[1]范莉丽 王奇志.改进的生物激励神经网络的机器人路径规划[J].计算机技术与发展,2006,(04):19-21.
　FAN Li-li,WANG Qi-zhi.Robot Path Planning of Modified Biologically Inspired Neural Networks[J].,2006,(04):19-21.
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改进的生物激励神经网络的机器人路径规划()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2006年04期
页码:
19-21
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Robot Path Planning of Modified Biologically Inspired Neural Networks
文章编号:
1005-3751（2006）04-0019-03
作者:
范莉丽 王奇志
北京交通大学计算机与信息技术学院
Author(s):
FAN Li-li; WANG Qi-zhi
School of Computer and Information Technology, Beijing J iaotong University
关键词:
移动机器人; 路径规划; 神经网络; 神经元活性值; 障碍回避
Keywords:
mobile robot; path planning; neural networks; neural dynamics; obstacle avoidsnce
分类号:
TP24
文献标志码:
A
摘要:
介绍了基于生物激励神经网络的移动机器人路径规划。机器人的路径生成过程是由神经网络组成动态变化的冲经元活性值状态路线图实现的。通过神经元活性值的传播，机器人被吸引到目标点，而同时障碍物使自己处在活性值最低点，起到推开机器人避碰的目的。仿真研究表明该方法生成的由起始点到目标点的路径是连续的、平滑的．避障的，不会陷入U形障碍物，与障碍物形状和所处位置无关，能对快速变化的环境做出迅速反应。但在当前位置邻近位置中具有最大活性值的位置不惟一的情况下，产生路径可能不理想，即到达目标点的避障路径是较长的，而不是最短或者是接近最短的。文中对该不足进行了分析，并提出了改进方法，使生成路径是最短的或是接近最短。对改进方法进行了仿真，实验结果证明该方法是有效的和可行的
Abstract:
The biologically inspired neural networks based path planning approacbes of mobile robot were introduced. The generated path of mobile robot is realized by the dynamics neural activity landscape consisting of networks. Robot was attracted to the target through the neural activity propagation,while the obstacles put away the robot to avoid collision by making themselves stay at the valley of the activity landscape. Simulation demonstated that the generated path was continuous, smooth, and obstacle avoidance, not trapped in concave Ushaped obstacle, has nothing to do with the shape and location of the obstacles , can respond quickly to the fast changing environment. But the generated path may not be ideal, in the sense that the obstacle avoided path is not the shortest or approximatly shortest but relatively long in the situation that the number of the neighboring position possessing the max aetivitiy of current position is not role. This disadvantage was analyzed in the paper ,and a modified method was proposed to make the generated path is the shorest or approximatly shorest. Simulate the modified method, the simulation results show that the method is valid and feasible

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备注/Memo

备注/Memo:
清华大学智能技术与系统国家重点实验室开放课题资助（0413）范莉丽（1978-），女，江西南昌人，硕士研究生，研究方向为移动机器人路径规划；王奇志，副教授，博士，研究方向为机器人智能控制、路径规划、多机器人协调等
更新日期/Last Update: 1900-01-01