All material regarding the Aerial Robotics: Path Planning and State Estimation 2021 can be found in this repository. The outline of this repository:
- Week wise split content.
- Each week will be accompanied by its own
README,RESOURCES,ASSIGNMENTmardown files. - Every week, there will be some resource put on the
RESOURCESfile. This can be updated during the progress of the week to help solve some doubts. - After the completion of a week, an Assignment will be given. The Questions and Submission of the Assignment will be mentioned in the
ASSIGNMENTfile itself. - Wherever possible the Assignments will be given using Colab notebooks so that the hassle of installing various packages etc. be eliminated.
This course is a primer to anybody interested in Aerial Robotics. The course introduces Path planning in 3D and State Estimation for Aerial Robots, with the aim of setting students in the right direction for further education in the field of Aerial Robotics.
The course starts off with the basics of Python programming, followed by introduction to path planning in two dimensions as a precursor to the same in 3D. Path Planning in 3D will be dealt with in greater detail. The course then moves on to State Estimation and Odometry for Aerial vehicles in 3 Dimensions.
Every week, Python notebooks shall be put up in the Course Repository. The notebooks are intended to ease the students’ work of writing algorithms from scratch. Every notebook shall be considered for final Evaluation. Research paper discussion sessions shall be conducted, where state-of-the-art algorithms shall be discussed.
- Week I: Basics of Python Programming.
- Week II: Seach based Path Planning.
- Week III: Sampling based Path Planning.
- Week IV: Introduction to Kalman and Bayesian Filters.
- Week V: State Estimation Machinery.
- Week VI: Final Project. You can refer to the course handout for more information.