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首页> 外文会议>Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer >Shape of object recognition based on information fusion for intelligent robot
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Shape of object recognition based on information fusion for intelligent robot

机译：基于信息融合的智能机器人目标识别形状

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摘要

when moving under unknown environment, the intelligent robot must have the capability of recognizing object. In this article, we focused on studying two aspects during object recognition, one was extraction of target shape feature, and the other was recognition algorithm. On studying feature extraction, we proposed the apothem sequence that is shape descriptor based on object border and took it as characteristic quantity of recognition. Experiment show that apothem sequence is a simple and effective. On studying recognition algorithm, the RS-ANN information fusion algorithm combined rough set theory with neural network was proposed. At first, we reduced the information table by Rough set, which was formed by training sample set, in order to unearth minimal decision-making regulations, and then the structure of BP network was confirmed, and the shape of object is recognized finally. Experimental results show that the algorithm solved the problem of redundant feature samples, so met real-time requirements of object recognition of the mobile robot in a dynamic environment.
机译：在未知环境中移动时，智能机器人必须具有识别对象的能力。在本文中，我们集中于研究对象识别过程中的两个方面，一个是目标形状特征的提取，另一个是识别算法。在研究特征提取过程中，我们提出了基于对象边界的形状描述符即阿特姆序列，并将其作为特征识别量。实验表明，阿序序列是一种简单有效的方法。在研究识别算法的基础上，提出了将粗糙集理论与神经网络相结合的RS-ANN信息融合算法。首先，通过训练样本集形成的粗糙集对信息表进行简化，以挖掘出最小的决策规则，然后确定BP网络的结构，最终识别出物体的形状。实验结果表明，该算法解决了冗余特征样本的问题，满足了动态环境中移动机器人目标识别的实时性要求。

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