Course Summary
Learn the fundamentals of robotics, including kinematics, motion planning, sensors, and control. This course is designed for both beginners and advanced learners, with hands-on activities and projects to reinforce your understanding.Key Learning Points
- Gain a foundational understanding of robotics and its applications
- Learn about kinematics, motion planning, sensors, and control
- Engage in hands-on activities and projects to reinforce your learning
Related Topics for further study
Learning Outcomes
- Develop a foundational understanding of robotics
- Apply kinematics, motion planning, sensors, and control to robotics projects
- Design and build your own robotic system
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming and mathematics
- Access to a computer with internet connection
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Hands-on
Similar Courses
- Introduction to Robotics
- Robotics: Aerial Robotics
- Robotics: Perception
Related Education Paths
Notable People in This Field
- Founder of iRobot
- Co-founder of iRobot
Related Books
Description
How can robots determine their state and properties of the surrounding environment from noisy sensor measurements in time? In this module you will learn how to get robots to incorporate uncertainty into estimating and learning from a dynamic and changing world. Specific topics that will be covered include probabilistic generative models, Bayesian filtering for localization and mapping.
Outline
- Gaussian Model Learning
- Course Introduction
- WEEK 1 Introduction
- 1.2.1. 1D Gaussian Distribution
- 1.2.2. Maximum Likelihood Estimate (MLE)
- 1.3.1. Multivariate Gaussian Distribution
- 1.3.2. MLE of Multivariate Gaussian
- 1.4.1. Gaussian Mixture Model (GMM)
- 1.4.2. GMM Parameter Estimation via EM
- 1.4.3. Expectation-Maximization (EM)
- MATLAB Tutorial - Getting Started with MATLAB
- Setting Up your MATLAB Environment
- Basic Probability
- Bayesian Estimation - Target Tracking
- WEEK 2 Introduction
- Kalman Filter Motivation
- System and Measurement Models
- Maximum-A-Posterior Estimation
- Extended Kalman Filter and Unscented Kalman Filter
- Mapping
- WEEK 3 Introduction
- Introduction to Mapping
- 3.2.1. Occupancy Grid Map
- 3.2.2. Log-odd Update
- 3.2.3. Handling Range Sensor
- Introduction to 3D Mapping
- Bayesian Estimation - Localization
- WEEK 4 Introduction
- Odometry Modeling
- Map Registration
- Particle Filter
- Iterative Closest Point
- Closing
Summary of User Reviews
Coursera's Robotics Learning course is a popular choice among users. Many users appreciate its comprehensive approach to teaching robotics and its practical applications.Key Aspect Users Liked About This Course
Comprehensive approach to teaching roboticsPros from User Reviews
- Great course for beginners and advanced learners
- Covers a wide range of topics in robotics
- Hands-on experience with real-life projects
- Well-structured and easy to follow
- Engaging and interactive content
Cons from User Reviews
- Some users found the course challenging
- Lack of personalized feedback from instructors
- Requires a significant time commitment
- Limited opportunities for networking and collaboration
- Not all topics may be relevant to everyone
Daniel Lee
4.3 Raiting
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