Abstract: In order to utilize the advantage of the feature map in calculating efficiency and solve the problem that the traditional dynamic window approach is sensitive to global parameters, a path planning fusion algorithm based on feature map is proposed. A feature map expression applicable to path planning is given, and the detection of obstacles in the feature map is achieved by improving the calculation method of the distance between the robot and the obstacles. Combined with the basic principle of the Bug algorithm and the properties of line segment features, the searching and optimization algorithm is used to search the global feasible path first, and then the key nodes of the global optimal path are obtained by node optimization, and solutions are proposed for the problems of search direction selection at internal and external corner points and obstacle endpoint bypassing. To address the problem of high sensitivity of the traditional dynamic window approach to global parameters, the degree of influence of the parameters in the objective function on the planned path when the robot is at different positions is analyzed, and the original objective function is improved using the dynamic parameter approach. When the algorithms are fused, the calculation method of direction function in the objective function is improved in order to solve the problem that the robot may slow down in the intermediate nodes of the path. The simulation experiment verifies that the searching optimization algorithm is effective, the improved dynamic window approach reduces the sensitivity of parameters, and the fusion algorithm has a greater advantage in computational efficiency, with a maximum reduction of 79.27% and a minimum reduction of 43.16% in computational time consumption, and the robot moves more smoothly.

Key words: path planning, feature map, Bug algorithm, dynamic window approach, fusion algorithm

摘要： 为利用特征地图计算效率高的优点，同时解决传统动态窗口法对全局参数敏感的问题，提出一种基于特征地图的路径规划融合算法。通过给出适用于路径规划的特征地图表达方式，改进机器人与障碍物间距离的计算方法，实现了特征地图中障碍物的检测；结合爬虫（Bug）算法的基本原理和线段特征的属性，使用搜索优化算法，先搜索全局可行路径，再进行节点优化得到全局最优路径的关键节点，并对内外角点处搜索方向选择、障碍物端点绕行等问题提出了解决方法；针对传统动态窗口法对全局参数敏感性高的问题，分析了目标函数中各参数在路径不同位置对规划路径的影响程度，使用动态参数的方法对原目标函数进行改进；算法融合时，改进方向函数的计算方法，解决了机器人在路径中间节点出现明显减速的问题。经仿真实验验证，搜索优化算法有效，改进后的动态窗口算法降低了参数的敏感性，融合算法在计算效率方面有较大的优势，计算耗时最多减小79.27%，最少减小43.16%，而且机器人移动更平滑。

关键词: 路径规划, 特征地图, 爬虫算法, 动态窗口法, 融合算法