Robotics Simulation with Python and Pybullet: Zero to Hero | Udemy [Update 04/2026]
English | Size: 16.7 GB
Genre: eLearning[/center]
Learn PyBullet, Python robotics, path planning, particle filter, kinematics, visual servoing, and mobile robots
What you'll learn
Build and simulate mobile robots and robot arms using PyBullet from scratch
Implement localization algorithms including Particle Filter
Apply A* global path planning and Dynamic Window Approach (DWA) for autonomous navigation
Derive and solve Forward and Inverse Kinematics using trigonometric, matrix, and Jacobian methods
Build a complete Autonomous Mobile Robot (AMR) with LiDAR mapping and click-to-navigate control
Implement Image-Based Visual Servoing (IBVS) for camera-guided robot arm control
Train a robot to follow lines using Q-learning reinforcement learning
Build a full pick-and-place system with conveyor belt, color detection, and industrial robot arms (UR3, SCARA)
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
Who this course is for:
Robotics students and enthusiasts who want hands-on simulation experience without physical hardware
Software engineers and Python developers looking to break into the robotics field
Researchers and graduate students who need to prototype and test robotics algorithms quickly
Hobbyists and self-learners curious about autonomous robots, robot arms, and AI-driven navigation
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