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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ロボット工学専門講座ペンシルベニア大学（University of Pennsylvania）このコースについて
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次における6の5コース
約15時間で修了
英語
字幕：英語, スペイン語, 中国語（簡体）
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Particle FilterEstimationMapping
柔軟性のある期限
スケジュールに従って期限をリセットします。
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における6の5コース
約15時間で修了
英語
字幕：英語, スペイン語, 中国語（簡体）
提供：
ペンシルベニア大学（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.
シラバス - 本コースの学習内容
4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.
4時間で修了
9件のビデオ （合計52分）, 3 学習用教材, 1 個のテスト
3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.
3時間で修了
5件のビデオ （合計21分）
4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.
4時間で修了
6件のビデオ （合計36分）
3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.
3時間で修了
6件のビデオ （合計23分）
レビュー
ROBOTICS: ESTIMATION AND LEARNING からの人気レビュー
SSにより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.
SJにより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.
VGにより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.
DAにより2021年2月5日
This course was interesting but I think the video material was too shallow and not detailed enough. The assignment for Week 4 was extremely challenging!
ロボット工学専門講座について
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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