Spécialisation Robotique

Commence le Apr 10

Spécialisation Robotique

Learn the Building Blocks for a Career in Robotics

Gain experience programming robots to perform in situations and for use in crisis management

À propos de cette Spécialisation

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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courses
6 courses

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Projets

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Certificats

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Cours
Beginner Specialization.
No prior experience required.
  1. COURS 1

    Robotique : Robotique Aérienne

    Session à venir : Apr 10 — May 15.
    Sous-titres
    English

    À propos du cours

    Comment peut-on créer des aéronefs miniatures capables d'opérer de façon autonome aussi bien dans des environnements extérieurs qu'intérieurs ? Dans ce cours, vous apprendrez les bases de la mécanique du vol et comment réaliser des drones volants de type quadrirotor. Vous apprendrez à créer des modèles dynamiques, dériver les commandes et synthétiser un plan de vol dans un environnement en 3 dimensions. Vous serez exposé aux défis qui consistent à utiliser des mesures bruitées pour la localisation et le pilotage dans des milieux complexes et tri-dimensionnels. Enfin, vous serez exposé à des exemples pratiques et réels d'applications possibles et des futurs défis qui guettent l'industrie des drones en plein essor.
  2. COURS 2

    Robotics: Computational Motion Planning

    Session à venir : Apr 10 — May 15.
    Sous-titres
    English

    À propos du cours

    Robotic systems typically include three components: a mechanism which is capable of exerting forces and torques on the environment, a perception system for sensing the world and a decision and control system which modulates the robot's behavior to achieve the desired ends. In this course we will consider the problem of how a robot decides what to do to achieve its goals. This problem is often referred to as Motion Planning and it has been formulated in various ways to model different situations. You will learn some of the most common approaches to addressing this problem including graph-based methods, randomized planners and artificial potential fields. Throughout the course, we will discuss the aspects of the problem that make planning challenging.
  3. COURS 3

    Robotics: Mobility

    Session à venir : Apr 10 — May 15.
    Engagement
    4 semaines d'étude, 2 à 4 heures par semaine
    Sous-titres
    English

    À propos du cours

    How can robots use their motors and sensors to move around in an unstructured environment? You will understand how to design robot bodies and behaviors that recruit limbs and more general appendages to apply physical forces that confer reliable mobility in a complex and dynamic world. We develop an approach to composing simple dynamical abstractions that partially automate the generation of complicated sensorimotor programs. Specific topics that will be covered include: mobility in animals and robots, kinematics and dynamics of legged machines, and design of dynamical behavior via energy landscapes.
  4. COURS 4

    Robotics: Perception

    Session en cours : Mar 20 — Apr 24.
    Engagement
    4 weeks of study, 3-5 hours/week
    Sous-titres
    English

    À propos du cours

    How can robots perceive the world and their own movements so that they accomplish navigation and manipulation tasks? In this module, we will study how images and videos acquired by cameras mounted on robots are transformed into representations like features and optical flow. Such 2D representations allow us then to extract 3D information about where the camera is and in which direction the robot moves. You will come to understand how grasping objects is facilitated by the computation of 3D posing of objects and navigation can be accomplished by visual odometry and landmark-based localization.
  5. COURS 5

    Robotics: Estimation and Learning

    Session en cours : Mar 20 — Apr 24.
    Engagement
    4 weeks of study, 3-4 hours/week
    Sous-titres
    English, Chinese (Simplified)

    À propos du cours

    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.
  6. COURS 6

    Robotics: Capstone

    Session à venir : May 29 — Jul 17.
    Engagement
    6 weeks of study, 2-4 hours/week
    Sous-titres
    English

    À propos du Projet Final

    In our 6 week Robotics Capstone, we will give you a chance to implement a solution for a real world problem based on the content you learnt from the courses in your robotics specialization. It will also give you a chance to use mathematical and programming methods that researchers use in robotics labs. You will choose from two tracks - In the simulation track, you will use Matlab to simulate a mobile inverted pendulum or MIP. The material required for this capstone track is based on courses in mobility, aerial robotics, and estimation. In the hardware track you will need to purchase and assemble a rover kit, a raspberry pi, a pi camera, and IMU to allow your rover to navigate autonomously through your own environment Hands-on programming experience will demonstrate that you have acquired the foundations of robot movement, planning, and perception, and that you are able to translate them to a variety of practical applications in real world problems. Completion of the capstone will better prepare you to enter the field of Robotics as well as an expansive and growing number of other career paths where robots are changing the landscape of nearly every industry. Please refer to the syllabus below for a week by week breakdown of each track. Week 1 Introduction MIP Track: Using MATLAB for Dynamic Simulations AR Track: Dijkstra's and Purchasing the Kit Quiz: A1.2 Integrating an ODE with MATLAB Programming Assignment: B1.3 Dijkstra's Algorithm in Python Week 2 MIP Track: PD Control for Second-Order Systems AR Track: Assembling the Rover Quiz: A2.2 PD Tracking Quiz: B2.10 Demonstrating your Completed Rover Week 3 MIP Track: Using an EKF to get scalar orientation from an IMU AR Track: Calibration Quiz: A3.2 EKF for Scalar Attitude Estimation Quiz: B3.8 Calibration Week 4 MIP Track: Modeling a Mobile Inverted Pendulum (MIP) AR Track: Designing a Controller for the Rover Quiz: A4.2 Dynamical simulation of a MIP Peer Graded Assignment: B4.2 Programming a Tag Following Algorithm Week 5 MIP Track: Local linearization of a MIP and linearized control AR Track: An Extended Kalman Filter for State Estimation Quiz: A5.2 Balancing Control of a MIP Peer Graded Assignment: B5.2 An Extended Kalman Filter for State Estimation Week 6 MIP Track: Feedback motion planning for the MIP AR Track: Integration Quiz: A6.2 Noise-Robust Control and Planning for the MIP Peer Graded Assignment: B6.2 Completing your Autonomous Rover

Créateurs

  • Université de Pennsylvanie

    For over 150 years, Penn Engineering’s world-acclaimed faculty, state-of-the-art research laboratories and highly interdisciplinary curricula have offered a learning experience that is unparalleled. Having evolved in transformative ways to meet the technological opportunities and challenges of the 21st Century, the School’s educational philosophy has remained constant: to integrate current theory and hands-on experience with modern instrumentation and analytic techniques.

    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.

  • Vijay Kumar

    Vijay Kumar

    Nemirovsky Family Dean of Penn Engineering and Professor of Mechanical Engineering and Applied Mechanics
  • CJ Taylor

    CJ Taylor

    Professor of Computer and Information Science
  • Daniel E. Koditschek

    Daniel E. Koditschek

    Professor of Electrical and Systems Engineering
  • Daniel Lee

    Daniel Lee

    Professor of Electrical and Systems Engineering
  • Jianbo Shi

    Jianbo Shi

    Professor of Computer and Information Science
  • Kostas Daniilidis

    Kostas Daniilidis

    Professor of Computer and Information Science
  • Sid Deliwala

    Sid Deliwala

    Director, Electrical and Systems Engineering Labs and Lecturer, Electrical and Systems Engineering

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