基于定向约束的脉冲耦合神经网络路径规划

Path Planning Based on Pulse Coupled Neural Networks with Directed Constraint

文中提出了一种基于定向约束的脉冲耦合神经网络的路径规划方法。该方法基于脉冲耦合神经网络,不需要进行经典神经网络的前期训练,将拓扑化地图与脉冲耦合神经网络相结合,设计距离和角度约束,从而减少了脉冲耦合神经网络中激活的神经元数量,加快了路径规划速度。仿真结果表明该路径规划算法的运算时间比A^*算法更短。

This paper proposed a path planning method based on pulse coupled neural networks(PCNN)with directed constraint.This application does not require pre-training and is different with classical neural networks.The method combines topological maps with PCNN,and designs distance and constraints angle.In this way,the number of activated neurons is reduced,and the effectiveness of path planning is improved.Compared with the A^*algorithm,the simulation results show that this path planning algorithm is faster.