Welcome to Week 1! In this week, you will be introduced to the exciting field of Unmanned Aerial Robotics (UAVs) and quadrotors in particular. You will learn about their basic mechanics and control strategies and realize how careful component selection and design affect the vehicles’ performance. This week also provides you with instructions on how to download and install Matlab. This software will be used throughout this course in exercises and assignments, so it is strongly recommended to familiarize yourself with Matlab soon. Tutorials to help you get started are also provided in this week.
1.1 Unmaned Aerial Vehicles Cours 1
UNIVERSITY OF PENNSYLVANIA Robotics: Aerial Robotics
How can we create agile micro aerial vehicles that are able to operate autonomously in cluttered indoor and outdoor environments? You will gain an introduction to the mechanics of flight and the design of quadrotor flying robots and will be able to develop dynamic models, derive controllers, and synthesize planners for operating in three-dimensional environments. You will be exposed to the challenges of using noisy sensors for localization and maneuvering in complex, three-dimensional environments. Finally, you will gain insights through seeing real-world examples of the possible applications and challenges for the rapidly-growing drone industry. Mathematical prerequisites: Students taking this course are expected to have some familiarity with linear algebra, single variable calculus, and differential equations. Programming Prerequisites: Some experience programming with MATLAB or Octave is recommended (we will use MATLAB in this course.) MATLAB will require the use of a 64-bit computer.
Nemirovsky Family Dean of Penn Engineering and Professor of Mechanical Engineering and Applied Mechanics
University of Pennsylvania
Aggressive Flight 2017
This video presents an autonomous 250 g quadrotor performing aggressive maneuvers using a Qualcomm snapdragon flight and relying only on onboard computation and sensor capabilities. The control planning and estimation tasks are solved based on the information provided by a single camera and an IMU.
We show aggressive trajectories around poles and narrow window gaps at different inclinations. Our system is able to traverse narrow gaps requiring accelerations up to 1.5 g and roll and pitch angles up to 90 degrees with velocities of 5 m/s.
The current approach does not require any switching control strategy and it is fully based on the information of only a single camera and IMU. This is the first time that aggressive maneuvers are solved with such a small footprint vehicle, using only onboard sensors and without relying on external motion capture systems.