Course Summary
This course focuses on the fundamentals of robotics and how to apply artificial intelligence techniques to solve robotics problems. You'll learn about robot kinematics, localization, mapping, and control, and how to use probabilistic algorithms and machine learning to make robots more intelligent.Key Learning Points
- Learn the fundamentals of robotics and artificial intelligence techniques
- Apply probabilistic algorithms and machine learning to solve robotics problems
- Understand robot kinematics, localization, mapping, and control
Related Topics for further study
Learning Outcomes
- Understand the fundamentals of robotics and artificial intelligence
- Apply probabilistic algorithms and machine learning to solve robotics problems
- Develop skills in robot kinematics, localization, mapping, and control
Prerequisites or good to have knowledge before taking this course
- Knowledge of calculus and linear algebra
- Familiarity with Python programming language
Course Difficulty Level
IntermediateCourse Format
- Online, self-paced
- Video lectures
- Code exercises
Similar Courses
- Robotics: Vision Intelligence and Machine Learning
- Robotics
Related Education Paths
Notable People in This Field
- Rodney Brooks
- Helen Toner
Related Books
Description
Learn how to program all the major systems of a robotic car. Topics include planning, search, localization, tracking, and control.Outline
- lesson 1 Localization Localization Total Probability Uniform Distribution Probability After Sense Normalize Distribution Phit and Pmiss Sum of Probabilities Sense Function Exact Motion Move Function Bayes Rule Theorem of Total Probability lesson 2 Kalman Filters Gaussian Intro Variance Comparison Maximize Gaussian Measurement and Motion Parameter Update New Mean Variance Gaussian Motion Kalman Filter Code Kalman Prediction Kalman Filter Design Kalman Matrices lesson 3 Particle Filters Slate Space Belief Modality Particle Filters Using Robot Class Robot World Robot Particles lesson 4 Search Motion Planning Compute Cost Optimal Path First Search Program Expansion Grid Dynamic Programming Computing Value Optimal Policy lesson 5 PID Control Robot Motion Smoothing Algorithm Path Smoothing Zero Data Weight Pid Control Proportional Control Implement P Controller Oscillations Pd Controller Systematic Bias Pid Implementation Parameter Optimization lesson 6 SLAM (Simultaneous Localization and Mapping) Localization Planning Segmented Ste Fun with Parameters SLAM Graph SLAM Implementing Constraints Adding Landmarks Matrix Modification Untouched Fields Landmark Position Confident Measurements Implementing SLAM lesson 7 Runaway Robot Final Project
Summary of User Reviews
This Artificial Intelligence for Robotics course on Udacity has received positive reviews from many users. They have praised the course for being informative and engaging, with practical applications that help to reinforce learning. Many users have also appreciated the instructor's clear and concise teaching style.Key Aspect Users Liked About This Course
Many users have praised the practical applications of the course, which help to reinforce learning.Pros from User Reviews
- Informative and engaging course material
- Practical applications that reinforce learning
- Clear and concise teaching style by the instructor
Cons from User Reviews
- Some users have reported technical issues with the course platform
- The course may be challenging for beginners in AI or robotics
- The pace of the course may be too fast for some users
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