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 7,445회
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)
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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- 4 stars20.57%
- 3 stars12.42%
- 2 stars4.07%
- 1 star4.27%
ROBOTICS: ESTIMATION AND LEARNING의 최상위 리뷰
VM 제공2018년 9월 18일
This is a really comprehensive course which gave me a good knowledge about Gaussian Model and Kalman Filter ...
SG 제공2016년 12월 13일
It's a great course. Although the assignment is little tough, you will gain a lot after completing it.
LZ 제공2016년 11월 26일
Week 1 and Week 3 are organized much better than Week 2 and Week 4. If you don't have enough time, I recommend that you focus on Week 1 and 3.
AV 제공2016년 6월 24일
A tough course with few hours of lecture material and some good programming assignments.You will be satisfied by those assignments however .
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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