Acerca de este Curso
4.2
292 ratings
76 reviews
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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Fechas límite flexibles

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Sugerido: 4 weeks of study, 3-4 hours/week

Aprox. 12 horas para completar
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English

Subtítulos: English, Chinese (Simplified)

Habilidades que obtendrás

Particle FilterEstimationMapping
Stacks
Globe

Cursos 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Calendar

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Clock

Sugerido: 4 weeks of study, 3-4 hours/week

Aprox. 12 horas para completar
Comment Dots

English

Subtítulos: English, Chinese (Simplified)

Programa - Qué aprenderás en este curso

1

Sección
Clock
4 horas para completar

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....
Reading
9 videos (Total: 52 min), 3 readings, 1 quiz
Video9 videos
WEEK 1 Introduction1m
1.2.1. 1D Gaussian Distribution8m
1.2.2. Maximum Likelihood Estimate (MLE)6m
1.3.1. Multivariate Gaussian Distribution7m
1.3.2. MLE of Multivariate Gaussian4m
1.4.1. Gaussian Mixture Model (GMM)4m
1.4.2. GMM Parameter Estimation via EM7m
1.4.3. Expectation-Maximization (EM)6m
Reading3 lecturas
MATLAB Tutorial - Getting Started with MATLAB10m
Setting Up your MATLAB Environment10m
Basic Probability10m

2

Sección
Clock
3 horas para completar

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....
Reading
5 videos (Total: 21 min), 1 quiz
Video5 videos
Kalman Filter Motivation4m
System and Measurement Models5m
Maximum-A-Posterior Estimation4m
Extended Kalman Filter and Unscented Kalman Filter4m

3

Sección
Clock
4 horas para completar

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....
Reading
6 videos (Total: 36 min), 1 quiz
Video6 videos
Introduction to Mapping7m
3.2.1. Occupancy Grid Map6m
3.2.2. Log-odd Update6m
3.2.3. Handling Range Sensor6m
Introduction to 3D Mapping8m

4

Sección
Clock
3 horas para completar

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....
Reading
6 videos (Total: 23 min), 1 quiz
Video6 videos
Odometry Modeling5m
Map Registration5m
Particle Filter4m
Iterative Closest Point5m
Closingm
4.2
Direction Signs

67%

comenzó una nueva carrera después de completar estos cursos
Briefcase

83%

consiguió un beneficio tangible en su carrera profesional gracias a este curso
Money

25%

consiguió un aumento de sueldo o ascenso

Principales revisiones

por VGFeb 16th 2017

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.

por NNJun 20th 2016

This is course is really helpful for beginners to understand how probability is useful in Robotics.Assignments are bit tough but worth the time .

Instructor

Daniel Lee

Professor of Electrical and Systems Engineering
School of Engineering and Applied Science

Acerca de University of Pennsylvania

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. ...

Acerca del programa especializado Robotics

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....
Robotics

Preguntas Frecuentes

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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