Robot Control Basics: A 16-Week Self-Study Journey
Course Syllabus
Course Description
Welcome to Robot Control Basics, a comprehensive 16-week self-study course meticulously crafted to guide you from foundational principles to practical application in the fascinating world of robotics. Designed for motivated beginners with a grasp of basic programming and mathematics, as well as intermediate learners seeking to solidify their understanding, this course demystifies how robots perceive, move, and execute complex tasks. We will journey through the core pillars of robotics, including kinematics, dynamics, trajectory planning, and control theory. By blending accessible theoretical explanations with practical, hands-on examples using Python and common simulation tools, this course bridges the gap between abstract concepts and real-world implementation. By the conclusion of this journey, you will possess a robust foundation in robot control, empowering you to analyze, simulate, and design basic robotic systems with confidence.
Primary Learning Objectives
Upon successful completion of this course, you will be able to:
Articulate the fundamental concepts of robot kinematics, dynamics, and control systems.
Mathematically model the position, orientation, and motion of robotic manipulators and mobile robots.
Design and implement smooth trajectories for robots to follow.
Apply fundamental feedback control strategies, such as PID control, to achieve desired robot behaviors.
Analyze the stability and performance of basic robotic control systems.
Implement core robot control algorithms in a simulated environment using Python.
Develop a complete robotic task, from planning to execution, in a capstone project.
Necessary Materials
A computer with a stable internet connection.
Working knowledge of a programming language. Python is strongly recommended and will be used for all examples. Familiarity with libraries like NumPy is beneficial.
A robotics simulation environment. We recommend CoppeliaSim (formerly V-REP), an accessible and powerful tool. Alternatives include Gazebo or PyBullet.
A foundational understanding of linear algebra (vectors, matrices) and calculus (derivatives, integrals). Review materials for key concepts will be suggested where applicable.
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Course Content: Weekly Lessons
Module 1: Foundations of Robotic Motion
Week 1: Lesson 1 – What is Robot Control?
Learning Objectives:
Define robot control and explain its critical importance in robotics.
Identify and categorize different types of robots and their applications.
Understand the fundamental components that constitute a robotic system.
Key Vocabulary:
Robot: An actuated mechanism programmable in two or more axes with a degree of autonomy, moving within its environment, to perform intended tasks.
Control System: The core logic, whether hardware or software, that manages, commands, and regulates the behavior of a robot to achieve a desired outcome.
Actuator: The muscle of a robot; a component responsible for moving or controlling a mechanism (e.g., electric motors, hydraulic pistons).
Sensor: The senses of a robot; a device that detects and responds to input from the physical environment (e.g., cameras, LiDAR, encoders).
End-Effector: The device at the end of a robotic arm, designed to interact with the environment (e.g., a gripper, a welding tool).
Content:
At its heart, robot control is the science and engineering of making a robot do what you want it to do, reliably and accurately. Think of it as the robot’s central nervous system. Without a control system, a robot is just a collection of inert metal and wires. It’s the controller that translates a high-level goal, like pick up the box, into the precise sequence of electrical signals sent to motors that makes it happen.
In this first lesson, we’ll survey the vast landscape of robotics. Robots aren’t just the humanoid machines of science fiction; they are all around us. We’ll explore common classifications:
Industrial Manipulators: The powerful, precise arms you see in factories (like a KUKA or FANUC arm).
Mobile Robots: Robots that move around, such as warehouse AGVs (Automated Guided Vehicles), planetary rovers, or your Roomba.
Humanoid Robots: Robots designed to mimic the human form, like Boston Dynamics’ Atlas.
Regardless of their form, all robots share a common architecture. They have a physical body (links, joints), actuators (motors) to create motion, sensors (cameras, encoders) to perceive themselves and the world, and the all-important control system to tie it all together. This week, we will deconstruct these elements to build a foundational mental model of how a complete robotic system operates.
Practical Hands-on Examples:
Research and identify three different types of robots currently used in industry (e.g., manufacturing, healthcare, logistics). For each, list its primary function, its key sensors, and its main actuators.
If you have a robotic vacuum cleaner or similar home robot, observe its behavior. Try to infer its control logic. How does it detect walls? How does it ensure it covers the whole room? What are its sensors and actuators?
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Week 2: Lesson 2 – Representing Robot Poses: Coordinate Frames and Transformations
Learning Objectives:
Grasp the concept of coordinate frames and their necessity in robotics.
Represent 2D and 3D position and orientation mathematically.
Combine multiple transformations using homogeneous transformation matrices.
Key Vocabulary:
Coordinate Frame: A reference system used to define the position and orientation of objects in space, typically denoted by {A} and consisting of an origin point and three mutually orthogonal axes (x, y, z).
Pose: The combined position and orientation of an object in space.
Translation: A linear shift of an object from one point to another without any change in its orientation. Represented by a vector.
Rotation Matrix: A 3×3 matrix that describes the rotation of a coordinate frame relative to another.
Homogeneous Transformation Matrix: A powerful 4×4 matrix that combines both a rotation matrix and a translation vector into a single matrix, simplifying the process of calculating poses.
Content:
Before we can command a robot to move to a specific spot, we need a universal language to describe that spot. This language is built on coordinate frames. Imagine you’re giving directions. You might say, Go 10 meters north and 5 meters east. You just used a coordinate frame with yourself as the origin. In robotics, we do the same. We attach a frame to the world (a fixed reference, `{W}`), a frame to the robot’s base `{B}`, and a frame to its end-effector `{E}`. Robot control is then about figuring out the relationship between these frames.
This relationship has two parts: position (translation) and orientation (rotation).
Position is simple: it’s a vector describing the displacement from one frame’s origin to another’s (e.g., `[dx, dy, dz]`).
Orientation is more complex: it describes how one frame is rotated relative to another. We represent this with a 3×3 rotation matrix.
The magic comes when we combine these into a single tool: the 4×4 Homogeneous Transformation Matrix. This matrix neatly packages a 3×3 rotation and a 3×1 translation vector. Its true power lies in composition. If you know the transformation from the base to link 1 (`T_1^0`) and from link 1 to link 2 (`T_2^1`), you can find the transformation from the base to link 2 simply by multiplying the matrices: `T_2^0 = T_1^0 T_2^1`. This is the mathematical backbone of all robot kinematics.
Practical Hands-on Examples:
Python Exercise: Using the NumPy library, create a 2D homogeneous transformation matrix for a point. Write a function that takes a point `[x, y]` and applies a 20-unit translation along the x-axis and a 45-degree rotation. Plot the original and transformed points to visualize the change.
Conceptual Task: Draw a simple 2-link robotic arm on paper. Assign a coordinate frame {0} to its base, {1} to its first joint, and {2} to its end-effector. Describe qualitatively (in words) the translation and rotation required to get from {0} to {1}, and from {1} to {2}.
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Week 3: Lesson 3 – Forward Kinematics: From Joints to Space
Learning Objectives:
Understand the core concept of forward kinematics.
Learn the Denavit-Hartenberg (DH) convention for systematically modeling robotic arms.
Calculate the end-effector’s pose given the joint angles for a simple manipulator.
Key Vocabulary:
Kinematics: The study of motion without considering the forces that cause it.
Forward Kinematics (FK): The problem of finding the pose (position and orientation) of the end-effector given the values of all the robot’s joint variables (e.g., angles for revolute joints).
Joint Space: The set of all possible combinations of joint variable values. An n-joint robot has an n-dimensional joint space.
Task Space (or Cartesian Space): The space in which the robot’s end-effector moves and performs its task, typically described by x, y, z coordinates and orientation.
* Denavit-Hartenberg (DH) Parameters: A standard convention that uses four parameters (link length, link twist, link offset, joint angle) to describe the geometric relationship between adjacent robot links, simplifying the derivation of transformation matrices.
Content:
Forward kinematics answers a fundamental question: If I know the angle of each of my robot’s joints, where is its hand? This is like knowing your shoulder, elbow, and wrist angles and being able to calculate the exact coordinate of your fingertip in the room. It maps a configuration from the