移动机器人的完全遍历路径规划:生物激励与启发式模板方法  

Complete Coverage Path Planning of Mobile Robots:Biologically Inspired Neural Network and Heuristic Template Approach

作　　者：邱雪娜[1] 刘士荣[1] 俞金寿[2] Simon X.Yang 

机构地区：[1]宁波大学电气工程与自动化研究所,宁波315211 [2]华东理工大学自动化研究所,上海200237 [3]School of Engineering,The University of Guelph,Guelph,Ontario,Canada N1G 2W1

出　　处：《模式识别与人工智能》2006年第1期122-128,共7页Pattern Recognition and Artificial Intelligence

基　　金：浙江省自然科学基金(Y104560);浙江省留学回国基金

摘　　要：提出了基于生物激励神经网络的一种新的完全遍历路径规划方法.该方法集成了模板模型、启发式搜索和障碍物逼近算法.一种称为分流合作-竞争反馈网络的生物激励神经网络被用于移动机器人的工作环境建模,而模板模型法、启发式算法和障碍物逼近算法用于移动机器人的避障路径规划,其中障碍物逼近算法能够实现不规则形状障碍物周边区域的遍历,以进一步改善路径规划的覆盖区域,仿真研究表明,本文方法使得路径规划的性能得到明显的改进,例如规划路径的重叠率,而且算法简单有效.In this paper, a novel complete coverage path planning method based on biologically inspired neural network for mobile robot motion planning is developed , which integrates heuristic searching algorithm, template-based model and obstacle approaching algorithm. The biological neural network that is described by the shunting cooperative-competitive feedback network is used to model the environment of the workspace of mobile robot. The template-based model, heuristic searching algorithm and obstacle approaching algorithm are employed to plan the motion path of a mobile robot with obstacle avoidance. The obstacle approaching algorithm is used to cover the vicinity areas of the irregular obstacles so that the coverage area of the path planning is further improved . The simulation studies show that the performance of the path generated by the proposed method , such as the rate of the repeated coverage , is improved obviously , and the proposed algorithm is computationally simple and effective.

关 键 词：移动机器人 路径规划 神经网络 模板模型 启发式搜索 障碍物逼近算法 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]