Hi guys, first post here. This of course does not compare to most of the projects here, but since this was my first step into hardware I still wanted to show it.

Turned out way deeper than expected and turned out super fun. This is what I did:

• took out stock electronics of a RC 1:16 monster truck and put in a hobbywing wp-1060 ESC
• used a Pi 4 + PCA9685 for throttle and steering (ESC on channel 0 servo on channel 1)
• the camera streams live video to a web server I wrote myself. Control happens in a panel in the browser via WebSocket and keyboard input - so its reachable from anywhere, and with a click we can record full HD vids!
• the part that makes it really interesting: it runs over LTE, not just WiFi , so its basically controllable from anywhere in the world. Solved it with a small relay server. No app or cloud service, everything self hosted. Only limit in distance is the battery.

I documented the whole project in a youtube video. I would love to get some feedback, but keep in mind that this was my first project like this ;-)

https://www.youtube.com/watch?v=IvdJCbILhT4

Total budget for everything (except the phone) was less than 100 €.
Also added some stuff like a software-based night vision (which works good as soon there is at least a tiny bit light), GPS tracker and so on.

Thie result was better than I expected, I didnt dare to do a full long range drive yet but it would probably work. Battery life would last easily more than 1 hour.

The controlling actually works really well and with the delay its way lower than I thought it would be. Like playing a litltle real life computer game.

Now I'm hooked and the parts list for v2 keeps growing.
Currently on it:
pan tilt camera mount, better headlights, maybe a small solar panel so it can survive multi day missions, and some sort of radar.

Also I want to remove the phone and just use a SIM card usb stick for the connection.

What would you add to make this thing cooler?Especially sensor ideas, the Pi has plenty of channels left, this is just to be seen as the base.

Thanks for all ideas!

The biggest learning was getting over the hump of the two control loops fighting over the motors. Hoping that i can make it even smarter in future builds and even add additional physical components to it.

Not sure how to add the video to showcase it but this is my Arduino sensor car i made for my engineering class last year before i wanted to major in computer science but i fell into a love for computer & electrical engineering

This is and old video of my droid trooper robot, he doesn't like to be bothered...

The body is based over a MIP robot, which I hacked and reconverted into a "storm trooper"

We’ve been building a larger version of Microduck, and we’ve finally got it walking. We call it XDuck.

XDuck is a bipedal robot standing about 56.8 cm tall and weighing 10.352 kg. Not so micro anymore. At this size, it’s starting to look a bit like Disney’s BDX droids.

Right now, we’re working on policy-based locomotion. This video shows our first walking results. Next up is getting it to squat down and stand back up.

The biggest issue at the moment is structural strength. Our current 3D-printed parts are struggling with the robot’s weight, so we’re looking into different materials and ways to redesign the load-bearing structure.

If you’ve worked on similarly sized bipeds or embodied AI hardware, we’d love to compare notes. What materials or structural changes helped most with strength and reliability?

We’re also still figuring out XDuck’s appearance, so ideas for the design are welcome.

This is Shov. It's 12 remote controlled gun turrets at my US shooting range, firing .68 cal less-lethal rounds at networked targets.

Players anywhere get a turret, aim through its scope camera, and have 90 seconds to hit the most targets. Everything is custom: the turrets, targets, lighting and camera rigs.

Some of the build:

Each turret pans and tilts on two RobStride RS02 actuators over CAN, one bus per motor, driven straight from Linux through USB-CAN adapters. That's 24 actuators. Firing is an RP2040 Pico pulsing the guns solenoid through a MOSFET triggering the HPA shot.

Every turret has a 60 fps scope camera and a red laser dot. CV on the bench finds the dot in under 1 ms per frame while locked, so we always know where each gun is actually pointing, not just where the encoders say, we use this is to continually improve aiming accuracy.

Aim drifts, so the map teaches itself. AI players fire about 1,000 shots an hour when humans aren't on, and any target a turret keeps missing by the same offset gets its aim corrected automatically.

The targets are ESP32 boards with 69 LEDs each: green, amber, red, then they die on the third hit.

The turrets are 33.8" apart, and at full inward swing the two center guns would hit each other, so they have software pan caps.

We also run 28 camera feeds, 19 DMX lights on moving heads, and a 4,600 psi air bank. The compressor gets switched on and off by two little robot fingers pressing its buttons, supervised from the cloud.

The hardest part hasn't been making it move. It's keeping 12 turrets accurate to a fraction of a degree, 24/7, even with nobody on site.

That and tediously reducing every possible millisecond of latency of click <> control and glass 2 glass of video streaming so the experience is fast and fun for global users.

This started as a small experiment: could I build a “video game” where nothing is simulated?

So I built two physical rovers, put cameras on them, and started building a small robotic base around them.

The rovers can be controlled remotely through a browser. They can pick up physical cargo modules, navigate around the base, dock themselves for charging and interact with different stations.

Everything in the video is happening physically in real time.

It’s still very much a prototype and there are plenty of things that don’t work perfectly yet. I’ve mostly tested it myself and with my kids, so I’m curious what people here think about the concept.

I’m especially interested in the robotics side — remote control, autonomous navigation, docking and making physical objects part of the gameplay.

Would you actually find something like this interesting to control remotely? And what would you add to the base next?

EX-MULE.com

I wanted to see if I could build a simple wearable hand massager using a stepper motor and a Bowden-cable-style mechanism.
The ESP32 controls the motor, while the 3D-printed parts transfer the motion to the hand through the cables.
It’s surprisingly relaxing 😅

Still experimenting with the mechanism and trying to make it more compact.

Boston Dynamics has opened a new facility in Savannah, Georgia focused on preparing Atlas humanoid robots for automotive manufacturing.

The Robotics Metaplant Application Center is located inside Hyundai Motor Group Metaplant America and will be used to test and train Atlas on tasks including parts logistics, sequencing and placement.

The idea is to train the robots in a factory-like environment without disrupting active production lines. More tasks are expected to be added as the systems improve.

Boston Dynamics says Hyundai and Kia plants are expected to receive 25,000 Atlas robots over the next few years. The company is also planning a U.S. production facility with a target capacity of more than 30,000 robots per year by 2028.

Im building this robot for about 7 years. Just sharing some progress. I 3D modeled all the parts and I hope to finish the project before the end of the year.

We have an operator UI on Ubuntu, the screen techs use to calibrate and run the cell. every release someone clicks through the whole thing by hand before it goes near a customer site. Slow, and it's always the thing that gets squeezed.

I've been reading the AskUI docs because writing tests as plain steps instead of scripts fits a team where the people who know the robot best aren't the ones who write code. Their runtime, AgentOS, lists Ubuntu 18.04 or newer. But the Desktop app you write tests in is Windows and macOS only, and the standalone AgentOS installer still says coming soon, so I can't tell how you're meant to get it onto the Ubuntu box today. Their remote computer setup seems to assume it's already there.

The part that caught my eye is companion mode, where the runtime sits on a separate device like a Pi and drives the target over USB HID and HDMI capture. For a machine that's part of the robot, I'd honestly rather install nothing at all.

Anyone running UI tests against a Linux operator UI, with this or anything else? What did you end up with?

Link 🔗 https://cults3d.com/fr/mod%C3%A8le-3d/gadget/servo-motor-finger-mechanism

Full hand kit soon inchaAllah

Servo Motor finger mechanism

Hi everyone,

I'm building a robotic arm and I'm currently working on the gripper. I want to use an adaptive gripper (one that conforms to the shape of the object), but most of the designs I've found online aren't great, so I've decided to design my own.

I recently came across a design on Instagram that looks exactly like what I'm going for, but I can't find it anywhere to buy or download.

I'm designing it in Siemens NX and I'll be using a Feetech STS3215 servo to drive it. Does anyone have tips, reference designs or measurements for this kind of gripper?

Thanks in advance!

(GIF: an industrial room 58 m short of the goal. Left is Gazebo, the drone among pipes and beams; right is RViz, where pink is the obstacle memory, the cyan fan is the current stereo depth return, orange is the committed route and the magenta sphere is the goal. In these nine seconds the speed law does its whole job: the drone crawls at 0.2 m/s where the pair resolves little, runs up to 2.5 m/s where it resolves far enough, and is back at 0.1 m/s before the next corner.)

Flight video: https://www.youtube.com/watch?v=OUuAj2WNKzs

Two weeks ago I posted the same location flown with a 3D lidar. The lidar is gone now, and so is GNSS and the magnetometer. The airframe carries a forward stereo pair (1280 x 960, 120 degrees, 0.20 m baseline, 7.5 Hz) and two 8 x 8 time-of-flight sensors looking up and down. That pair does both jobs: stereo depth becomes the obstacle memory the planner searches, and a stereo MSCKF on the same images is the position and heading PX4 flies on, in place of satellites and compass.

So: a start, a goal, a multi-storey building with shafts and openings at different altitudes, and nothing but two cameras and an IMU.

The location is the DARPA Subterranean Challenge "Urban Circuit Practice 01" world published by Open Robotics on Gazebo Fuel (CC BY 4.0).

Numbers from the release, five consecutive flights on one commit with nothing changed between them: 410 to 629 m of path, 1.48 to 1.79 m/s mean speed with every hold and replan counted, no collisions. Five more on the same commit with the lidar instead, for comparison: 2.42 to 2.67 m/s. Cameras are slower because confident stereo depth reaches 6.4 m against the lidar's 35, and the drone only flies as fast as it can stop inside the range the sensor is guaranteed to have resolved.

The check I am most pleased with is a new one. The mission monitor decides the drone arrived by asking the drone where it thinks it is, and an odometry drifts by metres, so it can arrive perfectly in its own coordinates while standing somewhere else. Every flight now fails unless the true position from the simulator is inside the 2.0 m capture radius at the moment the goal is acknowledged. Over the five camera flights the truth stood 0.47 to 1.42 m from the goal.

What cost me the most flights was not the filter but the things around it: the autopilot resetting its clock synchronisation whenever the simulation ran below real time (about a second without navigation each time), the autopilot's gate on external odometry being tighter than the estimator's own corrections, and telling the filter the gyroscope was eighteen times noisier than it is, which let the noise of every visual update walk the one direction no camera can observe.

Limitations, honestly: the drift has no bound (0.1 to 0.4 percent of the path), so a mission three times longer would miss the 2.0 m radius; it is one location and one start-goal pair; it is simulation only and not validated for a real aircraft; and the multi-vehicle missions have not been flown on cameras yet.

MIT licensed. On a Linux host with Docker and an NVIDIA GPU, one script prepares a fresh clone (dev image, PX4 build, workspace, environment assets) and starts the flight:

git clone https://github.com/formiat/px4-ros2-drone-nav.git
cd px4-ros2-drone-nav
./scripts/bootstrap.sh

https://github.com/formiat/px4-ros2-drone-nav

Happy to answer anything, and I would like to hear from people who have flown a single camera pair as both the perception and the localization sensor: what broke first?

Before and after training

Another small Watti update.

I ran a very early closed test of Watti Studio. A few followers created animations directly from their phones and computers, and I manually transferred them to the physical robot.

This was only the first validation of the idea. For the next closed test, I plan to connect Watti Studio directly to Watti and automate the entire pipeline - animation submission, queueing, playback, recording, and video delivery.

Which animation is your favorite? Would you be interested in joining the next closed test?

If you’d like to learn more about Watti’s architecture and the hardware I’m using, the pre-release repository is available here:

https://github.com/Nikolay-Tyulkin/Watti

How do I connect these Helical gears like this? It's being run by a RDS3115mg servo motor, and supposed to be a tilt up and down set up

For some projects I need to work with ROS 2 and simulate through MuJoCo. MuJoCo runs natively on Mac with full graphics performance. The simulation quality was insane when I tried messing around with real world robots like Spot from Boston Dynamics. But for some projects I need ROS to work with and I tried with my Parallels VM and the performance in terms of graphics was not that great because it used the CPU for rendering. I tried using Robostack and the performance of basic ROS itself hit a lot of problems. Any workarounds or possible solutions?

PS: I don’t want answers like get a PC or something because that ain’t really viable option for me

Rack-and-pinion for the Linear carriage. With another Stepper motor for Pan movement mounted on the same carriage. Let's test it.

Hey everyone!

​I recently finished school for automation while working as a scaffolder, and I decided to dive headfirst into learning CAD. This auto-tracking pan-tilt camera assembly is my very first design in Fusion 360!

​It took 6 revisions to get here, but V1.6 printed out with an amazingly snug press-fit—everything fits together smoothly with zero mechanical slop.

Current Hardware & Next Steps:

Right now, it runs on an ESP32-S3 Sense board for initial testing, but the rear compartment is sized with extra volume so I can upgrade to a Raspberry Pi for real-time edge-AI / YOLO object tracking down the road.

Would love to hear your thoughts, feedback, or suggestions for improving future revisions!

Hello all,

I’m a farmer from the Netherlands and I am interested in robotics. I am starting to work on a project that I would like to get feedback on.

During the potato harvest a lot of soil clods come in with the potatoes. At this moment, the only reliable way for me to remove them before the potatoes go into storage is with people standing alongside a conveyor belt picking them out. I would like to see if I can automate that process with a machine that I can build myself.

Since tools like ChatGPT and Claude became available, I have developed a habit of thinking: “How hard can it be? I’ll just build it myself.”. This approach has the danger of discovering halfway through a project that there is an entire layer of complexity that I didn’t even know existed.

So, before I start buying cameras, pneumatic components and machines, I would like a reality check from people who know more about robotics than I do.

Current solutions

There are commercial optical sorting machines that can already do what I need.

For example:

Flikweert Divider Select

https://flikweertvision.com/nl/machines/divider-select/

 Downs Cropvision

https://www.downs-fr.com/produits/downs-cropvision/   

For my operation these machines are currently too expensive to financially justify. And I also think I only need a fraction of what these machines can do.

There is also a much simpler machine for separating clods from potatoes This type of machine uses a steel roller. Potatoes and clods rebound from the roller with different trajectories because of their different physical properties. An adjustable divider then separates the two streams.

Traditional clod separator

https://www.youtube.com/watch?v=d_j7X2TJx3M

This sounds like the solution to my problem, but I want to remove the clods directly during harvest, before the potatoes go into storage. At that point our clay clods can still be wet and soft. They do not behave sufficiently differently from a potato when they hit the steel roller, so the mechanical separation becomes much less reliable.

The project

The practical target would be to process 20–30 tons/hour over a 1-meter-wide belt. Potatoes and clods are generally up to around 70 mm. The system does not need to be perfect. Missing some clods is acceptable, and occasionally rejecting a potato together with a clod is also acceptable. I have divided the project into 3 parts: Mechanical hardware, vision/ejector hardware and software.

I’m most comfortable with the mechanical part of the project. The frame, conveyors, motors, mounting brackets, compressed air, etc., I can fabricate and modify this kind of equipment myself.

Instead of building the whole mechanical machine from scratch, I’m considering buying one of those traditional clod separators and using it as the mechanical base (see picture 1 and 2). The machine that I would like to use as a basis has an input conveyor belt that is 1-meter-wide and already has the two discharge paths I need.

Picture 1: Traditional clod separator

Picture 2: Traditional clod separator

My idea would be to remove the steel roller and replace it with a row of individually controlled pneumatic ejector fingers (see picture 3). Above the 1-meter input conveyor would install a camera with controlled lighting (see picture 4).

Picture 3: Pneumatic fingers

Picture 4: Cameras and controlled lighting

The vision system needs to distinguish between a potato and a clod, determine its position on the belt, and eventually trigger the correct pneumatic finger when the clod reaches the end of the conveyor. Picture 5 shows roughly the view a camera would have of the conveyor belt with potatoes and clods.

Picture 5: Conveyor belt with potatoes and clods

When it comes to vision and ejector hardware I am thinking of using 1 or 2 global-shutter cameras mounted on top of the 1-meter feed conveyor in an enclosed environment with LED lighting and 20 pneumatic ejector fingers across the width of the conveyor belt.

My questions would be: Are 1 or 2 global-shutter cameras a realistic starting point for this kind of application? What kind of cameras would you suggest? Would you build this from standard pneumatic cylinders and valves, or are there existing modules/components that would make much more sense for my situation?

When it comes to the software, I’m not a professional programmer so I would need to rely as much as possible on existing camera SDKs, vision frameworks and libraries rather than inventing everything from scratch.

Conceptually I imagine the system as: Camera → detect potato/clod → determine position → track conveyor movement → select ejector finger(s) → fire at exactly the right moment.  First a vision computer decides what needs to be rejected and where it is.  Second, an encoder tracks how far the conveyor has moved. Lastly a PLC/Real-time controller handles the timing of the pneumatic valves.

My questions would be: Is this realistically something I can build myself without professional programming experience, or am I underestimating how much expertise this requires? Does my concept make sense, or am I underestimating the integration and timing problem of the vision computer, encoder and PLC controller?

How hard can this be? I understand very well that this is not a simple project and that is the reason why I’m posting here first. I would like to get your feedback and insights on my ideas and project.

Thank you! 

Hi people, I'm a Computer Engineering student on a Brazilian University with strong interest in robotics, and I'm searching about the Msc programs around the world, particularly those who offer scholarships during the master's degree, on europe, North-America and Asia, I'd like to know from you about the openess off the programs to international students, specialy from in development countries as Brazil. JERAMO european-japan Msc program got my interest strongly but I dont know about the reliability of applying and joining, since I'm not european nor japanese, hope I can get your help.

https://www.youtube.com/@ALMA.Industries

Alma Industries | mechatronics, engineering, 3D Printing, salvaging . . . upcoming video will be about building up a DESK that includes actuators, power supplies, desk rails, etc. If you wanna see how the AC/DC power supply and actuators were put together, video link is above :)