///Improving Multirotor Trajectory Tracking Performance using Learned Dynamics Models
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Field Robotics Center Seminar

April

3
Wed
Alexander Spitzer PhD Student Robotics Institute,
Carnegie Mellon University
Wednesday, April 3
11:00 am
- 12:00 pm
3305 Newell-Simon Hall
Improving Multirotor Trajectory Tracking Performance using Learned Dynamics Models

Abstract:

Multirotors and other aerial vehicles have recently seen a surge in popularity, partly due to a rise in industrial applications such as inspection, surveillance, exploration, package delivery, cinematography, and others. Crucial to multirotors’ successes in these applications, and enabling their suitability for other applications, is the ability to accurately track trajectories at high speed and high acceleration. In this talk, I will show how trajectories can be precisely tracked with a multirotor even in the presence of external disturbances, such as wind and varying payload, and modeling errors arising from for example, poor system identification and calibration, rotor degradation, and other unmodeled dynamics. We are able to achieve improved tracking performance by inverting an acceleration error dynamics model learned using incremental regression techniques. Dynamically inverting this model results in vehicle control inputs that are more precise, improving performance without requiring stiffer feedback gains. Simulation and hardware results will be presented that highlight the benefits of the proposed approach.

 

Bio:

Alex Spitzer is a Ph.D. student in the Resilient Intelligent Systems Lab (RISLab) at the Robotics Institute, advised by Professor Nathan Michael. His research focuses on combining machine learning techniques with optimization and control theory to improve robot performance. Prior to CMU, Alex received B.S. degrees in Computer Science and Electrical and Computer Engineering from Cornell University.