Description
Master Robotics Simulation with Python and Pybullet. This course teaches complete robotics simulation with Python and the PyBullet environment, from building autonomous mobile robots to industrial robotic arm systems, and prepares participants to enter the world of autonomous systems. The Robotics Simulation with Python and PyBullet course is the most comprehensive robotics simulation course on the Udemy platform, taking learners from zero to building fully autonomous robots and industrial part handling systems. No physical hardware is required to learn this course, and all training is done using only the Python programming language, the PyBullet environment, and a personal computer. In this path, learners first learn how to build mobile robots and integrate a variety of sensors such as ultrasonic sensors, LiDAR, cameras, collision sensors, and fall prevention sensors, and then implement line-following algorithms using image processing and OpenCV. In the following sections of the course, key and fundamental robotics algorithms, including wheel routing, particle filter for probabilistic localization, global path planning with the A* algorithm, local path planning with the DWA method for obstacle avoidance, maze solving algorithms, as well as Q-Learning reinforcement learning, are taught in depth. The first final project of the course involves building an autonomous mobile robot (AMR) with lidar mapping capabilities, intelligent routing, and a click-guided user interface. The second part of the course focuses on robotic arms, different control modes, direct and inverse kinematics, and machine vision-based control. The second final project depicts an industrial automation production line including a conveyor belt, color recognition with OpenCV, UR3 and SCARA industrial robotic arms, and coordination of two robotic arms.
What you will learn
- Complete robot construction and simulation: Design and simulate a variety of mobile robots and robotic arms from scratch in the PyBullet environment.
- Implementing localization algorithms: Using advanced algorithms such as particle filters to determine the robot’s position.
- Path planning and autonomy: using the A* algorithm for general routing and the DWA method for real-time obstacle avoidance.
- Robotic arm kinematics calculation: Extraction and solution of direct and inverse kinematic equations using trigonometric, matrix, and Jacobian methods.
- Complete Autonomous Mobile Robot (AMR) Project: Building an autonomous system with LiDAR mapping and click-based navigation control.
- Image-guided camera control (IBVS): An implementation of image-based vision servomechanics for precise guidance of a robotic arm.
- Reinforcement Learning with Q-Learning: Training a robot to follow lines using artificial intelligence and reinforcement learning.
- Construction of a complete industrial automation line: Implementation of a parts handling system with conveyors, color recognition, and UR3 and SCARA industrial arms.
This course is suitable for people who:
- Students and robotics enthusiasts: People looking for a hands-on simulation experience without the need for physical hardware.
- Software Engineers and Python Developers: Programmers who plan to enter the field of robotics and autonomous systems.
- Researchers and graduate students: Those who need to rapidly prototyping and testing robotic algorithms.
- Self-directed learning enthusiasts: People interested in autonomous robots, robotic arms, and AI-powered navigation.
Course details: Master Robotics Simulation with Python and Pybullet
- Publisher: Udemy
- Instructor: Saroj Debnath
- Training level: Beginner to advanced
- Training duration: 19 hours and 57 minutes
- Number of lessons: 66
Course topics
Prerequisites for the Master Robotics Simulation with Python and Pybullet course
- A computer with at least 4GB RAM running Windows, Mac, or Linux
- Basic math understanding (trigonometry, linear algebra) — a high school level is sufficient
- No prior robotics or simulation experience required — everything is taught from the ground up
Course images
Sample course video
Installation Guide
After Extract, view with your favorite player.
Subtitles: None
Quality: 1080p
Download link
Rapidgator link
File(s) password: www.downloadly.ir
File size
16.3 GB