Kinematics and Dynamics Modeling of 7 Degrees of Freedom Human Lower Limb Using Dual Quaternions Algebra
- URL: http://arxiv.org/abs/2302.11605v2
- Date: Thu, 13 Jun 2024 14:27:59 GMT
- Title: Kinematics and Dynamics Modeling of 7 Degrees of Freedom Human Lower Limb Using Dual Quaternions Algebra
- Authors: Zineb Benhmidouch, Saad Moufid, Aissam Ait Omar,
- Abstract summary: This paper exploits dual quaternions theory to provide a fast and accurate solution to the forward, inverse kinematics and Newton-Euler dynamics algorithm.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Denavit and Hartenberg based methods as Cardan, Fick and Euler angles describe the position and orientation of an end-effector in Three Dimensional (3D) space. However, the generation of unrealistic human posture in joint space constitutes the weak point to these methods because they impose a well-defined rotations order. A method to handle the transformation homogeneous performance uses the dual quaternions. Quaternions have proven themselves in many fields as providing a computational efficient method to represent a rotation, and yet, they can not deal with the translations in 3D-space. The dual numbers can extend quaternions to dual quaternions. This paper exploits dual quaternions theory to provide a fast and accurate solution to the forward, inverse kinematics and recursive Newton-Euler dynamics algorithm for 7 Degree of Freedom (DOF) human lower limb in 3D-space.
Related papers
- SC4D: Sparse-Controlled Video-to-4D Generation and Motion Transfer [57.506654943449796]
We propose an efficient, sparse-controlled video-to-4D framework named SC4D that decouples motion and appearance.
Our method surpasses existing methods in both quality and efficiency.
We devise a novel application that seamlessly transfers motion onto a diverse array of 4D entities.
arXiv Detail & Related papers (2024-04-04T18:05:18Z) - Measuring the Discrepancy between 3D Geometric Models using Directional
Distance Fields [98.15456815880911]
We propose DirDist, an efficient, effective, robust, and differentiable distance metric for 3D geometry data.
As a generic distance metric, DirDist has the potential to advance the field of 3D geometric modeling.
arXiv Detail & Related papers (2024-01-18T05:31:53Z) - Dual Quaternion Rotational and Translational Equivariance in 3D Rigid
Motion Modelling [6.130606305848124]
We propose a dual quaternion representation of rigid motions in the 3D space that jointly describes rotations and translations of point sets.
Our approach is translation and rotation equivariant, so it does not suffer from shifts in the data.
Models endowed with this formulation outperform previous approaches in a human pose forecasting application.
arXiv Detail & Related papers (2023-10-11T16:06:14Z) - Geometry-Informed Neural Operator for Large-Scale 3D PDEs [76.06115572844882]
We propose the geometry-informed neural operator (GINO) to learn the solution operator of large-scale partial differential equations.
We successfully trained GINO to predict the pressure on car surfaces using only five hundred data points.
arXiv Detail & Related papers (2023-09-01T16:59:21Z) - MoDA: Modeling Deformable 3D Objects from Casual Videos [84.29654142118018]
We propose neural dual quaternion blend skinning (NeuDBS) to achieve 3D point deformation without skin-collapsing artifacts.
In the endeavor to register 2D pixels across different frames, we establish a correspondence between canonical feature embeddings that encodes 3D points within the canonical space.
Our approach can reconstruct 3D models for humans and animals with better qualitative and quantitative performance than state-of-the-art methods.
arXiv Detail & Related papers (2023-04-17T13:49:04Z) - TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis [19.322295753674844]
We present a learnable descriptor invariant under 3D rotations and reflections, i.e., the O(3) actions.
We propose an embedding of the 3D spherical neurons into 4D vector neurons, which leverages end-to-end training of the model.
Our results reveal the practical value of steerable 3D spherical neurons for learning in 3D Euclidean space.
arXiv Detail & Related papers (2022-11-26T02:15:35Z) - Exploring the Adjugate Matrix Approach to Quaternion Pose Extraction [0.0]
Quaternions are important for a wide variety of rotation-related problems in computer graphics, machine vision, and robotics.
We study the nontrivial geometry of the relationship between quaternions and rotation matrices by exploiting the adjugate matrix of the characteristic equation of a related eigenvalue problem.
We find an exact solution to the 3D orthographic least squares pose extraction problem, and apply it successfully also to the perspective pose extraction problem with results that improve on existing methods.
arXiv Detail & Related papers (2022-05-17T23:20:55Z) - Fully Steerable 3D Spherical Neurons [14.86655504533083]
We propose a steerable feed-forward learning-based approach that consists of spherical decision surfaces and operates on point clouds.
Due to the inherent geometric 3D structure of our theory, we derive a 3D steerability constraint for its atomic parts.
We show how the model parameters are fully steerable at inference time.
arXiv Detail & Related papers (2021-06-02T16:30:02Z) - Feature Disentanglement in generating three-dimensional structure from
two-dimensional slice with sliceGAN [35.3148116010546]
sliceGAN proposed a new way of using the generative adversarial network (GAN) to capture the micro-structural characteristics of a two-dimensional (2D) slice.
We combine sliceGAN with AdaIN to endow the model with the ability to disentangle the features and control the synthesis.
arXiv Detail & Related papers (2021-05-01T08:29:33Z) - Fast Gravitational Approach for Rigid Point Set Registration with
Ordinary Differential Equations [79.71184760864507]
This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA)
In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field.
We show that the new method class has characteristics not found in previous alignment methods.
arXiv Detail & Related papers (2020-09-28T15:05:39Z) - Quaternion Equivariant Capsule Networks for 3D Point Clouds [58.566467950463306]
We present a 3D capsule module for processing point clouds that is equivariant to 3D rotations and translations.
We connect dynamic routing between capsules to the well-known Weiszfeld algorithm.
Based on our operator, we build a capsule network that disentangles geometry from pose.
arXiv Detail & Related papers (2019-12-27T13:51:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.