Guided Autowave Pulse Coupled Neural Network (GAPCNN) based real time path planning and an obstacle avoidance scheme for mobile robots

被引：38

作者：

Syed, Usman Ahmed ^{[1
]}

Kunwar, Faraz ^{[1
]}

Iqbal, Mazhar ^{[1
]}

机构：

[1] Natl Univ Sci & Technol, Coll Elect & Mech Engn, Mechatron Engn Dept, Probabilist Robot & Intelligent Syst Mot PRISM La, Islamabad, Pakistan

来源：

ROBOTICS AND AUTONOMOUS SYSTEMS | 2014年 / 62卷 / 04期

关键词：

Path planning; Obstacle avoidance; GAPCNN; Neural networks;

D O I：

10.1016/j.robot.2013.12.004

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Real time path planning for mobile robots requires fast convergence to optimal paths. Most rapid collision free path planning algorithms do not guarantee the optimality of the path. In this paper we present a Guided Autowave Pulse Coupled Neural Network (GAPCNN) approach for mobile robot path planning. The proposed model is a novel approach that improves upon the recently presented Modified PCNN (MPCNN) by introducing directional autowave control and accelerated firing of neurons based on a dynamic thresholding technique. Simulation studies and experimental results in both static as well as dynamic environments confirm GAPCNN to be a robust and time efficient path planning scheme for finding optimal paths. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：474 / 486

页数：13

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