Vention’s Post

💡 We understand the challenges of handling complex geometry, especially when dealing with models containing tens of thousands of components. That's why we've developed specialized tools within 𝘥𝘦𝘤𝘰𝘥𝘦 to pre-process these massive models effortlessly. 🌟 With 𝘥𝘦𝘤𝘰𝘥𝘦, you can quickly visualize lightweight versions of your CAD data while retaining direct access to the originals for detailed analytics and precise toolpath generation. 🚀 Whether you're navigating intricate assemblies or executing complex movements, 𝘥𝘦𝘤𝘰𝘥𝘦 simplifies the process, ensuring seamless workflow and optimal results. 💻 Ready to streamline your robot programming projects and conquer complex geometry? Reach out to our team, and we'll be happy to assist: https://lnkd.in/eBgNPs5p 🔍 Ready to learn more? Checkout our webinar to explore more features of 𝘥𝘦𝘤𝘰𝘥𝘦: https://lnkd.in/ej7UxWaB 👩💻 Want to get started right away? Check out our tutorials here: https://lnkd.in/ejn8rw2x #Decode #HALRobotics #CADData #ComplexGeometry #RobotProgramming #Automation #Efficiency

🚀 Introducing Vention’s New Robot Power Pack! Connect to the Vention cloud robotics platform and give your robot superpowers: 🔹 Simulate programs with a cloud-based digital twin and deploy them in one click. 🔹 Unlock offline robot programming with a code-free interface and Python programming for advanced functions. 🔹 Remotely monitor your operations with real-time insights for maximum uptime and performance. 🔹 Access instant support from robotics experts with Remote Support and a dual camera pendant. The Robot Power Pack includes: 🔹 2 years of unlimited access to MachineLogic, MachineAnalytics, and Remote Support.  🔹 MachineMotion plug & play controller, pendant with camera, and robot safety modules. The Robot Power Pack is everything you need to power up your robotics project. Learn more: https://lnkd.in/eGkCMBgP #Vention #RobotPowerPack #automation #software #bundle #programming #deploying #operating #manufacturing #robots #hardware

I recently completed the 𝐒𝐭𝐚𝐭𝐞𝐟𝐥𝐨𝐰 𝐎𝐧𝐫𝐚𝐦𝐩 𝐜𝐨𝐮𝐫𝐬𝐞, and I'm excited to share what I learned! The course covered a wide range of topics, including: 𝐂𝐨𝐮𝐫𝐬𝐞 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰 𝐒𝐭𝐚𝐭𝐞 𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐬 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐞𝐟𝐥𝐨𝐰: Understanding and creating state machines in Stateflow. 𝐂𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐒𝐭𝐚𝐭𝐞𝐟𝐥𝐨𝐰 𝐂𝐡𝐚𝐫𝐭𝐬: The Stateflow Editor, transitions, conditions, states, temporal logic, and more. 𝐒𝐭𝐚𝐭𝐞𝐟𝐥𝐨𝐰 𝐒𝐲𝐦𝐛𝐨𝐥𝐬 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚: Understanding data and data scope. 𝐂𝐡𝐚𝐫𝐭 𝐀𝐜𝐭𝐢𝐨𝐧𝐬: Controlling chart execution with actions. 𝐂𝐡𝐚𝐫𝐭 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 𝐢𝐧 𝐒𝐢𝐦𝐮𝐥𝐢𝐧𝐤: Exploring the relationship between Stateflow chart execution and Simulink. 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Modeling supervisory control and driving modes for a robotic vacuum. 𝐅𝐥𝐨𝐰 𝐂𝐡𝐚𝐫𝐭𝐬: Using flow charts to model common logic patterns. 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝐒𝐭𝐚𝐭𝐞𝐟𝐥𝐨𝐰: Improving chart readability and reusing code with functions. 𝐂𝐡𝐚𝐫𝐭 𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐲:  Organizing charts using hierarchy. This course has provided me with valuable skills in creating and managing complex Stateflow charts in Simulink. I'm looking forward to applying these skills to real-world projects and furthering my learning journey. #Stateflow #Simulink #MATLAB #Robotics #MBSE #MBD #Modelbasedevelopment #lifelonglearning

Don't forget to join in today 25th of April the Visual Components webinar hosted by our own Mikael Saksi and Jyri Luhtio about how #simulation and robot offline programming (#OLP) can help you improve your manufacturing efficiency. It starts 14:00 CET / 08:00 Eastern time. #offlineprogramming #robotics #weldingrobots #3dsimulation #weldingautomation #robotwelding

🌟 𝐃𝐒𝐀 𝐏𝐫𝐨𝐛𝐥𝐞𝐦: Unique Paths 🌟 📣 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧: 📣 A robot is located at the top-left corner of an m x n grid (i.e. grid[0][0]). The robot can only move either down or right at any point in time. The robot is trying to reach the bottom-right corner of the grid (i.e. grid[m-1][n-1]). Given the two integers m and n, return the number of possible unique paths that the robot can take to reach the bottom-right corner. The test cases are generated so that the answer will be less than or equal to 2 * 10^9. ⭕ 𝘌𝘹𝘢𝘮𝘱𝘭𝘦 1: Input: m = 3, n = 7 Output: 28 ⭕ 𝘌𝘹𝘢𝘮𝘱𝘭𝘦 2: Input: m = 3, n = 2 Output: 3 Explanation: From the top-left corner, there are a total of 3 ways to reach the bottom-right corner: Right -> Down -> Down Down -> Down -> Right Down -> Right -> Down 〰 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧:〰 🔹 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡:🔹 ➖ Use dynamic programming to store the number of ways to reach each cell in the grid/matrix. ➖ Initialize the first row and first column to 1 since there's only one way to move straight across those cells ( Remember we can only go either left or down). ➖ Each cell that is not in the first row or the first column the number of ways to reach it is sum of the ways to reach the cell directly above it and the cell directly to the left of it. ➖ The value at the bottom-right cell of the grid/matrix will give the total number of unique paths. #DynamicProgramming #Algorithms #Programming #ProblemSolving #DSAChallenge #CodeSnippet ➿ 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐜𝐨𝐝𝐞 ➿

Don't forget to join next week the Visual Components webinar on improving your #manufacturing and production line efficiency using #3dsimulation and #robot offline programming (#olp). Recent research shows that monitoring daily production output is a top priority for production managers. Production Output is a critical KPI that reflects the efficiency, effectiveness, and overall health of the manufacturing process. Our own Mikael Saksi and Jyri Luhtio will go deeper into this topic and how #simulation and time-saving #offlineprogramming can significantly help you. #factoryofthefuture #simulationsoftware #robotics

💡 Day 76 of #100DaysOfProgrammingChallenge 🚀 Challenge Accepted: #62 Unique Paths ✨ Today, we're tackling the "Unique Paths" problem where a robot navigates an m x n grid! Problem Breakdown: A robot starts at the top-left corner of an m x n grid and needs to reach the bottom-right corner. The robot can only move either down or right at any step. We need to find the number of unique paths the robot can take to reach its destination. Example: - Input: m = 3, n = 7 - Output: 28 - Explanation: The robot can move in various ways, but there are exactly 28 unique paths from the start to the end. Steps to Solve: 1.Understand Movements: The robot can only move right or down, so each path consists of a combination of these moves. 2.Combinatorial Approach: The number of unique paths can be determined by counting combinations of moves. Specifically, you need (m - 1) down moves and (n - 1) right moves. 3. Calculate Paths: Use combinatorial mathematics to calculate the number of unique ways to arrange these moves. Example Walkthrough: Given m = 3 and n = 7: - The robot needs to make 2 down moves and 6 right moves. - The total number of moves is 2 + 6 = 8. - The number of unique ways to arrange these moves is calculated as combinations of choosing 2 down moves out of 8 total moves: C(8, 2) = 28. Ready to explore different paths and dive into combinatorial calculations? Let's get started! 💻 #CodingChallenge #Algorithms #UniquePaths #Programming

🤖 Exciting Project Update: Real-time Box Detection for Robotics Competition! 🏆 I've just completed a computer vision project that will be used for robotic arm automation. Here's what makes it special: ✅ Real-time box detection using OpenCV and Python ✅ Live video processing from camera feed ✅ Instant box measurement and visualization Key technical features: 1️⃣ Canny edge detection for robust object identification 2️⃣ Contour analysis to isolate and measure the box 3️⃣ Real-time bounding box drawing with dimension display This system is designed to work seamlessly with a robotic arm, pushing the boundaries of automated object manipulation. Potential applications span from advanced manufacturing to automated warehousing. The algorithm efficiently: - Captures live video - Applies Gaussian blur to reduce noise - Uses Canny edge detection to identify box edges - Finds and analyzes contours to locate the largest box - Calculates and displays box dimensions in real-time I'm thrilled to be part of a competition that drives innovation in robotics and computer vision. This project showcases the power of combining OpenCV's capabilities with custom Python scripting for real-world applications. Stay tuned for updates on how our robotic arm performs using this detection system! #ComputerVision #Robotics #AICompetition #OpenCV #Python #Innovation

This is one of those things that looks trivial when it's done like this but if you've ever had to do this any other way, you'll understand just how much of a benefit it is. 😴 Imagine moving the robot by hand (or joystick) to every single point along that curve or surface and clicking a button to save the position. We're talking about 1000s of clicks and motions versus maybe a dozen when you can use the CAD data and a tool like decode.

𝘥𝘦𝘤𝘰𝘥𝘦 allows the creation of complex toolpaths in seconds simply by selecting a curve or surface for the robot to follow. 🏎 Programming either of the examples below traditionally would require 10s or 100s of lines of code, and trying to do these with teach-and-repeat doesn't even bear thinking about. 🎛 What's more we keep these fully parametric so you can change how the toolpaths are generated at any time and we've included loads of tools to keep programming simple. Things like swapping from a concentric pattern to a zig-zag, keeping a tool perpendicular to a surface, adjusting your tool angles, and much more, are doable in a just couple of clicks. 👀 If you want to learn more we'll be hosting a webinar on 27th March at 14:00 GMT. Sign up here - https://lnkd.in/eBPYv3wz During this session, the HAL Robotics team will take you through the principles that drove 𝘥𝘦𝘤𝘰𝘥𝘦's development, show you how it works and give you the opportunity to ask any questions you might have. 👨🔬 Alternatively, if you want to get stuck in early there are getting started tutorials here - https://lnkd.in/ejn8rw2x #robotprogramming #robots #software #robotics #industrialrobots #cobots #manufacturing #ukmfg #lowcode #nocode