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 专项课程宾夕法尼亚大学课程信息
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学生职业成果
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完成这些课程后已开始新的职业生涯
43%
通过此课程获得实实在在的工作福利
20%
加薪或升职
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第 5 门课程（共 6 门）
完成时间大约为15 小时
英语（English）
字幕：英语（English）, 西班牙语（Spanish）, 中文（简体）
您将获得的技能
Particle FilterEstimationMapping
学生职业成果
40%
完成这些课程后已开始新的职业生涯
43%
通过此课程获得实实在在的工作福利
20%
加薪或升职
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 5 门课程（共 6 门）
完成时间大约为15 小时
英语（English）
字幕：英语（English）, 西班牙语（Spanish）, 中文（简体）
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.
教学大纲 - 您将从这门课程中学到什么
完成时间为 4 小时
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.
完成时间为 4 小时
9 个视频 （总计 52 分钟）, 3 个阅读材料, 1 个测验
完成时间为 3 小时
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.
完成时间为 3 小时
5 个视频 （总计 21 分钟）
完成时间为 4 小时
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.
完成时间为 4 小时
6 个视频 （总计 36 分钟）
完成时间为 3 小时
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.
完成时间为 3 小时
6 个视频 （总计 23 分钟）
审阅
来自ROBOTICS: ESTIMATION AND LEARNING的热门评论
由 SS 提供Apr 6, 2017
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Aug 6, 2016
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Feb 15, 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.
由 DA 提供Feb 5, 2021
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!
关于 机器人 专项课程
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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