Researchers at Harvard built a robot that uses rubber bands to move and sense.

Everyone talks about learning a programming language for Robotics or ROS… but the real gateway into robotics is understanding the engineering laws that actually make robots move. Here are few of the fundamentals: 1️⃣ Forward Kinematics How your robot knows where its hand is. 2️⃣ Inverse Kinematics “How do I move my joints to reach that exact spot?” Robots don’t stretch. 3️⃣ Jacobian Law The secret behind smooth motion. Without it, your robot moves like it’s lagging on bad Wi-Fi. 4️⃣ Dynamics (Newton–Euler / Lagrangian) Forces, mass, torque, gravity - everything that decides whether your robot lifts the payload… or humbles you. 5️⃣ PID Control The universal “calm down” system. Keeps your robot from wobbling like a shopping cart with a bad wheel. 6️⃣ Motor Laws Electricity in → torque out. Choose the wrong motor and your robot becomes decorative art. 7️⃣ Sensor Fusion Robots don’t “trust” a single sensor - they cross-check everything. These laws are the difference between a robot that moves and a robot that apologizes for existing. Understanding the principles is vital to building intelligent machines.

Programming welding by hand: A few years ago, when I was still at Siemens, I remember Wandelbots visiting us and showcasing their Trace Pen: you could teach a robot by simply waving a pen. Do you guys remember, Marco and Christian??? They’ve since pivoted toward a robotics OS, but it’s interesting to see others still pursuing that same vision of intuitive, hand-guided programming. Glance Vision Technologies Srl is applying this idea to welding with DARDO. ✅ Faster workflows, guide the robot directly instead of coding ✅ Precise motion capture, ideal for complex welding tasks ✅ Real-time feedback, fine-tune paths in seconds Website: https://lnkd.in/dDH3afwy Can hand-guided programming help skilled coders achieve more in less time while keeping precision high? Seen at Piotr Stanecki 🌐 —- Weekly robotics and AI insights. Subscribe free: scalingdeep.tech

With disconnected workflows making way for multidisciplinary engineering, the world now demands new skill sets far beyond what's currently taught in engineering textbooks. This is where unified modeling and simulation (MODSIM) is primed for the most impact. By unifying modeling and simulation into one workflow, engineers can design, validate and optimize products more efficiently. Collaboration happens in real time, with the freedom to iterate and push boundaries. Educators can accelerate this transformation by implementing MODSIM competencies into existing engineering curricula and creating a new education experience. The good news? It's actually easier than it seems. Intrigued? http://go.3ds.com/cMcS

"𝐌𝐲 𝐅𝐈𝐑𝐒𝐓 𝐒𝐓𝐄𝐏 𝐢𝐧 𝐭𝐡𝐞 𝐰𝐨𝐫𝐥𝐝 𝐨𝐟 𝐑𝐎𝐁𝐎𝐓𝐈𝐂𝐒 !!"🤖 Feeling proud to share the successful completion and submission of our 𝐁𝐥𝐮𝐞𝐭𝐨𝐨𝐭𝐡-𝐂𝐨𝐧𝐭𝐫𝐨𝐥𝐥𝐞𝐝 𝐌𝐞𝐜𝐚𝐧𝐮𝐦 𝐂𝐚𝐫 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 for our college, ABESIT'S Science Exhibition organized by ISTE Student Chapter in collaboration with Institution’s Innovation Council (IIC). This project was a deep dive into robotics, high-current electronics, and collaborative problem-solving. I, as a team leader, led my team - 𝐓𝐞𝐚𝐦 𝐒𝐩𝐚𝐫𝐭𝐚𝐧𝐬, with my teammates Adarsh Upadhyay, Anant Sharma, and Ansh Chaudhary, who played an essential role in transforming persistent technical failures into a functional solution. 𝐈𝐟 𝐲𝐨𝐮 𝐬𝐚𝐰 𝐨𝐮𝐫 𝐢𝐧𝐢𝐭𝐢𝐚𝐥 𝐩𝐫𝐨𝐭𝐨𝐭𝐲𝐩𝐞, 𝐲𝐨𝐮’𝐝 𝐫𝐞𝐜𝐨𝐠𝐧𝐢𝐳𝐞 𝐭𝐡𝐞 𝐟𝐫𝐮𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧! We battled code glitches, intermittent Bluetooth connections, and the classic '𝐀𝐫𝐝𝐮𝐢𝐧𝐨 𝐂𝐥𝐢𝐜𝐤𝐢𝐧𝐠' 𝐫𝐞𝐬𝐞𝐭 until the very last minute 😅. 🔧 𝐓𝐡𝐞 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐁𝐮𝐢𝐥𝐝: 4x TT motors (100 rpm) 4x Mecanum wheels 1x Arduino UNO (DIP) 1x L298N High-Current Motor Driver Module 1x Bluetooth HC-05 2x 18650 Battery Cells (7.4V total supply) ⚙️𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐢𝐧 𝐁𝐫𝐢𝐞𝐟: The command is sent to the HC-05 after connecting with any Bluetooth car controller app available on Play Store, which then transmits the data to the Arduino Uno. The Uno translates the logic and sends the control signals to the L298N motor driver. The final control logic implements a reliable skid-steer (tank drive) movement, allowing the car to move straight and perform 360° rotation in place. Overall, it was a great learning experience as this was my first project with Arduino. Coding the commands was challenging, but through focused research, we learned enough to get the system operational after many hardware and software failures. 𝐅𝐚𝐢𝐥𝐮𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐛𝐞𝐬𝐭 𝐭𝐞𝐚𝐜𝐡𝐞𝐫!💡 This project was a perfect demonstration that the difference between success and failure often lies in finding the right balance between software logic, stable power delivery, and robust mechanical design. This is just the start, lot more to accomplish in upcoming years!! 𝘠𝘰𝘶 𝘬𝘯𝘰𝘸 𝘸𝘩𝘢𝘵’𝘴 𝘨𝘳𝘦𝘢𝘵? 𝘈𝘤𝘩𝘪𝘦𝘷𝘪𝘯𝘨 𝘢 𝘭𝘦𝘷𝘦𝘭 𝘰𝘧 𝘤𝘳𝘢𝘧𝘵𝘴𝘮𝘢𝘯𝘴𝘩𝘪𝘱 𝘴𝘰 𝘤𝘭𝘦𝘢𝘯 𝘵𝘩𝘢𝘵 𝘫𝘶𝘥𝘨𝘦𝘴 𝘪𝘯𝘪𝘵𝘪𝘢𝘭𝘭𝘺 𝘮𝘪𝘴𝘵𝘢𝘬𝘦 𝘺𝘰𝘶𝘳 𝘱𝘳𝘰𝘫𝘦𝘤𝘵 𝘧𝘰𝘳 𝘢 𝘱𝘳𝘰𝘧𝘦𝘴𝘴𝘪𝘰𝘯𝘢𝘭𝘭𝘺 𝘮𝘢𝘯𝘶𝘧𝘢𝘤𝘵𝘶𝘳𝘦𝘥 𝘬𝘪𝘵. 𝘛𝘩𝘦 𝘥𝘦𝘵𝘢𝘪𝘭𝘴 𝘮𝘢𝘵𝘵𝘦𝘳! 🏆 A big thank you to our college and the exhibition for providing the platform to apply our engineering skills! 🙏 #Robotics #Engineering #ScienceExhibition #Arduino #Teamwork #CollegeProject #ABESIT #AKTU #ICE

💡 𝗖𝗼𝗺𝗺𝗼𝗻 𝗠𝗶𝘀𝘁𝗮𝗸𝗲𝘀 𝗪𝗲 𝗢𝗳𝘁𝗲𝗻 𝗠𝗮𝗸𝗲 𝗪𝗵𝗶𝗹𝗲 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 As a mechatronics student, I have realized that sometimes our projects fail not because of bad ideas but because of avoidable mistakes during design and execution. Here are some key lessons I’ve learned (and still learning 😅): 1️⃣ Jumping into hardware too early: Always start with a clear plan, block diagram, and workflow before wiring anything. 2️⃣ Power issues: Motors, sensors, and microcontrollers need proper power management. Don’t power everything from one line without checking ratings. 3️⃣ No common ground: Forgetting to connect GND properly can make the whole system behave weirdly. 4️⃣ Wrong sensor choice: Choose sensors based on range, accuracy, and environment not just because they “look cool.” 5️⃣ Messy wiring: Organized wiring isn’t just aesthetic; it prevents shorts and makes debugging easier. 6️⃣ Skipping simulation: Tools like Proteus or Tinkercad can save hours of frustration before moving to real hardware. 7️⃣ Poor documentation: Comment your code, save versions, and note what changed. 8️⃣ Too many features: Keep it simple. Start small, test, and then expand your project. 9️⃣ Ignoring real-world conditions: Think about vibration, dust, and how your components will behave outside lab conditions. 🔟 Neglecting safety: Whether it’s high current or rotating parts, always stay safe and add protection circuits. ⚙️ Every failed attempt teaches something that’s the beauty of engineering. I’m sharing this as a reminder (to myself too 😅) that great projects come from great fundamentals. #Mechatronics #EngineeringStudents #LearningByDoing #Robotics #Arduino #STEM #ProjectBasedLearning #Projects #Growth

"How difficult is hardware engineering in robotics?" A student asked this during in one of our career sessions at the Aurora Robotics Core Workshop. Reality is: robotics hardware will break you before it makes you. You're soldering at 2 AM because a single faulty connection killed six hours of work. You're debugging power distribution while your software teammates are already celebrating their sprint. You're learning that gravity, friction, and heat don't care about your beautiful CAD models. One wrong voltage? Dead servo. Misaligned sensor? Garbage data. Thermal runaway? Fire hazard. Hardware doesn't forgive. It teaches. And that's exactly why it matters. While everyone rushes to AI and software, hardware engineers are becoming the scarcest, most valuable players in robotics. The U.S. Bureau of Labor Statistics projects 19% growth in robotics hardware roles through 2033 - four times faster than average engineering jobs. Companies are desperate for people who can make machines move, not just think. But here's what separates those who make it from those who don't: resilience shaped by curiosity. The best hardware engineers aren't the ones who never fail - they're the ones who fail, adapt, and build again. They see a smoking circuit board not as defeat, but as data. It's not supposed to be easy. It's supposed to be worth it. #RoboticsCareers #HardwareEngineering #CareerThursday #EngineeringLife #RoboticsEngineering #BuildTheFuture

Simulation refines, but fundamental reasoning defines. The Underrated Power of Paper Calculations in Modern Design: --------------------------------------------------------------------------------------- In an era dominated by high-speed simulators and AI-assisted tools, the art of “back-of-the-envelope” calculation is often underestimated. Yet, the first few equations scribbled on paper can reveal more about feasibility, stability, and trade-offs than hours of automated sweeps. Hand calculations and simplified models help engineers maintain critical thinking and an unbiased mindset. They illuminate the governing physics before any algorithm takes over. A rough paper estimate of gain, efficiency, or phase margin often guides us toward what truly matters—and prevents chasing numerical illusions. Across analog/mixed-signal ICs, power management, and MEMS/biomedical interfaces, those manual minutes of reasoning still anchor the most elegant designs. Key benefits: Intuition building: first-order models expose the dominant poles, loss paths, and scaling laws. Bias resistance: paper math reduces overfitting to simulator defaults and idealized models. Speed: quick feasibility checks prune bad ideas before deep sweeps. Robustness: back-of-the-envelope sanity checks catch unit errors and unrealistic corner cases. Communication: clear pencil math makes design intent reviewable and teachable. Simulation refines, but fundamental reasoning defines. Good engineering begins with pencil, not processor.

With disconnected workflows making way for multidisciplinary engineering, the world now demands new skill sets far beyond what's currently taught in engineering textbooks. This is where unified modeling and simulation (MODSIM) is primed for the most impact. By unifying modeling and simulation into one workflow, engineers can design, validate and optimize products more efficiently. Collaboration happens in real time, with the freedom to iterate and push boundaries. Educators can accelerate this transformation by implementing MODSIM competencies into existing engineering curricula and creating a new education experience. The good news? It's actually easier than it seems. Intrigued? http://go.3ds.com/DI90

Matplotlib is a powerful Python library used to create static, interactive, and animated visualizations like graphs, charts, and plots—helping engineers analyze and present data effectively. ------------- Learn Future-proof Skills in Manufacturing & Engineering🏭 Acquire new-age digital manufacturing skills, including Digital Twin, Industry 4.0, IIoT, Additive Manufacturing, Material Informatics, and more. 🔗 Link in bio or visit GaugeHow.com Recommended for mechanical, electrical, electronics, and manufacturing engineering students, plus professionals, entrepreneurs, and educators aiming to excel in the Industry 4.0 era. ❤️ Like, Save & Share with your engineering friends 📌 Follow @IndustryX.ai Digital Manufacturing for more Digital Manufacturing & Engineering content . . . . #DigitalManufacturing #smartManufacturing #Manufacturing #Engineering #Industry40 #SmartFactory #Automation #3DPrinting #ManufacturingProcess #LeanManufacturing #DigitalTransformation #DesignForManufacturing #Robotics #industryXai #gaugehow #mechatronics #engineer #industrialengineering #industrial

No power source. No electronics. The soft lens still focuses perfectly on microscopic details. Light becomes the only control system needed. Scientists just created something that changes everything about robotic vision. This squishy robotic eye focuses automatically. No batteries. No circuits. Just pure material engineering. The lens can see details as fine as: • Hairs on an ant's leg • Individual pollen grain lobes • Microscopic structures invisible to naked eye As a Computer Science student working with robotics, this breakthrough excites me. We're always dealing with power constraints and complex electronic systems. This technology eliminates both problems. The lens mimics biological eyes. It responds directly to light changes. The material itself does the focusing work. This opens doors for soft robots that need vision but can't handle traditional cameras. Think medical devices. Environmental sensors. Rescue robots. No electronics means: • Better durability • Lower maintenance • Operation in harsh environments • Simpler manufacturing From my experience with Java and Python projects, I know complexity kills reliability. This approach strips away complexity while adding capability. That's brilliant engineering. The future of robotics might be softer than we thought. And smarter. What applications do you see for electronics-free vision systems? hashtag#SoftRobotics hashtag#ComputerVision hashtag#Innovation