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.
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シラバス - 本コースの学習内容
週
14時間で修了
Gaussian Model Learning
We will learn about the Gaussian distribution for parametric modeling in robotics. The Gaussian distribution is the most widely used continuous distribution and provides a useful way to estimate uncertainty and predict in the world. We will start by discussing the one-dimensional Gaussian distribution, and then move on to the multivariate Gaussian distribution. Finally, we will extend the concept to models that use Mixtures of Gaussians.
9件のビデオ （合計52分）, 3 readings, 1 quiz
9件のビデオ
WEEK 1 Introduction1 分
1.2.1. 1D Gaussian Distribution8 分
1.2.2. Maximum Likelihood Estimate (MLE)6 分
1.3.1. Multivariate Gaussian Distribution7 分
1.3.2. MLE of Multivariate Gaussian4 分
1.4.1. Gaussian Mixture Model (GMM)4 分
1.4.2. GMM Parameter Estimation via EM7 分
1.4.3. Expectation-Maximization (EM)6 分
3件の学習用教材
MATLAB Tutorial - Getting Started with MATLAB10 分
Setting Up your MATLAB Environment10 分
Basic Probability10 分
週
23時間で修了
Bayesian Estimation - Target Tracking
We will learn about the Gaussian distribution for tracking a dynamical system. We will start by discussing the dynamical systems and their impact on probability distributions. This linear Kalman filter system will be described in detail, and, in addition, non-linear filtering systems will be explored.
5件のビデオ （合計21分）, 1 quiz
5件のビデオ
Kalman Filter Motivation4 分
System and Measurement Models5 分
Maximum-A-Posterior Estimation4 分
Extended Kalman Filter and Unscented Kalman Filter4 分
週
34時間で修了
Mapping
We will learn about robotic mapping. Specifically, our goal of this week is to understand a mapping algorithm called Occupancy Grid Mapping based on range measurements. Later in the week, we introduce 3D mapping as well.
6件のビデオ （合計36分）, 1 quiz
6件のビデオ
Introduction to Mapping7 分
3.2.1. Occupancy Grid Map6 分
3.2.2. Log-odd Update6 分
3.2.3. Handling Range Sensor6 分
Introduction to 3D Mapping8 分
週
43時間で修了
Bayesian Estimation - Localization
We will learn about robotic localization. Specifically, our goal of this week is to understand a how range measurements, coupled with odometer readings, can place a robot on a map. Later in the week, we introduce 3D localization as well.
6件のビデオ （合計23分）, 1 quiz
6件のビデオ
Odometry Modeling5 分
Map Registration5 分
Particle Filter4 分
Iterative Closest Point5 分
Closing45
4.2
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50%
コースが具体的なキャリアアップにつながった
20%
昇給や昇進につながった
人気のレビュー
The material is clearly presented. The Matlab exercises complement and reinforce the subject, the level of difficulty is well balanced, thanks for this great course.
This is course is really helpful for beginners to understand how probability is useful in Robotics.Assignments are bit tough but worth the time .
講師
Daniel Lee
Professor of Electrical and Systems EngineeringSchool of Engineering and Applied Science
ペンシルベニア大学（University of Pennsylvania）について
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
ロボット工学の専門講座について
The Introduction to Robotics Specialization introduces you to the concepts of robot flight and movement, how robots perceive their environment, and how they adjust their movements to avoid obstacles, navigate difficult terrains and accomplish complex tasks such as construction and disaster recovery. You will be exposed to real world examples of how robots have been applied in disaster situations, how they have made advances in human health care and what their future capabilities will be. The courses build towards a capstone in which you will learn how to program a robot to perform a variety of movements such as flying and grasping objects.
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