摘要
针对蚁群算法应用于移动机器人路径规划时存在易于陷入局部最优解、收敛速度慢的问题,提出了一种适用于静态障碍环境下基于改进蚁群算法的移动机器人路径规划方法。该方法改进了节点间的状态转移规则,增加了得到最优路径的概率;自适应调整启发函数,提高了算法的搜索效率;基于狼群法则对信息素进行更新,有效避免了算法陷入局部最优解;动态调整了衰减系数,在后期增加了蚂蚁对最优路径的选择概率,加快了算法的收敛速度。仿真实验表明,与其他算法在相同环境下比较,该改进算法在路径规划结果相同的情况下具有较快的收敛速度;且改进算法在不同复杂程度环境中均得到了最优路径,也表明了该算法的有效性和可靠性。该算法具有良好的寻优能力,可以适用于不同复杂环境中的移动机器人路径规划。
In order to solve the problem that the ant colony algorithm was used in path planning for mobile robot,and is easily falls into local optimum and has a slow convergence speed,the paper proposes a method that the improved ant colony algorithm was used in path planning for mobile robot in the static environment. The method improves the transition rule of node state to increase the probability of searching optimal path. An adaptive heuristic function improves the searching efficiency of the algorithm. Updating the pheromone avoids falling into local optimum based on the assignment rule of wolves.Dynamical adjustment of the decay parameter increases the probability of choosing the optimal path by ants at a later stage,thus accelerating the convergence speed. The simulation results show that the improved algorithm has faster convergence speed under the condition of the same result of path planning when compared to other algorithm in the same environment.The improved algorithm obtains the optimal path in the environments of different complexity levels and it shows the efficiency and feasibility of the improved algorithm,which has good optimization ability and can be applied to path planning for mobile robot in the environments of different complexity levels.
出处
《重庆邮电大学学报（自然科学版）》
CSCD
北大核心
2016年第6期849-855,共7页
Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)
基金
国家自然科学基金(61201447)
河南省高等学校青年骨干教师资助计划项目(2014GGJS-084)
河南省科技创新杰出人才计划项目(134200510025)
河南省教育厅科学技术研究重点项目(13A520367)
郑州轻工业学院校级青年骨干教师培养对象资助计划项目(XGGJS02)
郑州轻工业学院博士科研基金(2010BSJJ038)
郑州轻工业学院研究生科技创新基金~~
关键词
移动机器人
蚁群算法
路径规划
mobile robot
ant colony algorithm
path planning
