DocumentCode :
3244006
Title :
Robot visual planar locating method based on improved RBF network
Author :
Zhang, Mingjun ; Yang, Jie ; Shang, Yunchao ; Xu, Jianan
Author_Institution :
Coll. of Mech. & Electr. Eng., Harbin Eng. Univ., Harbin, China
fYear :
2009
fDate :
14-17 July 2009
Firstpage :
1740
Lastpage :
1744
Abstract :
For robot visual location, a locating method based on non-linear mapping planar orientation using improved radial basis function (RBF) network is presented. To improve the adaptability on the camera´s pose change, this method uses the pitch degree of camera as one of the input of RBF network, makes use of the non-linear approach of RBF network fits the non-linear mapping relationship from the image plane to the special plane, and positions the targets. In order to reduce the interaction between the RBF´s feeling domains, improve the locating accuracy, a Gaussian core function which could inhibit the nuclear feeling domain is presented, and the improved RBF network take the Gaussian core function for the use of RBF, the locating accuracy is enhanced. In order to reduce the redundancy in RBF network´s initial center selection, an improved method for initial center selection is proposed, this method form the samples in accordance with the norm, taking into account the infection of network´s implicit nodes and preset a redundant threshold. Setting different pitch degrees, the results of locating experiments express this method is effective in the application of target locating on the special plane. The comparison of results of locating experiments shows that, under the same condition, the precision of this method is higher than the precision of the visual locating methods based on planar homography matrix estimation which is in common use.
Keywords :
Gaussian processes; matrix algebra; mobile robots; neurocontrollers; nonlinear control systems; path planning; radial basis function networks; robot vision; Gaussian core function; RBF network; camera pose change; mobile robot visual planar locating method; nonlinear mapping planar orientation; nuclear feeling domain; pitch degree; planar homography matrix estimation; radial basis function network; Cameras; Educational institutions; Intelligent networks; Intelligent robots; Mechatronics; Mobile robots; Orbital robotics; Radial basis function networks; Robot vision systems; Transmission line matrix methods; RBF network; non-linear mapping; planar location; robot vision;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Intelligent Mechatronics, 2009. AIM 2009. IEEE/ASME International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-2852-6
Type :
conf
DOI :
10.1109/AIM.2009.5229807
Filename :
5229807
Link To Document :
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