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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机器人 專項課程宾夕法尼亚大学課程信息
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學生職業成果
40%
完成這些課程後已開始新的職業生涯
43%
通過此課程獲得實實在在的工作福利
20%
加薪或升職
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 5 門課程（共 6 門）
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完成時間大約為15 小時
英語（English）
字幕：英語（English）, 西班牙語（Spanish）, 中文（簡體）
您將獲得的技能
Particle FilterEstimationMapping
學生職業成果
40%
完成這些課程後已開始新的職業生涯
43%
通過此課程獲得實實在在的工作福利
20%
加薪或升職
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 5 門課程（共 6 門）
可靈活調整截止日期
根據您的日程表重置截止日期。
完成時間大約為15 小時
英語（English）
字幕：英語（English）, 西班牙語（Spanish）, 中文（簡體）
提供方
宾夕法尼亚大学
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.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 4 小時
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.
完成時間為 4 小時
9 個視頻 （總計 52 分鐘）, 3 個閱讀材料, 1 個測驗
9 個視頻
WEEK 1 Introduction1分鐘
1.2.1. 1D Gaussian Distribution8分鐘
1.2.2. Maximum Likelihood Estimate (MLE)6分鐘
1.3.1. Multivariate Gaussian Distribution7分鐘
1.3.2. MLE of Multivariate Gaussian4分鐘
1.4.1. Gaussian Mixture Model (GMM)4分鐘
1.4.2. GMM Parameter Estimation via EM7分鐘
1.4.3. Expectation-Maximization (EM)6分鐘
3 個閱讀材料
MATLAB Tutorial - Getting Started with MATLAB10分鐘
Setting Up your MATLAB Environment10分鐘
Basic Probability10分鐘
完成時間為 3 小時
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.
完成時間為 3 小時
5 個視頻 （總計 21 分鐘）
5 個視頻
Kalman Filter Motivation4分鐘
System and Measurement Models5分鐘
Maximum-A-Posterior Estimation4分鐘
Extended Kalman Filter and Unscented Kalman Filter4分鐘
完成時間為 4 小時
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.
完成時間為 4 小時
6 個視頻 （總計 36 分鐘）
6 個視頻
Introduction to Mapping7分鐘
3.2.1. Occupancy Grid Map6分鐘
3.2.2. Log-odd Update6分鐘
3.2.3. Handling Range Sensor6分鐘
Introduction to 3D Mapping8分鐘
完成時間為 3 小時
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.
完成時間為 3 小時
6 個視頻 （總計 23 分鐘）
6 個視頻
Odometry Modeling5分鐘
Map Registration5分鐘
Particle Filter4分鐘
Iterative Closest Point5分鐘
Closing45
審閱
來自ROBOTICS: ESTIMATION AND LEARNING的熱門評論
由 SS 提供2017年4月6日
Leanring of mechanism and implementation of Kalman filter and particle filter from experiment is very interesting for me. And these method let me know more about map building in SLAM framework.
由 SJ 提供2016年8月6日
Lesson 1 and Lesson 3 are clear. However, homework in Lesson 2 and Lesson 4 is hard to finish because of too few materials in the lesson. Overall, it is a fairly good course.
由 VG 提供2017年2月15日
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.
由 NN 提供2016年6月19日
This is course is really helpful for beginners to understand how probability is useful in Robotics.Assignments are bit tough but worth the time .
關於 机器人 專項課程
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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