Setting Up Robotics Projects Using Embedded Technology

For our 3rd year mechatronics design project at the University of Waterloo, our team (Varrun Vijayanathan, Ethan Dau, Eric Gharghouri) built a SCARA-style robot arm—capable of moving a 20-sided die across a 300x150x75mm space—with sub-$300 in parts and a focus on precision and repeatability. We didn’t take the easiest route. We took the one with the most learning. Key Features: → Custom inverse kinematics in C and Python with multi-solution handling → Hardware-timed stepper motor control using STM32 timers → Hardware limit switch debouncing + custom state machine → Z-axis rack system with gain-scheduled torque control → Manual joystick control mode with real-time override Tech Stack: → STM32 Nucleo + PlatformIO → 3D-printed structure with cycloidal joints and axial/thrust bearings → Custom debounce circuit for limit switches → Simulation tooling in Python (Tkinter) → Modular firmware with HAL layers for HMI, motors, and controls We hit our 60mm target 10/10 times at the demo and passed both accuracy and repeatability objectives. I led firmware, systems integration, and architecture—and came out the other side with a much deeper understanding of embedded motion control and hardware/software co-design. Full write-up, code, videos, and lessons here: https://lnkd.in/grAuhWEC

"A robot without control is just a pile of metal" Building the bomb diffusion robot wasn’t just about designing a strong frame and selecting the right motors—it was about ensuring precise, real-time control for the bomb squad. In high-risk scenarios, latency, reliability, and ease of operation can mean the difference between success and disaster. Key Challenges in Remote Control Design: - Latency & Responsiveness – The robot should receive near-instant data - Multiple Control Inputs – The robot's hybrid drive system and 5 DOF arm needed a complex integration procedure - Secure & Long-Range Communication – Ensuring uninterrupted signals in complex environments - User Experience - The controls should be intuitive enough for the user to learn within minutes The Control System We Designed: - Control Communication: Radio Frequency (RF) Control - Custom-Built Remote: One Joystick - For controlling the hybrid wheel drive system Toggle Switches - For controlling the lights and cameras and switching between skid steering and Ackermann control Rotary Encoders - For precise adjustments in robotic arm positioning. We had to strike a balance between advanced features and affordability. Instead of high-end industrial controllers, we developed a custom-built embedded control system, keeping costs optimized without compromising reliability. Building a control system is always about balancing user experience, latency, and reliability. What’s the biggest challenge you’ve faced in designing a control system? Latency? Signal interference? Intuitive UI? Drop your thoughts in the comments! 🔜 Next up: Integration & Safety—bringing together mechanics, electronics, and software to create a fully functional robot. From power management to failsafe mechanisms, I’ll share how we ensured the robot operates safely and efficiently in mission-critical scenarios. Stay tuned!

🚀 Getting Started with Real-World Robots with LeRobot from Hugging Face (Top 60+ open-source robotics projects for beginners- Video: https://lnkd.in/eTYHRfYh Blog article with presentation: https://lnkd.in/e2E5_Jje) 🛠️ 1. Order and Assemble Your Koch v1.1 🌐 Follow instructions on the Koch v1.1 Github page for detailed assembly guidance. 📺 Visual walkthrough: Assembly video provides step-by-step visual instructions. ⚙️ 2. Configure Motors, Calibrate Arms, Teleoperate a. Control Motors with DynamixelMotorsBus 🔄 Configure Motors: Assign unique indices to each motor for proper communication. b. Teleoperate with KochRobot 🏗 Instantiate KochRobot: Create a robot instance using pre-configured arms. 🧮 Calibrate Robot: Align the leader and follower arms for synchronized movements. 🕹 Teleoperate: Manually control the robot by moving the leader arm, which directs the follower arm. c. Add Cameras with OpenCVCamera 🔍 Find Camera Indices: Detect available cameras and assign indices for identification. 📸 Instantiate Camera: Connect and initialize the cameras using OpenCV. 🎥 Add Cameras to Robot: Integrate camera feeds into the robot system for real-time visual feedback. d. Use koch.yaml and Teleoperate Function ▶️ Run Teleoperate Script: Utilize the YAML configuration file to automate teleoperation setup and execution. 🎥 3. Record Your Dataset and Visualize It a. Use koch.yaml and Record Function 📹 Record Data: Capture state and action data during teleoperation for later use in training. b. Tips for Recording Dataset 🏁 Start with simple tasks (e.g., grasping objects) to build a foundational dataset. 🎥 Record multiple episodes for consistency and better training data. 🔄 Gradually introduce variations to improve the robustness of your model. c. Visualize All Episodes 👀 Run Visualization Script: Review recorded episodes using visualization tools for analysis and debugging. d. Replay Episode 🕹 Run Replay Script: Test the repeatability of recorded episodes by replaying actions on the robot. 🧑🏫 4. Train a Policy on Your Data a. Use the Train Script 🧠 Run Train Script: Train a neural network policy using the recorded dataset for autonomous robot control. b. (Optional) Upload Policy Checkpoints ☁️ Upload Latest Checkpoint: Share your trained model by uploading checkpoints to the cloud. 🧪 5. Evaluate Your Policy a. Use koch.yaml and Record Function 🧪 Run Evaluation Script: Perform evaluation runs using the trained policy and record the results. b. Visualize Evaluation Afterwards 👀 Visualize Evaluation: Analyze the performance of your policy through visualized evaluation data. Tutorial link: https://lnkd.in/eFCmE2ut Source: https://lnkd.in/ePQJbaWv