@inproceedings{7d86df9ab3dc4db6b98b7b6ea97f0cd3,
title = "Learning control for space robotic operation using support vector machines",
abstract = "Automatical operation of space robots is a challenging and ultimate goal of space servicing. In this paper, we present a novel approach for tracking and catching operation of space robots based on learning and transferring human control strategies (HCS). We firstly use an efficient support vector machine (SVM) to parameterize the model of HCS, and then develop a new SVM-based leaning structure to improve HCS in tracking and capturing control. The approach is fundamentally valuable in dealing with some problems such as small sample data and local minima, which makes it efficient in modeling, understanding and transferring its learning process. The simulation results demonstrate that the proposed method is useful and feasible in tracking trajectory and catching objects autonomously.",
author = "Panfeng Huang and Wenfu Xu and Yangsheng Xu and Bin Liang",
year = "2006",
doi = "10.1007/11760023_176",
language = "英语",
isbn = "3540344373",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "1208--1217",
booktitle = "Advances in Neural Networks - ISNN 2006",
note = "3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks ; Conference date: 28-05-2006 Through 01-06-2006",
}