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.
제공자:
로봇 공학 특화 과정펜실베이니아 대학교이 강좌에 대하여
최근 조회 7,513회
유연한 마감일
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공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 6개 강좌 중 5번째 강좌:
완료하는 데 약 15시간 필요
영어
자막: 영어, 스페인어, 중국어 (간체자)
귀하가 습득할 기술
- Particle Filter
- Estimation
- Mapping
유연한 마감일
일정에 따라 마감일을 재설정합니다.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 6개 강좌 중 5번째 강좌:
완료하는 데 약 15시간 필요
영어
자막: 영어, 스페인어, 중국어 (간체자)
제공자:
펜실베이니아 대학교
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.
강의 계획표 - 이 강좌에서 배울 내용
완료하는 데 4시간 필요
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.
완료하는 데 4시간 필요
9개 동영상 (총 52분), 3 개의 읽기 자료, 1 개의 테스트
완료하는 데 3시간 필요
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.
완료하는 데 3시간 필요
5개 동영상 (총 21분)
완료하는 데 4시간 필요
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.
완료하는 데 4시간 필요
6개 동영상 (총 36분)
완료하는 데 3시간 필요
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.
완료하는 데 3시간 필요
6개 동영상 (총 23분)
검토
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- 1 star4.27%
ROBOTICS: ESTIMATION AND LEARNING의 최상위 리뷰
MZ 제공2018년 3월 24일
Good materials for beginners. Assignments are interesting and useful. .
SG 제공2018년 3월 2일
Very succinct lectures which provides necessary foundation to learn advanced localization algorithms.
WW 제공2020년 10월 8일
The course is too difficult and the class is too short to understand, I have to spend a lot of this learn the knowledge needed in other place.
AS 제공2019년 11월 22일
Really good course. Engaging and relevant content. The assignments push you but test your fundamentals and you end up learning a lot.
로봇 공학 특화 과정 정보
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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