基于语义分割网络的AGV路径规划算法

AGV searching path planning algorithm based on semantic segmentation network

针对基于栅格地图的路径规划技术在面对大地图、高分辨率地图的情况下,存在的规划速度慢、内存占用高的问题,提出一种基于语义网络的网络搜索算法。首先使用语义分割网络对栅格地图进行预采样,其次通过图像学膨胀拓宽最优路径形成最优路径范围,增强算法鲁棒性,最后利用语义网络的特征图指导搜索算法规划,加快了高分辨率栅格地图的路径规划的速度。实验仿真表明,网络搜索算法较传统搜索算法,时间平均缩短72.5%,遍历点数平均减少51.6%,路径长度平均延长0.73%,网络搜索算法可以有效加快路径搜索速度,减少内存占用。

A semantic network-based network search algorithm is proposed to address the problems of slow planning speed and high memory occupation of raster map-based path planning techniques in the face of large maps and high-resolution maps.Firstly,a semantic partitioning network is used to pre-sample the raster map,secondly,the optimal path range is formed by widening the optimal path through imagery expansion to improve the robustness of the algorithm,finally,the feature map of the semantic network is used to guide the planning of the search algorithm,which speeds up the path planning of the high-resolution raster map.Experimental simulations show that the network search algorithm reduces the time by an average of 72.5%,the number of traversal points by an average of 51.6%,and the path length by an average of 0.73%compared to the traditional search algorithm,and the network search algorithm can effectively speed up the path search and reduce the memory occupation.