<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>RRT Algorithm on Rishika Bera</title><link>https://rishika2024.github.io/tags/rrt-algorithm/</link><description>Recent content in RRT Algorithm on Rishika Bera</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>berarishika@gmail.com (Rishika Bera)</managingEditor><webMaster>berarishika@gmail.com (Rishika Bera)</webMaster><copyright>Rishika Bera</copyright><lastBuildDate>Thu, 11 Sep 2025 14:15:05 +0700</lastBuildDate><atom:link href="https://rishika2024.github.io/tags/rrt-algorithm/index.xml" rel="self" type="application/rss+xml"/><item><title>RRT for 2D Path Planning</title><link>https://rishika2024.github.io/posts/rrt/</link><pubDate>Thu, 11 Sep 2025 14:15:05 +0700</pubDate><author>berarishika@gmail.com (Rishika Bera)</author><guid>https://rishika2024.github.io/posts/rrt/</guid><description>&lt;p&gt;An RRT is a set of vertices (configurations) and edges (connections between configurations). Each iteration samples a random point in the domain, finds the nearest existing vertex, and takes a small step of size \(\Delta\) toward the sample. If you run it for long enough, you get close to uniform coverage over the whole space.&lt;/p&gt;</description></item></channel></rss>