文章基本信息

标题：Estimation and Mapping of System-Surface Interaction by Combining Nonlinear Optimization and Machine Learning
本地全文：下载
作者：Eva Dierkes ; Christian Meerpohl ; Kathrin Flaßkamp 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：14
页码：138-143
DOI：10.1016/j.ifacol.2021.10.342
语种：English
出版社：Elsevier
摘要：AbstractExploring unknown environments is one of the main applications of mobile robotic systems. Since explorative trajectories can be used to gather information on the environment as well as on the internal dynamics of the robotic system, we propose a combined parameter estimation and mapping approach consisting of three steps: first, the parameter estimation problem is addressed by nonlinear optimization. Then, clustering is used to classify the estimated parameters. Finally, Support Vector Machines (SVMs) are used to expand the optimal parameter values of the recorded data onto the entire map. The proposed approach is applied to a wheeled mobile rover system in a scenario with sharply changing surface properties. Further, it is assumed that the model structure is known and the slip parameter is estimated during the exploration as it depends on system-surface interaction. From the simulations, it was demonstrated that the proposed approach can estimate the position-dependent slip parameter and identify more than 90% of the surface map.
关键词：KeywordsRobotic explorationParameter estimationModelingidentification of nonlinear systemsMachine learning