<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Sampling-based Algorithms for Optimal Motion Planning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1105.1186/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1105.1186/feed.xml" rel="self" type="application/rss+xml" /><item><title>Sampling-based Algorithms for Optimal Motion Planning — mentioned by antegamisou</title><link>https://news.ycombinator.com/item?id=35446727</link><guid isPermaLink="false">urn:hackerpapers:mention:1105.1186:35446727</guid><pubDate>Tue, 04 Apr 2023 22:31:07 GMT</pubDate><description>&lt;p&gt;This reply will focus mainly on the academics. Since you&amp;#x27;ve mentioned exoskeletons, knowledge of kinematics and dynamics is imperative. Rotation Matrices, Forward/Inverse Kinematics, Denavit - Hartenberg Parameters, Lagrangian Mechanics are a few fundamental concepts one should be familiar with. Their applications mostly pertain to robotic manipulators (arms), which are what members of exoskeleton&amp;#x27;s are modeled after. They&amp;#x27;re covered extensively in the classic in the field textbook Robotics Modelling, Planning and Control by Siciliano, Sciavicco, Villani, Oriolo…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1105.1186/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.RO</category></item></channel></rss>
