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Keep shoppingPython-powered Manipulator Robotics
7 students
Python-powered Manipulator Robotics
"Kinematics, Modeling, Simulation, Trajectory Planning, and Workspace Analysis of Robotic Manipulators Using Python"
Created byDr A Vimala Starbino
Last updated 8/2025
English
What you'll learn
- Analyze and simulate the workspace and motion of standard manipulators in Cartesian, Cylindrical, and Polar coordinate systems using Python.
- Apply rotation matrices and Euler angles to model and simulate 3D motion of rigid bodies and coordinate transformations in space.
- Derive and implement forward kinematics and Jacobian matrices using DH parameters to evaluate workspace, motion, and singularities.
- Generate smooth joint space trajectories and solve inverse kinematics algebraically for manipulators using Python simulations.
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Course content
5 sections • 21 lectures • 7h 58m total length
Requirements
- This course is beginner-friendly with no strict prerequisites! A basic understanding of Python programming and high school-level math (especially vectors and matrices) will be helpful. Learners only need a computer with internet access — all coding can be done using Google Colab, so there's no need to install software. Most importantly, bring a curiosity to explore robotics and a willingness to learn by coding — I’ll guide you step-by-step!
Description
This hands-on course offers an engaging introduction to the world of robotic manipulators using Python. Designed for engineering students, robotics enthusiasts, and beginner coders, it provides a step-by-step guide through essential topics in robot motion and control.
Starting with the basics, you'll explore rigid body motion, coordinate transformations, and Euler angles. The course then delves into robot kinematics, where you’ll learn how to model robotic arms using Denavit–Hartenberg (DH) parameters and simulate their behavior through code. Interactive Python exercises help you compute forward kinematics, analyze the robot's reachable workspace, and detect singularities using the Jacobian matrix.
Beyond static analysis, the course introduces trajectory planning techniques to generate smooth, realistic motion for robot joints and end-effectors. You’ll also explore practical scenarios and learn how to implement path planning effectively. All implementations are carried out using Python in Google Colab, making it easy to learn without needing any software installations or hardware setup.
By the end of the course, you'll not only understand how robotic arms move and interact with their environment but also gain the practical coding skills to simulate, analyze, and control them. Whether you're preparing for robotics research, automation projects, or just exploring the field, this course will provide a strong foundation using accessible, hands-on programming with real-world relevance.
Who this course is for:
- This course is ideal for engineering students, hobbyists, and early-career professionals who are curious about robotics and want to learn how robotic manipulators work through hands-on programming. Whether you're from mechanical, electrical, electronics, or computer science background, if you're interested in simulating robot motion, understanding kinematics, or planning robotic tasks using Python, this course is for you. It’s especially valuable for learners who enjoy problem-solving and want to build practical skills in robotics through coding — no prior experience in robotics is required!