A novel formulation for determining joint constraint loads during optimal dynamic motion of redundant manipulators in DH representation

Joo H. Kim, Jingzhou Yang, Karim Abdel-Malek

Research output: Contribution to journalArticlepeer-review

Abstract

The kinematic representations of general open-loop chains in many robotic applications are based on the Denavit-Hartenberg (DH) notation. However, when the DH representation is used for kinematic modeling, the relative joint constraints cannot be described explicitly using the common formulation methods. In this paper, we propose a new formulation of solving a system of differential-algebraic equations (DAEs) where the method of Lagrange multipliers is incorporated into the optimization problem for optimal motion planning of redundant manipulators. In particular, a set of fictitious joints is modeled to solve for the joint constraint forces and moments, as well as the optimal dynamic motion and the required actuator torques of redundant manipulators described in DH representation. The proposed method is formulated within the framework of our earlier study on the generation of load-effective optimal dynamic motions of redundant manipulators that guarantee successful execution of given tasks in which the Lagrangian dynamics for general external loads are incorporated. Some example tasks of a simple planar manipulator and a high-degree-of-freedom digital human model are illustrated, and the results show accurate calculation of joint constraint loads without altering the original planned motion. The proposed optimization formulation satisfies the equivalent DAEs.

Original languageEnglish (US)
Pages (from-to)427-451
Number of pages25
JournalMultibody System Dynamics
Volume19
Issue number4
DOIs
StatePublished - May 2008

Keywords

  • Denavit-Hartenberg representation
  • Differential-algebraic equations
  • Fictitious joints
  • Joint constraints
  • Lagrange multipliers
  • Motion planning
  • Optimization
  • Redundant manipulator

ASJC Scopus subject areas

  • Modeling and Simulation
  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications
  • Control and Optimization

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