Newton-type algorithms for robot motion optimization

Al . : Newton - Type Algorithms for Robot Motion Optimization 3

Planning Robot Motion in a 2-D Region with Unknown Obstacles

The purpose of this paper is to present several algorithms for planning the motion of a robot in a two-dimensional region having obstacles whose shapes and locations are unknown. The convergence and efficiency of the algorithms are discussed and upper bounds for the lengths of paths generated by the different algorithms are derived and compared.

Newton methods for k-order Markov Constrained Motion Problems

This is a documentation of a framework for robotmotion optimization that aims to draw on classical constrained optimization methods. With one exception the underlying algorithms are classical ones: Gauss-Newton (with adaptive stepsize and damping), Augmented Lagrangian, log-barrier, etc. The exception is a novel any-time version of the Augmented Lagrangian. The contribution of this framework is...

On a Moving Base Robotic Manipulator Dynamics

There are many occasions where the base of a robotic manipulator is attached to a moving platform, such as on a moving ship, terrain or space shuttle. In this paper a dynamic model of a robotic manipulator mounted on a moving base is derived using both Newton-Euler and Lagrange-Euler methods. The presented models are simulated for a Mitsubishi PA10-6CE robotic manipulator characteristics mounte...

Robot Motion Vision Part II: Implementation

The idea of Fixation introduced a direct method for general recovery of shape and motion from images without using either feature correspondence or optical flow [1,2]. There are some parameters which have important effects on the performance of fixation method. However, the theory of fixation does not say anything about the autonomous and correct choice of those parameters. This paper presents ...