【作者】 郑勇；

【导师】 费凌；

【作者基本信息】 西华大学 ， 机械设计及理论， 2011， 硕士

【摘要】 移动机器人的目标识别是以图像处理、分析和理解为基础,是一项多学科综合的复杂技术,现在已经渗透到军事、空间探索、医学、工业等各个领域,具有很大的适用价值和重要意义。本文是对移动机器人的目标识别技术进行研究,主要完成以下几个方面的工作:首先搭建移动机器人的软硬件平台,采用上下位机的结构,下位机采用于S3C2410和C8051F350作为处理器并搭建必要外部电路,用于对图像信息的采集及机器人自主移动的控制。下位机采集图像信息后,通过无线网络将图像数据实时传送至上位机,上位机选用PC机,用于对图像数据进行预处理及目标识别,最后将识别结果传回移动机器人作为机器人的决策依据。其次就是分析移动机器人的主要噪声干扰,介绍在室内环境下的图像滤波算法和二值化算法,通过实验仿真,选择对抖动噪声具有抑制作用的滤波算法,提出Ostu阈值分割算法与Sobel边缘检测算子相结合的图像二值化处理策略。接着研究对预处理后的目标图像的特征提取,在分析相对不变矩、仿射不变矩、Zernike矩以及NMI优缺点的基础上,提出了一种新的组合不变矩,并对其进行重点研究,通过实验仿真表明,新提出的组合不变矩不仅具有类内聚合类间可分性,而且还具有一定的抗干扰能力,可以用来作为移动机器人目标识别的特征向量。最后介绍RBF神经网络模式分类器,提出RBF神经网络的训练算法,并提出应用RBF神经网络完成基于图像组合不变矩的目标识别算法,最后通过对均匀性与非均匀性背景的相似目标图像进行实验仿真表明,该算法能准确的识别出目标对象。更多还原

【Abstract】 Target recognition of mobile robot is based on image processing, analysis and understanding. It is a multidisciplinary integrated complex technology, and it has been applied successfully in the military, space exploration, medical and industrial fields, etc. So it has greatly importance and practical value. In this paper, the main works which has been done are as following:Firstly, the software and hardware platform of mobile robot ware been built, the structure of the system adopted two host, one host was C8051F350 processor ,S3C2410 processor based on ARM920T and essential hardware, which is used for image acquisition and controlling the motion of mobile robot. When the host acquisitioned image, Then transfer the collected target image to PC by wireless LAN in real time. The other host is PC, which is used for pre-processing and recognizing target image which is received from S3C2410. After completing the image recognition on PC, then return the recognition results to the mobile robot.Secondly, Analyses the main noise of mobile robot and research the image filtering algorithm and binary algorithm in indoor environment. Select the filtering algorithm for the jitter noise, Image binarization strategy which is Ostu threshold segmentation algorithm combining with Sobel edge detection was proposed via experiment and simulation.Thirdly, the research is carried out on the feature extraction of the target image after pre-processing. A new kind of combined invariant moments is proposed and researched here based on contrast of the relatively constant moments, affine invariant moments, Zernike moments and NMI. Simulation experiment shows that the new kind of combined moments has polymerizability within the class and has separability without the class and at the same time has a certain anti-interference ability, can be used as the feature vector for target recognization on the mobile robot.Finally, the paper introduced the RBF neural network model, puts forward the RBF neural network classifier training algorithm, and proposed a target recognition algorithm which is based on combining RBF neural network and combined invariant moments. Finally, through simulation experiment on the similar target image with uniform and non-uniform background, the algorithm proves to identify the target accurately. Finally, through simulation experiment on the similar target image with uniform and non-uniform background, the algorithm proves to identify the target accurately.更多还原