Advanced Command Algorithms for Robotics Engineers

Inverted Pendulum Control with PD, LQR & MPC in MATLAB ➡ Dynamic modeling of the inverted pendulum on a cart ➡ State-space representation of the cart–pole system ➡ PD controller for basic stabilization near upright equilibrium ➡ LQR optimal controller with energy-based swing-up control ➡ Model Predictive Control (MPC) for predictive stabilization ➡ Real-time cart–pole animation and simulation visualization ✨ Why this matters: The inverted pendulum is one of the most classic benchmark problems in control engineering because it represents a naturally unstable nonlinear system. To keep the pendulum balanced, the controller must continuously compute the correct control force to stabilize the system in real time. This simulation demonstrates how classical control and modern optimal control techniques can stabilize an unstable system. The project combines nonlinear dynamics, state-space modeling, and feedback control to visualize how different control strategies behave when stabilizing the inverted pendulum. These principles are widely used in robotics, aerospace systems, autonomous vehicles, and intelligent control applications. 📊 Key Highlights: ✔ Nonlinear dynamic modeling of the cart–pole system ✔ PD controller implementation for stabilization ✔ Energy-based swing-up controller with LQR balancing ✔ Model Predictive Control (MPC) implementation ✔ Real-time MATLAB simulation and animation ✔ Performance visualization of cart position and pendulum angle 💡 Future Potential: This framework can be extended toward: ➡ Comparison with PID and adaptive control strategies ➡ Reinforcement learning-based control ➡ Real-time sensor-based state estimation ➡ Hardware implementation using microcontrollers ➡ Advanced robotic stabilization systems 🔗 For students, engineers & robotics enthusiasts: This MATLAB simulation provides a practical framework for understanding nonlinear dynamics, optimal control, and predictive control strategies used in modern engineering systems. 🔁 Repost to support robotics research & engineering education! #Robotics #MATLAB #ControlSystems #InvertedPendulum #LQRControl #MPC #Automation #Mechatronics #EngineeringProjects #Simulation #RobotControl #STEM #EngineeringEducation #DynamicSystems #MATLABSimulation

End-to-end motion planning simulation built on the CARLA simulator that seamlessly integrates advanced path planning, smooth trajectory generation, and real-time vehicle control 🚗 🔑 Key Features: - Path Planning with RRT*: I implemented sampling-based algorithms (RRT, RRT*, and Informed RRT*) to compute collision-free paths in a simulated urban environment. By dynamically sampling the CARLA world and avoiding obstacles, the planner finds viable routes even in challenging scenarios. - Smooth Trajectory Generation: Using motion primitives based on cubic polynomial interpolation, the system generates smooth trajectories between waypoints. This ensures that the vehicle’s motion is both safe and comfortable. - PID Control for Real-Time Vehicle Guidance: A combination of longitudinal and lateral PID controllers has been designed to accurately follow the planned trajectory. The vehicle’s throttle, brake, and steering commands are continuously updated in real-time. Repository: https://lnkd.in/d7qMqD-p 📖 Learn More: CARLA Open-source simulator for autonomous driving research: https://carla.org/ Robotic Path Planning RRT and RRT*: https://lnkd.in/dTqpGfJt The PID Controller & Theory Explained: https://lnkd.in/dMHYQdJB #autonomousdriving #CARLA #PathPlanning #PIDControl #Simulation #Robotics #RRT #RRT*

📑 A Major Milestone in 𝗦𝗽𝗮𝘁𝗶𝗮𝗹 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴:  bridging the gap between 𝗟𝗟𝗠𝘀 and 𝟯𝗗 𝗦𝗰𝗲𝗻𝗲 𝗚𝗿𝗮𝗽𝗵𝘀 (3DSGs) for Advanced Real-World Navigation Traditional robotic systems struggle to interpret abstract commands and operate in expansive environments. For humans, a task like "grab a snack from the kitchen" is trivial, but it involves understanding vague instructions, knowing where things are, and planning the best way to get them—all of which remain significant challenges for robots operating in large, complex environments. How can we combine AI with detailed 3D maps of spaces to help AI systems and robots not only understand complex tasks in large environments but also adapt and refine their plans as they discover new information or face unexpected challenges? Excited to share this new research, introducing SayPlan - a framework that bridges the gap between Large Language Models (LLMs) and 3D Scene Graphs (3DSGs), setting a new standard for robotic task planning in complex, multi-room, and multi-floor spaces. How does it work? 🏢 1) Hierarchical Scene Representation: The framework leverages 3DSGs to represent environments hierarchically, from floors to rooms, assets, and individual objects. This allows the system to abstract and collapse unnecessary details, focusing only on task-relevant components. 🔍 2) Semantic Search: SayPlan employs LLMs to explore task-relevant subgraphs through iterative expansion and contraction, refining the scope of planning. For example, when asked to "fetch an item from the fridge," the system narrows its focus from the building to the kitchen and finally the fridge. 🔄 3) Iterative Replanning: Plans are verified against a simulator, which identifies errors like unfulfilled preconditions (e.g., forgetting to open a fridge). The LLM receives feedback to correct its output, ensuring that the final plan is executable and aligned with environmental constraints. 🗺️ 4) Path Optimization and Learning: Navigational tasks are optimized using algorithms like Dijkstra's, offloading computational complexity from the LLM. Industrial Implications SayPlan reduces 𝗶𝗻𝗽𝘂𝘁 𝘁𝗼𝗸𝗲𝗻 𝘀𝗶𝘇𝗲 𝗯𝘆 𝘂𝗽 𝘁𝗼 𝟴𝟮% using hierarchical graph compression and achieves 100% success in simple tasks and 86.6% for complex, multi-step plans. Iterative replanning resolves execution errors, ensuring near-perfect performance in tests. SayPlan exemplifies the potential of cutting-edge research in robotics and AI, demonstrating how 𝗟𝗟𝗠𝘀 𝗰𝗮𝗻 𝗱𝗼 𝗺𝘂𝗰𝗵 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝘁𝗲𝘅𝘁 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (😉) and how hierarchical environmental representations can create a scalable, reliable, and precise planning system. 👉 Follow me for more in-depth insights on Industrial #AI applications across sectors. Enno Danke Maria Danninger Christian Souche Amine Kharrat Simon Roggendorf Dr. Veo Zumpe Dr. Matthias Ziegler #AI #Robotics #TaskPlanning #Innovation #Automation #Research

The robotics algorithm library every engineer should know! 📚 PythonRobotics is an open-source collection of Python code and textbook for robotics algorithms, created by Atsushi Sakai. It has 27.2k stars on GitHub and 7k forks, so it's a no brainer to bookmark it! 🔖 The project covers everything from localization (EKF, particle filters, histogram filters) to SLAM (FastSLAM, ICP matching), path planning (A*, RRT*, Dijkstra, D*, potential fields, state lattice), path tracking (Stanley, LQR, MPC), arm navigation, aerial navigation, and even bipedal planning. What makes this special? It's designed to be easy to read and understand, with minimal dependencies and practical, widely-used algorithms. Each algorithm comes with visual animations, mathematical explanations, and working code. The documentation is essentially a full textbook on robotics algorithms, available free online at https://lnkd.in/dvuuVy6e. Requirements are simple: Python 3.13+, NumPy, SciPy, Matplotlib, and cvxpy. That's it. This a learning resource with 2,201 commits, contributions from 138 developers, and active maintenance. The animations alone (stored in a separate repo) are worth studying. If you're learning robotics, building autonomous systems, or teaching algorithms, this is the resource. It's MIT licensed, so you can use it freely in research or commercial projects. Here's the link: https://lnkd.in/dNX5JzkX P.S. This is what good open-source looks like: educational, practical, well-documented, and community-driven. Bookmark it. 🔖 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com

This paper employs viability theory to pre-compute safe sets in the state-space of joint positions and velocities. These viable sets, constructed via data-driven and analytical methods for self-collision avoidance, external object collision avoidance and joint-position and joint-velocity limits, provide constraints on joint accelerations and thus joint torques via the robot dynamics. A quadratic programming-based control framework enforces these constraints on a passive controller tracking a dynamical system, ensuring the robot states remain within the safe set in an infinite time horizon. The proposed approach is validated through simulations and hardware experiments on a 7-DoF Franka Emika manipulator. In comparison to a baseline constrained passive controller, this method operates at higher control-loop rates and yields smoother trajectories. #research: https://vpp-tc.github.io #authors: Zizhe Zhang, Yicong Wang, Zhiquan Zhang, Tianyu Li, Nadia Figueroa