An extended virtual force field based behavioral fusion with neural networks and evolutionary programming for mobile robot navigation

K. Im, Se-Young Oh
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引用次数: 16

Abstract

A local navigation algorithm for mobile robots is proposed, based on the new extended virtual force field (EVFF) concept, neural network-based fusion for the three primitive behaviors generated by the EVFF, and the evolutionary programming-based optimization of the neural network weights. Furthermore, a multi-network version of the above neurally-combined EVFF has been proposed that lends itself not only to an efficient architecture but also to a greatly enhanced generalization capability. These techniques have been verified through both simulation and real experiments under a collection of complex environments.
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基于扩展虚拟力场的行为融合神经网络与进化规划的移动机器人导航
提出了一种基于扩展虚拟力场(EVFF)概念的移动机器人局部导航算法,基于神经网络对EVFF产生的三种原始行为进行融合,并基于进化规划优化神经网络权值。此外,还提出了上述神经组合EVFF的多网络版本,该版本不仅具有高效的架构,而且大大增强了泛化能力。这些技术已经在一系列复杂环境下通过仿真和实际实验进行了验证。
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