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.
Este curso forma parte de Programa especializado: Robótica
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Programa especializado: RobóticaUniversidad de PensilvaniaAcerca de este Curso
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Curso 5 de 6 en
Aprox. 15 horas para completar
Inglés (English)
SubtÃtulos: Inglés (English), Español (Spanish), Chino (simplificado)
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Prueba Coursera para negociosHabilidades que obtendrás
- Particle Filter
- Estimation
- Mapping
Fechas lÃmite flexibles
Restablece las fechas lÃmite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 5 de 6 en
Aprox. 15 horas para completar
Inglés (English)
SubtÃtulos: Inglés (English), Español (Spanish), Chino (simplificado)
¿PodrÃa tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?
Prueba Coursera para negocios
ofrecido por
Universidad de Pensilvania
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.
Programa - Qué aprenderás en este curso
4 horas para completar
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 horas para completar
9 videos (Total 52 minutos), 3 lecturas, 1 cuestionario
3 horas para completar
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 horas para completar
5 videos (Total 21 minutos)
4 horas para completar
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 horas para completar
6 videos (Total 36 minutos)
3 horas para completar
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 horas para completar
6 videos (Total 23 minutos)
Reseñas
- 5 stars58,53Â %
- 4 stars20,73Â %
- 3 stars12,39Â %
- 2 stars4,06Â %
- 1 star4,26Â %
Principales reseñas sobre ROBOTICS: ESTIMATION AND LEARNING
por HA6 de jun. de 2019
Course content needs researching on the internet as well. And course assignments are good learning experience but need research too.
por SG13 de dic. de 2016
It's a great course. Although the assignment is little tough, you will gain a lot after completing it.
por SJ6 de ago. de 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.
por NN19 de jun. de 2016
This is course is really helpful for beginners to understand how probability is useful in Robotics.Assignments are bit tough but worth the time .
Acerca de Programa especializado: Robótica
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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