Interested in this College?
Get updates on Eligibility, Admission, Placements Fees Structure

Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study, Virtual Classroom	Video and Text Based

Course Overview

The recent technology of flying robots has been utilized for various applications like mini helicopters or quadrotors. They are widely used in 3D construction and aerial filming of industrial sites. The time calls for the need for machines that run without the supervision of pilots for hours. The Autonomous Navigation for Flying Robots certification course focuses on autonomous navigation for quadrotors. This course is provided by TUM for learners who are keen to learn the concept of autonomous navigation.

Autonomous Navigation for Flying Robots training will be delivered by industry experts with the help of instructor-paced learning videos, quizzes, and a browser-based quadrotor simulator. The duration of the course is 4 weeks and the learner must give at least 4 hours per week to complete the course on time. The learner shall be trained extensively on the browser-based quadrotor simulator and will be given practical training. The candidate shall also receive Autonomous Navigation for Flying Robots certification by EdX after the completion of the course.

The Highlights

100% online course
Instructor-paced learning
4 weeks duration
4 hours per week
Shareable certification
Multiple learning modes
Industry-experts led training

Programme Offerings

Online instructor-paced learning
Industry Expert Curriculum
multiple learning mode options
Certification
group discussions

Courses and Certificate Fees

Certificate Availability
no

A candidate can pay the Autonomous Navigation for Flying Robots certification fee on the EdX portal. The fee will be paid online mode using a credit card, debit card, and internet banking. This is a one-time payment that must be paid before starting the course. For a detailed fee structure refer to the table below.

Fee structure for Autonomous Navigation for Flying Robots

Name of the course	Fee in INR
Autonomous Navigation for Flying Robots	Rs. 3,831

Eligibility Criteria

Academic Qualification

A candidate taking up the Autonomous Navigation for Flying Robots classes should have a deep understanding of linear algebra, 3-D geometry, and python programming.

Certification Qualifying Details

The Autonomous Navigation for Flying Robots certification shall be awarded to the learner who shall complete the course and associated assignments on time. The certification shall open gates of opportunities for the learners in this domain.

What you will learn

The autonomous navigation for Flying Robots certification syllabus shall provide comprehensive knowledge on how to infer the position of the quadrotor from its sensor readings and how to navigate it along a trajectory. The course material is delivered by experts from TUM. This online course will provide learners with hands-on training practicals and a browser-based quadrotor simulator for the overall development of the learner. The course includes topics such as 3D geometry, probabilistic state estimation, visual odometry, SLAM, 3D mapping, and linear control. After the completion of the course, the learner shall develop a deep understanding of the following concepts.

Understand the flight principles of quadrotors and their application potential.
Specify the pose of objects in 3D space and perform calculations between them (e.g., compute the relative motion).
Explain the principles of Bayesian state estimation.
Implement and apply an extended Kalman filter (EKF), and select appropriate parameters for it.
Implement and apply a PID controller for state control and to fine-tune its parameters.
Understand and explain the principles of visual motion estimation and 3D mapping.

Who it is for

Robotics EngineerPython Programmer

Autonomous Navigation for Flying Robots certification benefits the people who are keen to be a part of this domain. The course shall benefit python programmers, robotics engineers, and engineers who wish to be a part of the robotics industry.

Admission Details

To get admission to the Autonomous Navigation for Flying Robots online course, follow the steps mentioned below.

Step 1: Visit the EdX portal and search for the course or click on the direct link

https://www.edx.org/course/autonomous-navigation-for-flying-robots.

Step 2: Click on the ‘Enrol Now’ Tab and register on the portal.

Step 3: Fill in the required details carefully and click on submit.

Step 4: Complete the enrollment process by paying the Autonomous Navigation for Flying Robots certification fee.

Step 5: Start learning by attending the Autonomous Navigation for Flying Robots classes.

Application Details

The candidate will be directed to the application form after he or she will click on the ‘Enrol Now’ tab after the completion of the course. The candidate must fill in the form carefully and sign up on the EdX portal. The direct link for the application form is available here.

https://authn.edx.org/register?course_id=course-v1%3ADelftX%2BPV5Ex%2B1T2020&enrollment_action=enroll&email_opt_in=false.

Instructors

Technical University of Munich, Munich Frequently Asked Questions (FAQ's)

1: What is the duration of the Autonomous Navigation for Flying Robots online course?

The duration of the course is 4 weeks, during which the candidate should give at least 4 hours to complete the course.

2: What are the methods adopted in this course for learning?

This is an instructor-paced online course that comprises classes by experts.

3: Who shall issue the certificate after the completion of the course?

The certificate shall be issued by the EdX at the end of the course.

4: What are the topics that are included in the curriculum of the course?

The course shall include topics like 3D geometry, probabilistic state estimation, visual odometry, SLAM, 3D mapping, and linear control. For the detailed curriculum refer to the article above.

5: What is the prerequisite for the course?

The candidate should have knowledge of linear algebra, 3-D algebra, and python programming for the course.