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  • 标题:Towards a Robust Solution of the Non-Linear Kinematics for the General Stewart Platform with Estimation of Distribution Algorithms
  • 作者:Eusebio Eduardo Hernández Martinez ; Sergio Ivvan Valdez Peña ; Eduardo Sánchez Soto
  • 期刊名称:International Journal of Advanced Robotic Systems
  • 印刷版ISSN:1729-8806
  • 电子版ISSN:1729-8814
  • 出版年度:2013
  • 卷号:10
  • 期号:1
  • 页码:38
  • DOI:10.5772/52172
  • 语种:English
  • 出版社:SAGE Publications
  • 摘要:In robotics, solving the direct kinematics problem (DKP) for parallel robots is very often more difficult and time consuming than for their serial counterparts. The problem is stated as follows: given the joint variables, the Cartesian variables should be computed, namely the pose of the mobile platform. Most of the time, the DKP requires solving a non-linear system of equations. In addition, given that the system could be non-convex, Newton or Quasi-Newton (Dogleg) based solvers get trapped on local minima. The capacity of such kinds of solvers to find an adequate solution strongly depends on the starting point. A well-known problem is the selection of such a starting point, which requires a priori information about the neighbouring region of the solution. In order to circumvent this issue, this article proposes an efficient method to select and to generate the starting point based on probabilistic learning. Experiments and discussion are presented to show the method performance. The method successfully avoids getting trapped on local minima without the need for human intervention, which increases its robustness when compared with a single Dogleg approach. This proposal can be extended to other structures, to any non-linear system of equations, and of course, to non-linear optimization problems.
  • 关键词:Hybrid optimizer; Probabilistic learning; Direct Kinematics; Parallel Robots
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