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Someone built ultrasonic scanning on a tiny microcontroller and it’s seriously impressive Using Arduino, an ultrasonic sensor + a servo motor turns into a mini radar system: → scans surroundings in a sweep → detects objects in real time → measures distance with sound waves → builds a simple...

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Vaishnavi

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564,711 次观看 • 2 个月前 •via X (Twitter)

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Arduino-based radar system is a smart project that detects your surroundings with sound waves. Usually it's done using an ultrasonic sensor and a servo motor.🛜
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Arduino-based radar system is a smart project that detects your surroundings with sound waves. Usually it's done using an ultrasonic sensor and a servo motor.🛜

Ahib Jane

529,686 次观看 • 7 个月前

A student built a real anti-gravity machine… using an Arduino. How to Make an Acoustic Levitator: Arduino Nano + motor driver + about 60 ultrasonic transducers. They all emit ~40 kHz sound. The sound waves meet and form fixed pockets in the air. Tiny bits of styrofoam get stuck in those pockets and just hang there. If you put your hand in, the pattern breaks and they fall. Same principle labs use to move droplets or samples without touching them. Credit: u/williamlk5341 on r/arduino Based on an “Acoustic Levitator” Instructable guide: ---- Weekly robotics and AI insights. Subscribe free:
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A student built a real anti-gravity machine… using an Arduino. How to Make an Acoustic Levitator: Arduino Nano + motor driver + about 60 ultrasonic transducers. They all emit ~40 kHz sound. The sound waves meet and form fixed pockets in the air. Tiny bits of styrofoam get stuck in those pockets and just hang there. If you put your hand in, the pattern breaks and they fall. Same principle labs use to move droplets or samples without touching them. Credit: u/williamlk5341 on r/arduino Based on an “Acoustic Levitator” Instructable guide: ---- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

816,588 次观看 • 4 个月前

That’s insane! 🤯 A student built an acoustic levitation divide with an Arduino board. He built it using an Arduino Nano, a motor driver, and 60 ultrasonic transducers that can levitate low-density objects in place indefinitely. The transducers send out 40 kHz waves that create standing waves. The interference pattern produces nulls that trap objects. High-pressure areas form below and above the object, locking it in the low-pressure area between them. The transducers produce two sound waves moving in opposing directions at the same frequency and amplitude. The effect is that the low-pressure areas don't appear to move, like whipping a rope from both ends and having the wave meet in the middle. Sound waves are oscillating at high and low pressures. By creating a sound wave that doesn't move forward (a standing wave), you create areas of constant pressure. 🔉 Objects get trapped in the null points between high-pressure zones. The craziest part is that this was made more than 7 years ago! DIY levitation 😮‍💨 Reddit link: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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That’s insane! 🤯 A student built an acoustic levitation divide with an Arduino board. He built it using an Arduino Nano, a motor driver, and 60 ultrasonic transducers that can levitate low-density objects in place indefinitely. The transducers send out 40 kHz waves that create standing waves. The interference pattern produces nulls that trap objects. High-pressure areas form below and above the object, locking it in the low-pressure area between them. The transducers produce two sound waves moving in opposing directions at the same frequency and amplitude. The effect is that the low-pressure areas don't appear to move, like whipping a rope from both ends and having the wave meet in the middle. Sound waves are oscillating at high and low pressures. By creating a sound wave that doesn't move forward (a standing wave), you create areas of constant pressure. 🔉 Objects get trapped in the null points between high-pressure zones. The craziest part is that this was made more than 7 years ago! DIY levitation 😮‍💨 Reddit link: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

99,086 次观看 • 1 个月前

BaBot: The Ball-Balancing Robot - Real-time PID control on a 2-axis platform, powered by a microcontroller using the same chip as an Arduino. - Precision servos and IR sensors track the ball with speed and accuracy. - Perfect for learning control systems, teaching robotics, or showing off your engineering skills. - Fully open-source & ready to build at Video Credit: Johan Link #engineering #technology #robots #robotics
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BaBot: The Ball-Balancing Robot - Real-time PID control on a 2-axis platform, powered by a microcontroller using the same chip as an Arduino. - Precision servos and IR sensors track the ball with speed and accuracy. - Perfect for learning control systems, teaching robotics, or showing off your engineering skills. - Fully open-source & ready to build at Video Credit: Johan Link #engineering #technology #robots #robotics

Wevolver

48,877 次观看 • 1 年前

A Chinese quantum radar can see what no one else can: it detects objects at a distance of 15 million km! New Chinese radar research has developed a quantum radar operating at extremely low temperatures, capable of detecting objects up to 15 million km away—40 times the distance between the Earth and the Moon.
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A Chinese quantum radar can see what no one else can: it detects objects at a distance of 15 million km! New Chinese radar research has developed a quantum radar operating at extremely low temperatures, capable of detecting objects up to 15 million km away—40 times the distance between the Earth and the Moon.

China pulse 🇨🇳

71,539 次观看 • 2 个月前

AI in robotics gets all the attention right now, but sometimes the most interesting work is very practical. Viet built a small vision system that counts potatoes on a conveyor belt. No giant dataset. No huge model. Just a clear problem and a smart setup. He used Ultralytics’ ObjectCounter, trained a tiny YOLO11 nano model, and because there was no potato dataset, he annotated a single frame with SAM 2 and trained from that. One frame. Still works across the whole video. It is a good reminder that useful AI in industry often looks like this. Focused. Lightweight. Solves a real task. If you work in manufacturing or robotics, these small systems are usually the fastest wins. They save time, reduce errors, and do not need massive infrastructure. Nice work, Viet. His projects: —- Weekly robotics and AI insights. Subscribe free:
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AI in robotics gets all the attention right now, but sometimes the most interesting work is very practical. Viet built a small vision system that counts potatoes on a conveyor belt. No giant dataset. No huge model. Just a clear problem and a smart setup. He used Ultralytics’ ObjectCounter, trained a tiny YOLO11 nano model, and because there was no potato dataset, he annotated a single frame with SAM 2 and trained from that. One frame. Still works across the whole video. It is a good reminder that useful AI in industry often looks like this. Focused. Lightweight. Solves a real task. If you work in manufacturing or robotics, these small systems are usually the fastest wins. They save time, reduce errors, and do not need massive infrastructure. Nice work, Viet. His projects: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

1,674,814 次观看 • 7 个月前

Coding software is a joke compared to building hardware. Because when a software fails, it can be fixed quickly. In hardware, one mistake can set you back for months or end you. In hardware, every decision is cash flow. Every tiny error can cost tens of 1000s of dollars. You don’t get second chances. You get lessons, paid for in time and money. But when you finally get it right… You don’t just ship a product. You build a legacy. Something people touch, use, and trust for years. That’s the beauty and the pain of building in the real world.
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Coding software is a joke compared to building hardware. Because when a software fails, it can be fixed quickly. In hardware, one mistake can set you back for months or end you. In hardware, every decision is cash flow. Every tiny error can cost tens of 1000s of dollars. You don’t get second chances. You get lessons, paid for in time and money. But when you finally get it right… You don’t just ship a product. You build a legacy. Something people touch, use, and trust for years. That’s the beauty and the pain of building in the real world.

Prakash Dadlani

12,402 次观看 • 7 个月前

Placing objects sounds simple… until robots have to do it. This method makes it simple, fast & reliable. [Github ⬇️] Robotic object placement is tough, especially with stacking, hanging, or insertion. AnyPlace is a new two-stage method that uses only synthetic data and a vision-language model to teach robots where and how to place objects; even in the real world. Why this works ✅ Finds the right spot with help from vision-language models ✅ Handles stacking, insertion, and hanging with no real-world training ✅ Trained on synthetic data using Blender and IsaacSim ✅ Works in the real world without fine-tuning It shows that smart use of simulation and language models can make robotic placement tasks easier, faster, and more reliable. Github: Paper: Thank you for sharing Animesh Garg !
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Placing objects sounds simple… until robots have to do it. This method makes it simple, fast & reliable. [Github ⬇️] Robotic object placement is tough, especially with stacking, hanging, or insertion. AnyPlace is a new two-stage method that uses only synthetic data and a vision-language model to teach robots where and how to place objects; even in the real world. Why this works ✅ Finds the right spot with help from vision-language models ✅ Handles stacking, insertion, and hanging with no real-world training ✅ Trained on synthetic data using Blender and IsaacSim ✅ Works in the real world without fine-tuning It shows that smart use of simulation and language models can make robotic placement tasks easier, faster, and more reliable. Github: Paper: Thank you for sharing Animesh Garg !

Ilir Aliu - eu/acc

22,843 次观看 • 1 年前

Did you know you can create the Maxwell-Boltzmann distribution with just some balls and a motor? I built this simple device to illustrate it. This is how it works: A spinning agitator pumps energy into ~400 balls, so they bounce around and collide like gas particles in a box. They escape one at a time through a tiny hole, then fall a fixed gap into bins below. Since the hole is so small, every ball leaves with no vertical speed and takes the same time to fall. Constant fall time means how far a ball flies sideways is set purely by its speed. A faster ball goes into a further bin. So each bin is really a speed. Tally all 400 and you get the Maxwell-Boltzmann distribution.
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Did you know you can create the Maxwell-Boltzmann distribution with just some balls and a motor? I built this simple device to illustrate it. This is how it works: A spinning agitator pumps energy into ~400 balls, so they bounce around and collide like gas particles in a box. They escape one at a time through a tiny hole, then fall a fixed gap into bins below. Since the hole is so small, every ball leaves with no vertical speed and takes the same time to fall. Constant fall time means how far a ball flies sideways is set purely by its speed. A faster ball goes into a further bin. So each bin is really a speed. Tally all 400 and you get the Maxwell-Boltzmann distribution.

Ethan Gao

372,175 次观看 • 1 天前

Simulations are extremely powerful in Houdini, but even more powerful is leveraging the procedural system to create "real-time" simulations. These effects are driven by a mix of attributes and simple operations that you can build on to create complex results. Master SOP Workflows with these courses 30% Off for the next few days
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Simulations are extremely powerful in Houdini, but even more powerful is leveraging the procedural system to create "real-time" simulations. These effects are driven by a mix of attributes and simple operations that you can build on to create complex results. Master SOP Workflows with these courses 30% Off for the next few days

Voxyde

19,399 次观看 • 1 年前

🛠️ The easiest deck stairs you’ll ever build — no concrete, no fancy framing, no stress! Just stack some cinder blocks in a staggered pattern + drop in pressure-treated lumber planks. Done in minutes. This smart DIY turns an awkward drop off your deck into solid, good-looking stairs for a fraction of the cost and time. She even dressed it up with potted flowers and it looks clean as hell. Perfect weekend project for any homeowner. Who else loves simple, effective builds like this? 🇺🇸 Drop a 🧱 if you’re stealing this idea.
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🛠️ The easiest deck stairs you’ll ever build — no concrete, no fancy framing, no stress! Just stack some cinder blocks in a staggered pattern + drop in pressure-treated lumber planks. Done in minutes. This smart DIY turns an awkward drop off your deck into solid, good-looking stairs for a fraction of the cost and time. She even dressed it up with potted flowers and it looks clean as hell. Perfect weekend project for any homeowner. Who else loves simple, effective builds like this? 🇺🇸 Drop a 🧱 if you’re stealing this idea.

🦅 Eagle Wings 🦅

173,919 次观看 • 1 个月前

Want to use tactile sensing but not familiar with hardware? No worries! Just follow the steps, and you’ll have a high-resolution tactile sensor ready in 30 mins! It’s as simple as making a sandwich! 🥪 🎥 YouTube Tutorial: 🛠️ Open Source & Hardware Guide: 🌐 Project Website: Let’s make robotics more tactile! 🤖 #Robotics #TactileSensing #AI #opensource
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Want to use tactile sensing but not familiar with hardware? No worries! Just follow the steps, and you’ll have a high-resolution tactile sensor ready in 30 mins! It’s as simple as making a sandwich! 🥪 🎥 YouTube Tutorial: 🛠️ Open Source & Hardware Guide: 🌐 Project Website: Let’s make robotics more tactile! 🤖 #Robotics #TactileSensing #AI #opensource

Binghao Huang

31,584 次观看 • 1 年前

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.
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This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

Rohan Paul

37,121 次观看 • 4 个月前

Car-to-curb distance estimation using Ultralytics YOLO26. 🚗 In parking lots, knowing the exact distance between a vehicle and the curb can help prevent minor collisions and improve parking precision. In this demo, a car is detected and tracked, and the system calculates the distance from the curb in real time in pixel space, showcasing how computer vision can turn simple camera feeds into spatial awareness tools. Implementation details 👇 #cardistance #visionai #ROADSAFETYCONCERN
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Car-to-curb distance estimation using Ultralytics YOLO26. 🚗 In parking lots, knowing the exact distance between a vehicle and the curb can help prevent minor collisions and improve parking precision. In this demo, a car is detected and tracked, and the system calculates the distance from the curb in real time in pixel space, showcasing how computer vision can turn simple camera feeds into spatial awareness tools. Implementation details 👇 #cardistance #visionai #ROADSAFETYCONCERN

Muhammad Rizwan Munawar

27,503 次观看 • 2 个月前

Real time transactions are coming to Abstract. In this demo, we built a simple cookie clicker where every click is an onchain transaction. You can see each transaction happen immediately with almost no delay. A web2 UX secured by a blockchain under the hood. This is how every app on Abstract will be built in the near future. This particular demo leverages AGW and session keys to remove the need for wallet popups. It uses a special RPC endpoint (which will be live on our mainnet soon) to fetch transaction statuses ahead of time.
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Real time transactions are coming to Abstract. In this demo, we built a simple cookie clicker where every click is an onchain transaction. You can see each transaction happen immediately with almost no delay. A web2 UX secured by a blockchain under the hood. This is how every app on Abstract will be built in the near future. This particular demo leverages AGW and session keys to remove the need for wallet popups. It uses a special RPC endpoint (which will be live on our mainnet soon) to fetch transaction statuses ahead of time.

cygaar

56,166 次观看 • 1 年前

High school students built an autonomous ball-collecting robot! 🎾 A group of high school students built a robot that picks up balls and shoots them into a bin while moving without stopping, with impressive speed and accuracy. It combines mechanical design, sensors, and software making constant adjustments in real time while the robot is driving. When teenagers can build systems this sophisticated, the talent pipeline for the robotics industry is accelerating! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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High school students built an autonomous ball-collecting robot! 🎾 A group of high school students built a robot that picks up balls and shoots them into a bin while moving without stopping, with impressive speed and accuracy. It combines mechanical design, sensors, and software making constant adjustments in real time while the robot is driving. When teenagers can build systems this sophisticated, the talent pipeline for the robotics industry is accelerating! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

1,180,922 次观看 • 3 个月前

Tokenization is quickly becoming one of the most talked-about ideas in crypto. This year we’re aiming much higher. We don’t just want to tokenize miner revenue; we want to bring entire productive ecosystems on-chain. That means tokenizing baskets of assets, real energy infrastructure, AI agents, compute power, data centres, and even the cooling systems that keep high-performance hardware running at scale. All of it connected and interoperable with DeFi. Thats what we have been building throughout. A single on-chain framework that unlocks something genuinely powerful: real ownership, real yield, and real coordination between digital and physical infrastructure. From backend systems and front-end interfaces to smart contracts, AI, physical infrastructure, oracles, and autonomous agents, the stack keeps expanding and compounding. It’s about building the foundation for how value will be owned, deployed, and earned in the next generation of the on-chain economy. Simplified by PinLink.
0:15

Tokenization is quickly becoming one of the most talked-about ideas in crypto. This year we’re aiming much higher. We don’t just want to tokenize miner revenue; we want to bring entire productive ecosystems on-chain. That means tokenizing baskets of assets, real energy infrastructure, AI agents, compute power, data centres, and even the cooling systems that keep high-performance hardware running at scale. All of it connected and interoperable with DeFi. Thats what we have been building throughout. A single on-chain framework that unlocks something genuinely powerful: real ownership, real yield, and real coordination between digital and physical infrastructure. From backend systems and front-end interfaces to smart contracts, AI, physical infrastructure, oracles, and autonomous agents, the stack keeps expanding and compounding. It’s about building the foundation for how value will be owned, deployed, and earned in the next generation of the on-chain economy. Simplified by PinLink.

PinLink

14,339 次观看 • 5 个月前

Train a TensorFlow object detection model – then deploy it on a robot 🤖 Iulia Feroli (Iulia Feroli) shows how to turn a notebook into a real-time object detection app. This tutorial works for any project – though we demonstrate the deployment on (and assisted by!) the #ReachyMini, an open-source robot from Pollen Robotics. Built with PyCharm + Claude Code. 👉 Watch it in action:
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Train a TensorFlow object detection model – then deploy it on a robot 🤖 Iulia Feroli (Iulia Feroli) shows how to turn a notebook into a real-time object detection app. This tutorial works for any project – though we demonstrate the deployment on (and assisted by!) the #ReachyMini, an open-source robot from Pollen Robotics. Built with PyCharm + Claude Code. 👉 Watch it in action:

PyCharm, a JetBrains IDE

34,908 次观看 • 1 个月前

🇮🇳🇺🇸 EARTH JUST GOT A NEW EYE - AND IT SEES EVERYTHING NASA and India just launched a satellite that can track earthquakes, landslides, glacier shifts and crop collapse down to the centimeter. From 464 miles up, through clouds, at night, in real time. NISAR is the first U.S.–India satellite collab with shared hardware, and it’s armed with dual radar that scans Earth’s surface every 12 days. No filter. No weather delay. What do you get when Trump and Modi shake hands in space policy? A synthetic aperture radar that can watch the ground move before you even feel it. Forget Google Earth. This one updates itself. Source: NASA, NSF - NASASpaceflight.com
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🇮🇳🇺🇸 EARTH JUST GOT A NEW EYE - AND IT SEES EVERYTHING NASA and India just launched a satellite that can track earthquakes, landslides, glacier shifts and crop collapse down to the centimeter. From 464 miles up, through clouds, at night, in real time. NISAR is the first U.S.–India satellite collab with shared hardware, and it’s armed with dual radar that scans Earth’s surface every 12 days. No filter. No weather delay. What do you get when Trump and Modi shake hands in space policy? A synthetic aperture radar that can watch the ground move before you even feel it. Forget Google Earth. This one updates itself. Source: NASA, NSF - NASASpaceflight.com

Mario Nawfal

604,711 次观看 • 11 个月前