Modified Mon Mar 27 09:23:59 2017 by Eric.Woldridge.
Challenge Problem #1: Quad-Rotor Sensor Fusion
Low cost Unmanned Aircraft Systems (UAS) are playing an expanding role in achieving missions critical to the US. The variety of missions impacted by improved UAS technology is extensive. The DoD has expressed a vision for the continued integration of UAS technology to assist in battlefield awareness, force application, protection, and logistics. Inexpensive UAS platforms are expected to be fundamental to achieving the monitoring goals defined by the United States Geological Survey. The National Oceanic and Atmospheric Association believes UAS platforms will revolutionize the capacity to monitor the global environment.
To achieve these goals fundamental advancements must be made that facilitate the development of sensing and control algorithms for UAS. At the heart of sensing and control of UAS are models that combine or “fuse” multiple sensors to predict the future state of the aircraft and the world it acts in. As a representative example system this challenge problem explores sensor fusion algorithms for vision guided control of quad-rotor aircraft.
The quad-rotor platform indirectly senses the state of the world. To sense the state of the world the platform relies on a variety of sensors including Inertial Measurement Units (IMU), magnetometers, GPS, and cameras. These sensors provide information about the attitude, direction of acceleration, location of the vehicle, and the structure of the world. The true state of the copter must be inferred from the sensor measurements.
The goal of PPS developers is to perform real time estimation of the state, including the location (x,y,z), roll, pitch, yaw, velocity, acceleration, and distance between the copter and surrounding objects, and to make predictions of future states. All of this must be performed while the copter is under control. Accurate estimation of the vector of state data is difficult due to the non-linear dynamics of the UAS system, the noisy behavior of the sensor suite, and time lag in the sensor recordings.
- QuadRotorPoster.pdf (885.0 KB) - added by Chris.Fahlbusch 3 years ago.
- QuadRotor.jpg (28.0 KB) - added by Chris.Fahlbusch 3 years ago.
- QuadRotor.png (14.9 KB) - added by Chris.Fahlbusch 3 years ago.
- QuadRotorProblem.pdf (3.8 MB) - added by Chris.Fahlbusch 3 years ago.
- MapDissimilarityQuantification.pdf (292.7 KB) - added by Chris.Fahlbusch 3 years ago.