Dual Quaternion Quintic Blends (DQQB) Algorithm

Overview

This repository contains the implementation of the Dual Quaternion Quintic Blends (DQQB) algorithm, as detailed in our paper: "Dual Quaternion Quintic Blends: $\mathcal{C}^2$-Continuous, Time-Optimized Interpolation of Unit Dual Quaternions" (currently under review). The DQQB algorithm is a novel method for interpolating unit dual quaternions, offering $\mathcal{C}^2$-continuous trajectories, ideal for robotic applications requiring high precision and smooth motion.

Features

• Implementation of the DQQB algorithm for trajectory planning.
• Examples demonstrating the use of the algorithm in various scenarios.
• Tools for evaluating and visualizing the trajectories.
• Comprehensive documentation for ease of use and customization.

Examples

Two examples are given, the first one shows the execution of the trajectory depicted in the paper:

example_1.1.mov

The second video gives a small demonstration of a welding trajectory, which was executed by a LARA8 from Neura Robotics GmbH:

example_2.1.mov

Installation

Instructions to set up the environment and install necessary dependencies.

The quaternion and dual quaternion maths can be found in the package neura_dual_quaternions

pip install neura-dual-quaternions

For interactive plots, the packages jupyter-matplotlib and jupyter-widgets are needed.

pip install ipympl

pip install ipywidgets

Other site-packages:

pip install numpy

pip install matplotlib

Contact

For questions, please contact Jens Temminghoff at [email protected].

Authors

• Jens Temminghoff
• Marcel Huptych
• Jan Wiartalla
• Markus Schmitz
• Burkhard Corves
• Mathias Hüsing