Real-Time Robot Path Planning Based on a Modified Pulse-Coupled Neural Network Model
Real-Time Robot Path Planning Based on a Modified Pulse-Coupled Neural Network Model
复制标题
基于改进脉冲耦合神经网络模型的实时机器人路径规划
DOI：
10.1109/tnn.2009.2029858
复制
发表时间：
2009-11-01
影响因子：
--
通讯作者：
Yi, Zhang
中科院分区：
文献类型：
--
作者：
Qu, Hong;Yang, Simon X.;Yi, Zhang
本文提出了一种改进的脉冲耦合神经网络（MPCNN）模型，用于非静止环境中移动机器人的实时无碰撞路径规划。所提出的机器人神经网络是拓扑组织的，神经元之间仅存在局部横向连接。它在动态环境中工作，不需要事先了解目标或障碍物的运动。目标神经元首先放电，然后放电事件通过神经元之间的横向连接扩散出去，就像波的传播一样。障碍物与其邻居没有任何联系。每个神经元都会记录其父神经元，即导致其放电的邻居。实时最优路径就是从机器人到目标的父母序列。在障碍物和目标静止的静态情况下，本文证明网络中生成的波向外传播，传播时间与神经元之间的连接强度成正比。因此，生成的路径始终是从机器人到目标的全局最短路径。此外，所提出的模型中的每个神经元都可以将激发事件传播到其相邻的神经元，而无需任何比较计算。该模型用于为移动机器人生成无碰撞路径，以解决迷宫型问题、绕过凹形 U 形障碍物以及在障碍物变化的环境中跟踪移动目标。通过模拟和比较研究证明了所提出方法的有效性和效率。
This paper presents a modified pulse-coupled neural network (MPCNN) model for real-time collision-free path planning of mobile robots in nonstationary environments. The proposed neural network for robots is topologically organized with only local lateral connections among neurons. It works in dynamic environments and requires no prior knowledge of target or barrier movements. The target neuron fires first, and then the firing event spreads out, through the lateral connections among the neurons, like the propagation of a wave. Obstacles have no connections to their neighbors. Each neuron records its parent, that is, the neighbor that caused it to fire. The real-time optimal path is then the sequence of parents from the robot to the target. In a static case where the barriers and targets are stationary, this paper proves that the generated wave in the network spreads outward with travel times proportional to the linking strength among neurons. Thus, the generated path is always the global shortest path from the robot to the target. In addition, each neuron in the proposed model can propagate a firing event to its neighboring neuron without any comparing computations. The proposed model is applied to generate collision-free paths for a mobile robot to solve a maze-type problem, to circumvent concave U-shaped obstacles, and to track a moving target in an environment with varying obstacles. The effectiveness and efficiency of the proposed approach is demonstrated through simulation and comparison studies.