU Qi. Optimization design of steering linkage based on ADAMS[J]. Journal of Hunan University(NaturalSciences), 2008, 35(12):23-26.
[2] 陈岩, 苏菲, 沈林成. 概率地图UAV航线规划的改进型蚁群算法[J]. 系统仿真学报, 2009, 21(06):1658-1662, 1666. CHEN Yan,SU Fei,SHEN Lin-cheng. Improved ant colony algorithm based on PRM for UAV route planning[J]. Journal of System Simulation, 2009, 21(06):1658-1662, 1666.
[3] 屈鸿, 黄利伟, 柯星. 动态环境下基于改进蚁群算法的机器人路径规划研究[J]. 电子科技大学学报, 2015, 44(02):260-265. QU Hong,HUANG Li-wei,KE Xing. Research of improved ant colony based robot path planning under dynamic environment[J]. Journal of University of Electronic Science and Technology of China, 2015, 44(02):260-265.
[4] 喻环. 改进蚁群算法在机器人路径规划上的应用研究[D]. 合肥:安徽大学, 2017.
[5] 周岩, 王盛, 高传善, 等. 基于改进粒子群算法的模糊神经网络及其在短时天气预报中的应用[J]. 计算机应用与软件, 2010, 27(05):234-237. ZHOU Yan,WANG Sheng,GAO Chuan-shan,et al. An improved pso-based fuzzy neural network and its application in short-term weather forecast[J]. Computer Applications and Software, 2010, 27(05):234-237.
[6] 邱莉莉. 基于改进蚁群算法的机器人路径规划[D]. 上海:东华大学, 2015.
[7] 李心. 基于改进粒子群蚁群融合算法的智能移动机器人路径规划研究[D]. 沈阳:东北大学, 2014.
[8] 高敏. 基于粒子群蚁群算法的路径规划研究[D]. 南昌:南昌航空大学, 2013.
[9] 胡荟. 基于改进蚁群算法的机器人三维路径规划技术的研究[D]. 金华:浙江师范大学, 2012.
