Study Of Target Recognition Based On Mobile Robot

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.