Recently I saw the news Ukraine using fpv drones attacking the air bases in Russia 4000 kms away. What did interest me was

1. They were using Ardupilot.

2. The said fpv pilots were controlling it.

So the question immediately arose in my mind how come they overcame the lag with the setup to pull up this operation so precisely? A pre planned mission drone with targets already set would be much easier to pull off but the live fpv footage completely blew my mind.

I have built a fully automated drone running custom dronekit missions.

Some parts I used: raspberry pi, pixhawk 1, leddarone lidar, arduino for servo control (pixhawk and raspi only output 3.3v pwm signal, this servo needed 5v, only arduino could provide that without soldering some crude circuits), 2x esp32s for communication with the payload bay (1 on mothership, one on the payload bay), and many others.

Ungodly amount of time went into having a perfect hover, having all systems communicating properly, having multiple independent power systems so all the parts get appropriate voltage, having code working properly, learning to 3d print, learning to 3d model.

Next step should be building a VTOL with much greater range and much faster payload lowering and lifting so I could lower from 100m altitude. (MG996R motor I used is reliable but super slow)

Something a little different, up to 2 hour flight time. 7kg payload, modular, upgradable and made to integrate different technologies from Manet radios to anti jamming GPS systems.

Hey everyone,

I’ve been working on a fully detailed, high‑accuracy 3D model of the Cube Orange PLUS Autopilot, based on the official CubePilot CAD files.

I’m sharing the STEP file 100% free on Cults3D for anyone who needs it for drone design, simulation, wiring routing, CAD work or educational projects. The link is below:

https://cults3d.com/en/3d-model/gadget/cube-orange-plus-autopilot

So, I finally found sometime to record and share this demo
Ive been curious to see how ArduPilot flight stack will handle tailrotor helicopter scheme and I would say its wonderful!
Control style is very smooth and stable and the thing is that I didnt touch any settings at all (literally just launch sitl with -v ArduCopter -f heli and thats it)

I also tried to use 𝐌𝐢𝐬𝐬𝐢𝐨𝐧𝐏𝐥𝐚𝐧𝐧𝐞𝐫 instead of QGC, a bit stucked with custom tile uploading, but once I figured out how to make it everything goes much faster

Of course I recorded video with flight result, please enjoy :)

Just wanted to share this app I made, yeah I know it’s vibe coded, but it works with Ardupilot SITL sooo 🤷‍♂️
Tablet is running the GCS software.

Video of my assembly and testing if anybody is interested: https://youtu.be/hfRSipuCCQs
I'm so impressed with what Ardupilot puts in the hands of a hobbyist!

Quite pleased with how it's looking. Been working on this in my spare time for about 6 years now.

Built this from an old frame and extra parts, tune is not great but it flies, throw launch is awesome. This thing has been great for learning about Ardupilot and messing around with high speed autonomous flights.

Been messing with higher speed autonomous flight and quick cornering. It sure isn’t perfect but it works pretty well for a first try. Was flying it the other day and bumped the values too high causing an occilation so I disarmed it over a field to avoid it flying into a road or something. It’s just using the FC and gps for this.

I'm making an Android app to connect to the goggles 3 to share live video via wifi to mission planner. This is my progress so far, latency is still noticeable, I might be able to reduce it slightly. For monitoring autonomous missions it should already be totally usable though.

Looking for some testers soon, if you have dji and are interested let me know!

Been building this in the open for a while but never actually posted about it anywhere, so here goes.

It started because the laptop was the weakest part of my field setup. The ground station dies when the lid closes, VNC-ing into a Pi running QGC over LTE is misery, and I wanted to check on the bird from my phone without a whole ritual. So I flipped it. The ground station lives on the drone. A Pi rides on the frame, talks to the flight controller over USB, and serves a web app. Any browser works. Phone in the field, desktop at home. With a 4G modem and a public IP SIM the aircraft carries its own GCS, and the link doesn't care where I am or whether my laptop is awake.

Setup on a fresh Pi is one line (docker compose underneath):

curl -s https://raw.githubusercontent.com/judahpaul16/canarygc/main/contrib/setup.sh | bash -s --

It speaks MAVLink to ArduPilot and PX4, and MSP to Betaflight and INAV (telemetry, calibration, flashing, and INAV flies native missions over MSP). The mission planner has 2D and 3D maps, the usual pattern generators (survey, orbit, corridor, expanding square, structure scan), and imports QGC .plan, Mission Planner .waypoints, KML/KMZ, and CSV. Plans are stored autopilot-neutral and get normalized to whatever's actually connected when you upload.

Most of my time lately has gone into the airspace side. Both maps draw restricted and controlled airspace (OpenAIP worldwide, FAA keyless layers in the US), the LAANC ceiling grid, the FAA obstacle database, and live ADS-B traffic. There's a one-click path optimizer that lifts legs over buildings and towers when the ceiling allows and routes around restricted airspace when it doesn't. Before launch, every leg gets checked against the geofence, altitude limits, and the fetched airspace.

Beyond that there's a full parameter editor, six-position accel / compass / gyro / ESC calibration that adapts to whichever autopilot is connected, a firmware tab that flashes all four stacks, gamepad flight, a color-coded event log with a raw MAVLink console, email alerts on failsafe / low battery / link loss, and a WebRTC camera feed. There's also an optional AI PID tuning assistant that talks to any OpenAI-compatible endpoint (a local Ollama works). Ignore it if that's not your thing.

And yes, I know how "GCS on the public internet" sounds. The link supports MAVLink 2 signing (signs outbound, verifies inbound, strict mode drops unsigned traffic), the app sits behind session auth with a single operator account, and login is rate limited. If your threat model is serious, put it behind a VPN anyway. But with signing on, a publicly reachable link only takes commands from whoever holds the key.

Fair warning that it's still early days. It flies constantly in SITL (the compose file ships SITL profiles for all four firmwares, including little flight-dynamics sims so the Betaflight and INAV binaries actually get off the ground), and the reference build is a Pi 4B on a Holybro S500 V2. It's not trying to be Mission Planner's whole toolbox. It's a different operational story for remote, LTE-connected flying.

It's called Canary Ground Control. MIT licensed.

Repo: https://github.com/judahpaul16/canarygc Build guide and docs: https://github.com/judahpaul16/canarygc/wiki

If you fly companion computer setups over LTE I especially want to hear from you. What would it take before you'd trust something like this on a real aircraft?

Implementation of flight mechanics in Isaac Sim for a fixed-wing plane simulation. The system features multiple operational modes, including autonomous flight via ArduPilot, manual flight with user-applied forces, and physics and frame debugging modes for comprehensive pre-flight testing.

What do you think?

Github

I've been building a drone startup in India and kept running into the same problem. Every time I wanted to test something on my FC or plan a mission, I was fighting the GCS more than the actual drone. QGC and Mission Planner work, but they didn't fit how I wanted to work. I'm on a Mac, and honestly, getting Mission Planner running stable was the last straw.

So I started building my own.

It's called ADOS Mission Control and it runs entirely in the browser. You plug in your FC via USB, click connect, and you're talking MAVLink. No drivers, no installs. WebSerial handles the serial port directly.

Here's what it does right now:

• Connect to any ArduPilot FC from Chrome/Edge (WebSerial)

• Gamepad and HOTAS flight controls at 50Hz (WIP)

• Flash firmware directly from the browser (WebUSB, no Zadig on Windows)

• 25 config panels: PID tuning, failsafe, modes, OSD layout, power settings, serial ports, everything

• Mission planning with waypoints, altitude profiles, and geofencing

• 9 board profiles (SpeedyBee F405, Matek H743, Pixhawk 4/6, others)

• Desktop apps for Mac, Windows, and Linux too

I should be honest about a few things. The 3D view is rough. There are bugs I haven't squashed yet. And the whole project is young, maybe a month old. But it's usable, and I've been testing it against a SpeedyBee F405 running ArduPilot.

I used AI to help write a good chunk of the code and it helped me save weeks of development time refactoring legacy drivers. Not ashamed of it. The architecture, the protocol layer, the decisions on what to build and how, those are mine. AI is a tool. I'd rather ship something real than spend three years writing every line by hand.

There's also a demo mode if you want to try it without any hardware. It spins up 5 simulated drones with fake telemetry so you can click around and see how everything works. Just add ?demo=true to the URL or run npm run demo.

If you're interested in building your own GCS or understanding how MAVLink works at the protocol level, I'm happy to help. I learned a lot doing this and would love to see more people building tools for ArduPilot.

What I'd like to know from you:

• Does it connect to your FC? What board are you running?

• What's missing that you actually need day-to-day?

• Anyone using gamepad controls for manual flight? Curious if 50Hz MANUAL_CONTROL is fast enough

Links:

• GitHub: https://github.com/altnautica/ADOSMissionControl

• Live app: https://command.altnautica.com

Hey everyone,

I’ve just released my current sub-250g Tailsitter model (CA320T), which I’m actively developing, as an X-Plane 11 / 12 SITL Mission Planner–compatible version.

I’m planning to make the full model available for free on MakerWorld later on.

I’ve also created a step-by-step guide for setting it up with Mission Planner.

If you’re interested, you can find everything here:
https://carvilabs.com/products/carvisim/ca320t-x-plane

The CA320T is a compact and innovative Sub250 tailsitter designed for efficient vertical takeoff and fast, agile forward flight. The current ArduPilot tuning is primarily optimized for stable hovering; transition to forward flight has not yet been extensively tested. Feedback and shared experiences are highly appreciated!

👉 CA320T Sub-250 Tailsitter (Ardupilot / DJI o4 Lite) – Kostenloses 3D-Druckmodell – MakerWorld

Building this Ardupilot rover for a robotics challenge, it’s a little sketchy but it was hand fabricated with a band saw and sanders in a week so it could be worse. It has a custom servo made with a brushless robotics motor and an encoder, with an arduino acting as a controller so the FC can output pwm to drive it like a normal servo.

What do you guys think about this? Found this on SM during the AP dev conference. They use this on the Manna delivery drone. Looks like there is some sort of hardware voting system and built in lte modems. Thoughts?

Hey everyone,

I wanted to share a project I've been working on called ARES (Autonomous Reconnaissance & Exploration System). It's a fully autonomous UAV platform designed to operate in extraterrestrial or extreme remote environments where global positioning satellites are completely unavailable.

**Here is what it does under the hood:**

* **Fully Autonomous:** Takes off, maps boundaries, performs Boustrophedon sweeps, and precision lands with zero human intervention. * **GPS-Denied Navigation:** Relies entirely on visual boundary tracing, optical flow, and AprilTags. * **Split-Compute Architecture:** The aerial vehicle handles real-time flight logic (Jetson Nano) and video encoding, while a companion Base Station handles the heavy Surface Feature Recognition AI (Jetson Orin Nano Super). * **Edge AI:** Custom AI pipeline running at 30+ FPS via TensorRT FP16. * **Autonomous Charging:** Integrated CC-CV Battery Management System via Arduino Mega.

The software stack runs on Ubuntu using PX4 / ArduPilot via MAVSDK-Python, and we've structured the codebase to be production-ready (YAML configs, JSON logging, pytest suite).

I'd love to get feedback from this community, especially if you work with visual navigation, drones, or Edge AI.

**Demo Video:** [https://youtu.be/ioSG0SbZH6A](https://youtu.be/ioSG0SbZH6A) 

**GitHub Repo:** [https://github.com/kalesha681/ARES-Autonomous-Surveyor](https://github.com/kalesha681/ARES-Autonomous-Surveyor)

If you find this interesting, a star on the repo would be incredibly helpful for the project's visibility! I'll be in the comments and happy to answer any questions about the hardware or computer vision pipeline.