<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>SUAS 2025 on UAS2030</title><link>https://uas2030.github.io/docs/suas2025/</link><description>Recent content in SUAS 2025 on UAS2030</description><generator>Hugo</generator><language>en</language><copyright>Copyright (c) 2020-2024 Thulite</copyright><lastBuildDate>Thu, 07 Sep 2023 16:13:18 +0200</lastBuildDate><atom:link href="https://uas2030.github.io/docs/suas2025/index.xml" rel="self" type="application/rss+xml"/><item><title>Strategy</title><link>https://uas2030.github.io/docs/suas2025/strategy/</link><pubDate>Thu, 07 Sep 2023 16:13:18 +0200</pubDate><guid>https://uas2030.github.io/docs/suas2025/strategy/</guid><description>This document outlines the design, development, and strategic approach of UAS2030 for the SUAS 2025 competition. Building upon our experiences from SUAS 2024, we have refined our Unmanned Aerial System (UAS) to meet and exceed the updated requirements set forth in the SUAS 2025 Team Handbook.</description></item><item><title>Object Detection, Localisation, and Classification</title><link>https://uas2030.github.io/docs/suas2025/object-detection-localisation-and-classification/</link><pubDate>Thu, 07 Sep 2023 16:13:18 +0200</pubDate><guid>https://uas2030.github.io/docs/suas2025/object-detection-localisation-and-classification/</guid><description>The Object Detection, Localisation, and Classification (ODLC) subsystem is built around neural network pipeline optimised for real-time performance and accuracy. The pipeline begins with a YOLOv11 model tasked with initial target detection and classification.</description></item><item><title>Airframe &amp; Payload</title><link>https://uas2030.github.io/docs/suas2025/airframe-payload/</link><pubDate>Thu, 07 Sep 2023 16:13:18 +0200</pubDate><guid>https://uas2030.github.io/docs/suas2025/airframe-payload/</guid><description>Air Drop Design and Testing The air drop system is designed to ensure consistent, safe, and accurate delivery of payloads using a lightweight, efficient mechanism during autonomous missions.</description></item><item><title>Autopilot &amp; Flight Control</title><link>https://uas2030.github.io/docs/suas2025/autopilot-flight-control/</link><pubDate>Thu, 07 Sep 2023 16:13:18 +0200</pubDate><guid>https://uas2030.github.io/docs/suas2025/autopilot-flight-control/</guid><description>The flight control system of our hexacopter is centered around the Cube Orange+ flight controller running ArduPilot firmware version 4.4. This controller manages all core flight operations including stabilization, navigation, and mode transitions.</description></item><item><title>Obstacle Avoidance</title><link>https://uas2030.github.io/docs/suas2025/obstacle-avoidance/</link><pubDate>Thu, 07 Sep 2023 16:13:18 +0200</pubDate><guid>https://uas2030.github.io/docs/suas2025/obstacle-avoidance/</guid><description>Our UAV is equipped with the RPLidar A2 sensor;, a lightweight and fast-scanning LiDAR designed specifically for aerial obstacle avoidance applications.</description></item><item><title>Alternatives Considered</title><link>https://uas2030.github.io/docs/suas2025/alternatives-considered/</link><pubDate>Thu, 07 Sep 2023 16:13:18 +0200</pubDate><guid>https://uas2030.github.io/docs/suas2025/alternatives-considered/</guid><description>We considered multirotor, VTOL, and fixed-wing configurations. Fixed-wing designs were rejected due to limited drop accuracy and ODLC image quality. At the same time, VTOLs were dismissed because of their large turning radius at high speeds and complex mode transitions.</description></item><item><title>Proof of Flight</title><link>https://uas2030.github.io/docs/suas2025/proof-of-flight/</link><pubDate>Thu, 07 Sep 2023 16:13:18 +0200</pubDate><guid>https://uas2030.github.io/docs/suas2025/proof-of-flight/</guid><description> Download the WEBM or MP4 video. Manual flight for 1000ft</description></item></channel></rss>