面向未知地图的六足机器人路径规划算法

针对移动机器人路径规划中无法准确得知全局地图的问题，提出了一种基于模糊规则和人工势场法的局部路径规划算法。首先，利用测距组与模糊规则，进行障碍物的形状分类，构建局部地图；其次，在人工势场法中引入了一种修正的斥力函数，基于局部地图，利用人工势场法进行局部路径规划；最后，随着机器人的运动，设置时间断点，以减少路径震荡。针对随机障碍物和凹凸障碍物的地图，分别采用传统人工势场法和改进的人工势场法进行仿真，其结果表明：在遇到随机障碍物时，相比传统人工势场法，改进的人工势场法能够显著减少与障碍物的碰撞；在遇到凹凸障碍物时，改进的人工势场法能够很好地完成路径规划的目标。所提算法对地形变化适应能力强，能够实现在未知地图下的六足机器人路径规划。

Six-legged robot path planning algorithm for unknown map

The global map cannot be accurately known in the path planning of mobile robots. In order to solve the problem, a local path planning algorithm based on fuzzy rules and artificial potential field method was proposed. Firstly, the ranging group and fuzzy rules were used to classify the shape of obstacles and construct the local maps. Secondly, a modified repulsive force function was introduced in the artificial potential field method. Based on the local maps, the local path planning was performed by using the artificial potential field method. Finally, with the movement of robot, time breakpoints were set to reduce path oscillation. For the maps of random obstacles and bumpy obstacles, the traditional artificial potential field method and the improved artificial potential field method were respectively used for simulation. The experimental results show that, in the case of random obstacles, compared with the traditional artificial potential field method, the improved artificial potential field method can significantly reduce the collision of obstacles; in the case of bumpy obstacles, the improved artificial potential field method can successfully complete the goal of path planning. The proposed algorithm is adaptable to terrain changes, and can realize the path planning of six-legged robot under unknown maps.