Automation & Robotics Roadmap

A journey from fundamental principles to designing and deploying intelligent automated systems.

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Phase Main Area Technologies & Concepts Objective
1. Foundations Math, Programming & Electronics
  • Math: Linear Algebra, Calculus, Probability
  • Programming: Python (NumPy), C++
  • Electronics: Basic Circuits, Microcontrollers (Arduino, Raspberry Pi), Sensors
Build a solid foundational knowledge of math, programming, and core electronic components.
2. Control Systems Control Theory & Simulation
  • Theory: PID Control, State Space
  • Software: MATLAB/Simulink, Gazebo
  • Hardware: Motors (DC, Servo), Actuators
Understand and apply automatic control principles to manage the behavior of mechanical systems.
3. Robot Mechanics Kinematics & Dynamics
  • Kinematics: Forward/Inverse Kinematics
  • Dynamics: Lagrangian Mechanics
  • Operating System: ROS (Robot Operating System)
Analyze the motion, forces, and torques of robot structures.
4. Robot Perception Computer Vision & Sensors
  • Vision: OpenCV, Image Processing
  • Sensors: LiDAR, 3D Cameras, IMU
  • Algorithms: Kalman Filter, SLAM
Enable robots to "see" and understand their environment to localize themselves and create maps.
5. Artificial Intelligence Machine Learning & Planning
  • Machine Learning: Reinforcement Learning, Supervised Learning
  • Frameworks: TensorFlow, PyTorch
  • Planning: A* Algorithm, RRT
Equip robots with the ability to learn from experience and make optimal decisions on their own.
6. Specialization Integration & Application
  • Fields: Industrial Robots, Autonomous Vehicles, Drones
  • Integration: PLC, Industrial Networks (EtherCAT)
  • Safety: Safety Standards (ISO 10218)
Apply knowledge to develop automation solutions for specific industries.

Core Mindset

1. Systems Thinking

A robot is a complex system of software, hardware, and mechanics. Understanding how they interact is key.

2. Safety First

Robots, especially industrial ones, can be dangerous. Safety protocols and risk assessments are critically important.

3. Persistent Debugging

Bridging the gap between simulation and reality is always challenging. Be prepared for continuous testing and debugging.

4. Real-World Problem Solving

Focus on creating robust, reliable, and efficient solutions for real-world tasks.