[Charmed Labs] are responsible for bringing numerous open-source hardware products to fruition over the years, and their latest device is an adorably small robotic camera platform called Goby, currently crowdfunding for its initial release. Goby has a few really clever design features and delivers a capable (and hackable) platform for under 100 USD.

Goby embraces its small size, delivering what its creators dub “tinypresence” — or the feeling of being there, but on a very small scale. Cardboard courses, LEGO arenas, or even tabletop gaming scenery hits different when experienced from a first-person perspective. Goby is entirely reprogrammable with nothing more than a USB cable and the Arduino IDE, while costing less than most Arduino starter kits.

One of the physical features we really like is the tail-like articulated caster at the rear. Flexing this pivots Goby up or down (and can even flip Goby completely over), allowing one to pan and tilt the view without needing to mount the camera on a gimbal. It also comes into play for recharging; Goby simply moves over the disc-shaped charger and pivots down to make contact.

At Goby‘s heart is an ESP32-S3 and OmniVision OV2640 camera sensor streaming a live video feed (and driving controls) with WebRTC. Fitting the WebRTC stack onto an ESP32 wasn’t easy, but opens up possibilities beyond just media streaming.

Goby is set up to make launching an encrypted connection as easy as sharing a URL or scanning a QR code. The link is negotiated between bot and client with the initial help of an external server, and once a peer-to-peer connection is established, the server’s job is done and it is out of the picture. [Charmed Labs]’s code for this functionality — named BitBang — is in beta and destined for an open release as well. While BitBang is being used here to make it effortless to access Goby remotely, it’s more broadly intended to make web access for any ESP32-based device easier to implement.

As far as tiny remote camera platforms go, it might not be as small as rebuilding a Hot Wheels car into a micro RC platform, but it’s definitely more accessible and probably cheaper, to boot. Check it out at the Kickstarter (see the first link in this post) and watch it in action in the video, embedded just below the page break.

Getting a robot to stand on two wheels without tipping over involves a challenging dance with the laws of physics. Self-balancing robots are a great way to get into control systems, sensor fusion, and embedded programming. This build by [mircemk] shows how to make one with just a few common components, an Arduino, and a bit of patience fine-tuning the PID controller.

At the heart of the bot is the MPU6050 – a combo accelerometer/gyroscope sensor that keeps track of tilt and movement. An Arduino Uno takes this data, runs it through a PID loop, and commands an L298N motor driver to adjust the speed and direction of two DC motors. The power comes from two Li-ion batteries feeding everything with enough juice to keep it upright. The rest of the magic lies in the tuning.

PID (Proportional-Integral-Derivative) control is what makes the robot stay balanced. Kp (proportional gain) determines how aggressively the motors respond to tilting. Kd (derivative gain) dampens oscillations, and Ki (integral gain) helps correct slow drifts. Set them wrong, and your bot either wobbles like a confused penguin or falls flat on its face. A good trick is to start with only Kp, then slowly add Kd and Ki until it stabilizes. Then don’t forget to calibrate your MPU6050; each sensor has unique offsets that need to be compensated in the code.

Once dialed in, the result is a robot that looks like it defies gravity. Whether you’re hacking it for fun, turning it into a segway-like ride, or using it as a learning tool, a balancing bot is a great way to sharpen your control system skills. For more inspiration, check out this earlier attempt from 2022, or these self-balancing robots (one with a little work) from a year before that. You can read up on [mircemk]’s project details here.

We’re on a sort of vacation in Tanzania at the moment and staying in a modest hotel away from the tourist and government district. It’s a district of small shops selling the same things and guys repairing washing machines out on the sidewalk. The guys repairing washing machines are more than happy to talk. Everybody’s amazingly friendly here, the hotel guy grilled us for an hour about our home state. But I really didn’t expect to end up in a conversation about computer vision.

In search of some yogurt and maybe something cooler to wear, we went on a little walk away from the hotel. With incredible luck we found a robotics shop a few blocks away. Mecktonix is a shop about two meters each way, stuffed full of Arduinos, robots, electronics components, servos, and random computer gear, overseen by [Yohanna “Joe” Harembo]. Nearby is another space with a laser engraver and 3D printer. The tiny space doesn’t stop them from being busy. A constant stream of automotive tech students from the nearby National Institute of Transport shuffle in for advice and parts for class assigned projects.

In between students, Joe demos an autonomous car he’s working on. In classic hacker fashion, he first has to reattach the motor driver board and various sensors, but then he demos the car and its problem –  the video frame rate is very slow. We dive in with him and try to get some profiling using time.monotonic_ns(). He’s never done profiling before, so this is a big eye opener. He’s only processing one video frame every 4.3 seconds, using YOLO on a Pi 3, and yup, that’s the problem.  I suggest he change to gamut detection or a Pi 4.

Gamut Detection

If you’re not familiar with gamut detection, it’s one of the simplest of all computer vision techniques, so it’s easy to implement on slow processors and almost trivial to code. Basically, it’s “look for a color”.  If you want your robot to follow you, wear a lime green T shirt. Now the robot just has to look for lime green. Same for catching a ball or following a line. The algorithm is simple – convert each pixel to HSV, where hue corresponds to the direction around the color wheel,  saturation corresponds to how concentrated the color is, and value how bright. Brightness depends on the lighting, so you can throw value away and just set limits for H and S. Anything within those limits is part of our target. The box formed by those limits is our “gamut”.

There are a couple speedups you can apply as well. First, ask yourself how much resolution you need from the camera. If you only want to track a green T shirt that’s never less than 24 pixels on screen, turn the resolution down by a factor of six each way and look for four-pixel T shirts. You now have 1/36th as much data to process and your algorithm runs 36 times faster. If you can’t control the camera resolution, you can shrink the image or just sample every nth pixel. Second, you can often ask for a YUV or YIQ image from the camera. Discard Y and set your limits in IQ or UV coordinates. It’s about the same as HSV.

Joe’s eating this up – he’s had limited chances to talk with somebody else who is into computer vision. As we write this, he’s still trying with YOLO, but at lower resolution. If it doesn’t work he’ll try gamut detection. And it’s not his only project. Passenger carrying motorcycles called pikipikis are common here. A student has a project to enforce passengers wearing a helmet, and we fiddle with the student’s project.

The Dar es Salaam Scene

There’s other tech happening in Tanzania too. A few blocks away is [Ruta Electronics], a similar sugar cube sized shop developing smart meters. Everything from cases to PCB etching happens in the tiny shop. Downtown there are a few tech startups. There’s a fab lab, mostly oriented towards children. And on a quiet side street off the main drag, there’s a tiny shop with three guys who are hacking like crazy.

For us, we’ve had a chance to make a friend from a different culture and play with a robot car together — what could be better?  When you’re traveling, are you on the lookout for other hackers or hackerspaces? It’s worth the effort and brings our community together in a way that even the internet can’t.

When we think about machine learning, our minds often jump to datacenters full of sweating, overheating GPUs. However, lighter-weight hardware can also be used to these ends, as demonstrated by [Nikodem Bartnik] and his latest robot.

The robot is charged with autonomously navigating a simple racetrack delineated by cardboard barriers. The robot is based on a two-wheeled design with tank-style steering. Controlled by an Arduino Uno, the robot uses a Slamtec RPLIDAR sensor to help map out its surroundings. The microcontroller is also armed with a Bluetooth link and an SD card for storage.

The robot was first driven around the racetrack multiple times under manual control, all the while collecting LIDAR data. This data was combined with control inputs to help create a data set that could be used to train a machine learning model. Feature selection techniques were used to refine down the data points collected to those most relevant to completing the driving task. [Nikodem] explains how the model was created and then refined to drive the robot by itself in a variety of race track designs.

It’s a great primer on machine learning techniques applied to a small embedded platform.

While fixed sensors, relays, and cameras can be helpful in monitoring your home, there are still common scenarios you need to physically go and check something. Unfortunately, this is often the case when you’re away from home. To address this challenge, [PriceLessToolkit] created a guardian bot that can be controlled through Home Assistant.

The robot’s body is made from 3D-printed components designed to house the various modules neatly. The ESP32 camera module provides Wi-Fi and video capabilities, while the Arduino Pro Mini serves as the bot’s controller. Other peripherals include a light and radar sensor, an LED ring for status display, and a speaker for issuing warnings to potential intruders. The motor controllers are salvaged from two 9-gram servos. The onboard LiPo battery can be charged wirelessly with an integrated charging coil and controller by driving the bot onto a 3D-printed dock.

This build is impressive in its design and execution, especially considering how messy it can get when multiple discrete modules are wired together. The rotating castor wheels made from bearings add an elegant touch.

If you’re interested in building your own guard bot, you can find the software, CAD models, and schematics on Github.

Thanks for the tip [Bernard]!

Home Assistant is a popular software tool around, and we’ve seen it connect to boilers, blinds, beds and 433 MHz sensors.

This Hackaday prize entry from [saul] is the beginning of a reconfigurable kit of 3D printed parts and servo motors for robotics learning. With just access to a printer, a few cheap-as-chips servo motors, an Arduino, and some nuts and bolts, you could be hacking together robot walkers within a few hours of starting!

Bolt Bots is very simple to understand, with all the mechanics and wiring out there in the breeze, but strictly for indoor use we reckon. If you want to add remote control to your application, then drop in one of the ubiquitous nRF24L01 boards and build yourself a copy of the remote control [saul] handily provides in this other project.

There really isn’t a great deal we can say about this, as it’s essentially a build kit with quite a few configuration options, and you just have to build with it and see what’s possible. We expect the number of parts to proliferate over time giving even more options. So far [saul] demonstrates a few flavors of ‘walkers’, a rudimentary ‘robot arm’, and even a hanging drawbot.

The bolt hardware can be found in this GitHub repo, and the remote control code in this second one.

Servo-based designs are sometimes sneered at due to their dubious accuracy and repeatability, but with a little of effort, this can be vastly improved upon. Also, multi-legged walkers need multiple servos and controllers to drive ’em. Or do they?

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Who says you can’t teach an old robot new tricks? Nobody, actually. That saying is about dogs. But it applies to robots too, at least judging by the way this late-90s industrial beast was put to use in a way it was never intended: as a giant CNC router.

The machine in question is an ABB IRB6400, a six-axis, floor-mounted industrial machine that had a long career welding at a Eurorail factory in Austria before [Brian Brocken] made its acquaintance. He procured the non-working machine — no word on what he paid for it — and moved the 2-ton paperweight into his shop, itself a non-trivial endeavor. After a good scrubbing, [Brian] tried to get the machine started up. An error prevented the robot controller from booting; luckily, there’s a large community of ABB users, and [Brian] learned that one of the modules in the controller needed replacement.

After fixing that — and swapping out the controller’s long-dead backup batteries, plus replacing the original 1.44 MB floppy drive with a USB drive — he was able to bring the machine back to life. Unfortunately, the limited amount of internal memory made it difficult to use for anything complicated, so [Brian] came up with an application to stream coordinates to the controller over a serial port, allowing for unlimited operation. With that in place, plus a simple spindle mounted to the robot’s wrist with a 3D printed adapter, [Brian] was able to carve foam blocks into complex shapes. The video below shows everything from delivery to first chips — well, dust at least.

This build seems to be a significant escalation from [Brian]’s previous large-format CNC machine. He must have something interesting in mind, so stay tuned for details.

Having a 3D printer or a CNC machine available for projects is almost like magic. Designing parts in software and having them appear on the workbench is definitely a luxury. But for a lot of us, these tools aren’t easily available and projects that use them can be out-of-reach. That’s why one of the major design goals of this robotics platform was to use as many off-the-shelf components as possible.

The robot is called the OpenScout and, as its name implies, intends to be a fully open-source robotics platform for a wide range of use cases. It uses readily-available aluminum extrusion as a frame, which bolts together without any other specialized tools like welders. The body of the robot is articulating, helping it navigate uneven terrain outdoors. The specifications also call for using an Arduino to drive the robot, although there is plenty of space in the robot body to house any robotics platform you happen to have on hand.

For anyone looking to get right into the useful work of what robots can do, rather than spending time building up a platform from scratch, this is an excellent project. It’s straightforward and easy to build without many specialized tools. The unique articulating body design should make it effective in plenty of environments. If you do have a 3D printer, though, that opens up a lot of options for robotics platforms.

Aren’t ball races and marble runs fun? Wouldn’t they be so much more enjoyable if you didn’t have to climb back up the ladder each time, as it were, and reset the thing? [Johannes] wrote in to tell us about a wee robot with the Sisyphean task of setting a ball bearing on a simple but fun course, collecting it from the end, and airlifting it back to the start of the track.

[Johannes] built this ‘bot to test small-scale resin printing strength as well as the longevity of some tiny linear actuators from Ali that may or may not be available at a moment’s notice. The point was to see how these little guys fared when connected directly to an Arduino or other microcontroller, rather than going the safer route with a motor driver of some kind.

Some things worked well, like the c-clips that keep the axles together, and using quick pulses to release the magnetically-linked ball from the gripper. Other aspects didn’t work out so well. Tiny resin parts do not respond well to force, for starters. And then there’s the actuators themselves. The connections are fragile and the motors are weak, but they vary wildly in quality from piece to piece, so YMMV. Some lose steps, and others occasionally seize. But you wouldn’t know any of that from the graceful movement capture in the video below. Although it appears to be automated, the bot is under remote control because of the motor issues.

Not into ball runs? There are other Sisyphean tasks available, such as moving sand around in the name of meditation.

If there’s one thing audiences love in sci-fi, it’s a cute robot companion that follows the heroes around. If you want one of your own, starting with this build from [mircemk] could be just the ticket.

The build relies on the classic Arduino Uno microcontroller, which talks to a HC-SR04 ultrasonic sensor module and two infrared sensors in order to track a human target and follow it around. Drive is thanks to four DC gear motors, driven by a L293D motor driver, with a two-cell lithium battery providing power for everything onboard.

The robot works in a simple manner, following a hand placed in front of the robot’s sensors. First, the robot checks for the presence of an object in front using the ultrasonic sensor. If something is detected, the twin infrared sensors mounted left and right are used to guide the robot, following the hand.

It’s not a sophisticated algorithm, and it won’t really let your robot follow you down a crowded street. However, it’s a great project to learn on for beginners and could serve as a great entry into more advanced projects using face tracking or other techniques. Video after the break.