<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>modelling on Matthew Shields</title>
    <link>https://mshields.name/tags/modelling/</link>
    <description>Recent content in modelling on Matthew Shields</description>
    <generator>Hugo -- gohugo.io</generator>
    <copyright>© 2022 - 2026 Matthew Shields</copyright>
    <lastBuildDate>Fri, 10 Jun 2022 00:00:00 +0000</lastBuildDate><atom:link href="https://mshields.name/tags/modelling/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>UK Climate Change</title>
      <link>https://mshields.name/blog/2022-06-10-uk-climate-change/</link>
      <pubDate>Fri, 10 Jun 2022 00:00:00 +0000</pubDate>
      
      <guid>https://mshields.name/blog/2022-06-10-uk-climate-change/</guid>
      <description>Preamble I found this on NASA’s climate site and my curiosity was piqued.
Temperature anomaly, how did they get to that?
Anomaly versus what?
Surely, it’s against where the temperature should be, but how do you know where the temperature should be?
What effects do you account for and what do you leave out?
I thought I’d have a stab at how they came up with these numbers, but I wanted to do it for UK data, specifically.</description>
    </item>
    
    <item>
      <title>Ground Vehicle Centre of Mass Optimisation</title>
      <link>https://mshields.name/blog/2022-04-16-ground-vehicle-centre-of-mass-optimisation/</link>
      <pubDate>Sat, 16 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://mshields.name/blog/2022-04-16-ground-vehicle-centre-of-mass-optimisation/</guid>
      <description>Preamble In a previous post on Weight Distribution in Mobile Ground Vehicles I provided a model for determining the forces able to be generated by a ground vehicle, which ultimately will be a limiting factor in performance. I also provided a method for establishing the centre of mass location of a ground vehicle, providing that you have one to start with.
If you are at an earlier stage in design you may not have a ground vehicle to analyse and want to get a good estimate for the centre of mass location prior to manufacturing your first prototype.</description>
    </item>
    
    <item>
      <title>Weight Distribution in Mobile Ground Vehicles</title>
      <link>https://mshields.name/blog/2022-03-10-weight-distribution-in-prototype-mobile-robots-for-outdoor-use/</link>
      <pubDate>Sun, 03 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://mshields.name/blog/2022-03-10-weight-distribution-in-prototype-mobile-robots-for-outdoor-use/</guid>
      <description>Preamble A ground vehicle’s motion is generally only ever limited by one of two things, torque or friction. There are many ways to try and maximise both of these. This post will look to address friction by considering the normal forces at the contact patches between the wheels and the ground.
When designing a mobile ground vehicle, weight distribution is one area that can be optimised fairly freely to provide large benefits.</description>
    </item>
    
    <item>
      <title>Registration Methods Comparison</title>
      <link>https://mshields.name/blog/2022-03-10-registration-methods-comparison/</link>
      <pubDate>Thu, 24 Mar 2022 00:00:00 +0000</pubDate>
      
      <guid>https://mshields.name/blog/2022-03-10-registration-methods-comparison/</guid>
      <description>Preamble Registration is the technique of aligning two data sets by finding either a rigid or affine transformation, depending on the problem. In robotics this is often between two pointclouds but the technique can be useful for aligning any data sets especially those that contain spatial data, MRI scan images for example. The Wikipedia page for this topic is quite concise and well written.
Background Typically we represent transformations in 3D space as a matrix and translation vector that we apply to an input vector of coordinates to produce a transformed output vector of shifted coordinates.</description>
    </item>
    
    <item>
      <title>Extracting Road Markings from Pointcloud Data</title>
      <link>https://mshields.name/blog/2022-02-23-extracting-road-markings-from-pointcloud-data/</link>
      <pubDate>Sat, 26 Feb 2022 00:00:00 +0000</pubDate>
      
      <guid>https://mshields.name/blog/2022-02-23-extracting-road-markings-from-pointcloud-data/</guid>
      <description>Preamble This is the first post in a series looking back at past projects I have done but not shared publicly or documented in any way.
This was a piece of work from 2019 with the goal of extracting road markings from geo-referenced pointclouds. For those that don’t know, a geo-referenced pointcloud is created by taking a LiDAR and putting it on some kind of a rover vehicle, then taking all the observations of the LiDAR that were taken in the moving vehicle reference frame and converting them into the global “static” reference frame.</description>
    </item>
    
  </channel>
</rss>
