精确势场构建中的聚合算子

I. Nagy, Georgi Dinev, A. Dineva
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引用次数: 0

摘要

在现代工程和人工智能的各个学科中,对信息融合的需求变得越来越重要。聚合算子能够有效地支持来自不同来源的信息和数据的合并,从而做出正确的决策或表示和改进各种系统的通用知识。距离传感是智能移动机器人的基础。对传感器组合获得的数据进行智能处理,可以提取有用的信息来估计机器人的环境状态,特别是通过势场构建方法。势场的精度是基于agent测量得到的距离估计向量。为了引入更现实的距离评估过程,我们提出了加权有序加权平均算子在多智能体系统中的应用。传统的加权方法需要对增益因子进行调整,取而代之的是聚合算子。所提出的技术允许考虑测量的重要性和不确定度的影响,在扫描点的测量误差。仿真结果表明,该方法在减少智能体数量的基础上,提高了构建势场的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Aggregation operators in accurate potential field building
The need of information fusion has become increasingly important in various disciplines of modern engineering and artificial intelligence. Aggregation operators are efficiently support the merge of information and data from different sources in order to make proper decisions or to represent and improve generic knowledge of various system. The range sensing is a foundation of intelligent mobile robotics. Intelligent processing of data obtained by combination of sensors allows extracting useful information to estimate the state of the robot's environment especially by potential field building method. The accuracy of a potential field is based on the distance estimate vector obtained by measurements of the agents. In order to introduce more realistic distance evaluation process we propose the application of the weighted ordered weighted averaging (WOWA) operator in the multi-agent system (MAS). The traditionally used weighting method that required the tuning of a gain factor is replaced with the aggregation operator. The proposed technique allows considering both the importance of measurements and the effects of uncertainties, measurement errors at the scan points. Simulation results validate that the proposed technique improves the accuracy of the built potential field besides applying lower number of agents.
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