Robotics Simulation with Python and Pybullet: Zero to Hero | Udemy

The most complete Python robotics simulation course on Udemy — from zero to building fully autonomous robots and industrial pick-and-place systems, all in PyBullet.

If you’ve ever wanted to learn robotics but didn’t know where to start — or you’re a developer looking to break into autonomous systems, robot arms, or factory automation — this is the course for you. No hardware needed. Just Python, PyBullet, and a computer.

MOBILE ROBOT — FROM SENSORS TO FULL AUTONOMY

You’ll start by building a mobile robot from the ground up. You’ll integrate real sensors including ultrasonic sensors, LiDAR, cameras, bumper sensors, and fall prevention sensors. Then you’ll implement PID-controlled line following using image processing with OpenCV.

From there, you’ll master the core algorithms every robotics engineer needs to know:

• Wheel Odometry — dead reckoning and encoder-based position estimation
• Particle Filter — probabilistic Monte Carlo localization
• A* Global Path Planning — optimal graph-search-based navigation on grid maps
• Dynamic Window Approach (DWA) — real-time local path planning and obstacle avoidance
• Maze Solving Algorithms — wall-following and custom maze environments
• Q-Learning Reinforcement Learning — train a robot to follow lines using reward-based AI

CAPSTONE PROJECT 1: AUTONOMOUS MOBILE ROBOT (AMR)

Build a complete autonomous navigation system using a TurtleBot in PyBullet:

• LiDAR-based occupancy grid mapping — explore the environment and build a map in real time
• A* path planning with obstacle inflation — safe, robot-radius-aware route generation
• Click-to-navigate interface — click a destination, the robot drives there autonomously
• Reactive obstacle avoidance — real-time LiDAR safety layer during navigation
• Particle filter localization integrated into the full system

ROBOT ARM — CONTROL, KINEMATICS, AND VISION

Switch to the world of robot manipulators. You’ll learn all three control modes — position, velocity, and torque control — and integrate end-effector cameras and force/torque sensors. Then go deep into kinematics:

• Forward Kinematics (Trigonometric method) — geometry-based end-effector calculation
• Forward Kinematics (Homogeneous Transformation Matrices & DH Parameters) — full 3D matrix-based approach
• Inverse Kinematics (Analytical / Geometric method) — closed-form solutions for 2D arms
• Inverse Kinematics (Jacobian method) — numerical iterative IK with damped least squares and singularity handling

Then move into vision-based control:

• Eye-to-Hand Pose Estimation — fixed camera, robot-camera calibration, coordinate transformation
• Eye-in-Hand Pose Estimation — dynamic moving camera with hand-eye calibration
• Image-Based Visual Servoing (IBVS) — real-time camera-guided control law for object manipulation, implemented on a UR robot

CAPSTONE PROJECT 2: PICK AND PLACE FACTORY AUTOMATION

Build a complete industrial pick-and-place production line — fully vibe-coded from scratch:

• Conveyor belt simulation with continuously moving objects
• Color-based object detection using OpenCV for sorting
• UR3 industrial robot arm — picking and placing with precision
• SCARA robot controller — high-speed assembly-style manipulation
• Dual-arm coordination — two robots working together on one production line
• Gripper simulation — realistic grasping and release sequencing