<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Safe-Navigation on Omanshu Thapliyal</title><link>https://omanshuthapliyal.github.io/tags/safe-navigation/</link><description>Recent content in Safe-Navigation on Omanshu Thapliyal</description><generator>Hugo</generator><language>en</language><atom:link href="https://omanshuthapliyal.github.io/tags/safe-navigation/index.xml" rel="self" type="application/rss+xml"/><item><title>Unmanned Aerial Vehicle Safe Autonomous Operations</title><link>https://omanshuthapliyal.github.io/projects/unmanned-aerial-vehicle-safe-autonomous-operations/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://omanshuthapliyal.github.io/projects/unmanned-aerial-vehicle-safe-autonomous-operations/</guid><description>&lt;h2 id="problem">Problem&lt;/h2>
&lt;p>Air taxis and delivery drones are expected to fly autonomously over cities,
but public trust is low: in one survey cited in this work, only 26% of
respondents trusted autonomous air taxis. Before these vehicles can fly on
their own, their controllers need a clear safety guarantee: the set of states
from which the vehicle can still avoid a collision or reach its landing pad.
Computing that set means solving a Hamilton-Jacobi equation, and grid-based
solvers become impractical for realistic vehicle models with 6 to 12 states.
Learning-based controllers scale better, but they are hard to certify.&lt;/p></description></item></channel></rss>