基于人工势场与细胞自动机的移动机器人路径规划算法

A mobile robot path planning algorithm based on artificial potential field and cellular automata

提出了一种基于细胞自动机(Cellular Automata,CA)和人工势场的全向移动机器人路径规划算法,并通过一个4层的细胞自动机模型实现了该算法。通过构造扩张的障碍占位网格地图可在规划算法中将机器人简化为一个点,然后通过建立数值化的障碍人工势场图来考虑障碍物的局部影响,并使用CA模型得到距离传播图,最后通过搜索势场超曲面的最小值获得从起始点到目标点的最优无碰撞路径。仿真结果表明,提出的算法可以获得最优无碰撞路径,最优路径足够光滑且与障碍有较大的安全距离,便于全向移动机器人跟踪。

A path-planning algorithm for an omnidirectional mobile robot is presented based on Cellular Automata（CA） and artificial potential field, and the algorithm has been implemented by a 4-layer cellular automata model. First, an expanded occupancy grid map is constructed so that the mobile robot can be simplified as a point in the planning algorithm. Second, a digital obstacles artificial potential field map is obtained to include the local influence of the obstacles. Then, a distance propagation map is generated by a CA model. Finally, the optimal collision-free path from starting point to goal is extracted by following the minimum valley of the potential hypersurface. The simulation results show that the optimal collision-free paths can be found by the proposed algorithm. The optimal paths are smooth enough and have larger safety distance from the obstacles. So the optimal paths are convenient to track by an omnidirectional mobile robot.