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Rigid Body Dynamics Algorithms

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TLDR
Rigid Body Dynamics Algorithms presents the subject of computational rigid-body dynamics through the medium of spatial 6D vector notation to facilitate the implementation of dynamics algorithms on a computer: shorter, simpler code that is easier to write, understand and debug, with no loss of efficiency.
Abstract
Rigid Body Dynamics Algorithms presents the subject of computational rigid-body dynamics through the medium of spatial 6D vector notation. It explains how to model a rigid-body system and how to analyze it, and it presents the most comprehensive collection of the best rigid-bodydynamics algorithms to be found in a single source. The use of spatial vector notation greatly reduces the volume of algebra which allows systems to be described using fewer equations and fewer quantities. It also allows problems to be solved in fewer steps, and solutions to be expressed more succinctly. In addition algorithms are explained simply and clearly, and are expressed in a compact form. The use of spatial vector notation facilitates the implementation of dynamics algorithms on a computer: shorter, simpler code that is easier to write, understand and debug, with no loss of efficiency.

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Proceedings ArticleDOI

MuJoCo: A physics engine for model-based control

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STOMP: Stochastic trajectory optimization for motion planning

TL;DR: It is experimentally show that the stochastic nature of STOMP allows it to overcome local minima that gradient-based methods like CHOMP can get stuck in.
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Learning agile and dynamic motor skills for legged robots

TL;DR: In this paper, a method for training a neural network policy in simulation and transferring it to a state-of-the-art legged system is presented. But this method is limited to simulation and only few and comparably simple examples have been deployed on real systems.
Journal ArticleDOI

Centroidal dynamics of a humanoid robot

TL;DR: This paper studies the properties, structure and computation schemes for the centroidal momentum matrix (CMM), which projects the generalized velocities of a humanoid robot to its spatial centroidAl momentum, and introduces the new concept of “average spatial velocity” of the humanoid that encompasses both linear and angular components and results in a novel decomposition of the kinetic energy.
References
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A Mathematical Introduction to Robotic Manipulation

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Introduction to Robotics mechanics and Control

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TL;DR: This chapter discusses Jacobians: Velocities and Static Forces, Robot Programming Languages and Systems, and Manipulator Dynamics, which focuses on the role of Jacobians in the control of Manipulators.
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The Linear Complementarity Problem

TL;DR: In this article, the authors present an overview of existing and multiplicity of degree theory and propose pivoting methods and iterative methods for degree analysis, including sensitivity and stability analysis.
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