ICUAS'17 Paper Abstract

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Battiston, Adrian (McGill University), Sharf, Inna (McGill University), Nahon, Meyer (McGill University)

Attitude Estimation for Normal Flight and Collision Recovery of a Quadrotor UAV

Scheduled for presentation during the "Sensor Fusion - I" (ThA5), Thursday, June 15, 2017, 11:20−11:40, San Marco Island

2017 International Conference on Unmanned Aircraft Systems, June 13-16, 2017, Miami Marriott Biscayne Bay, Miami, FL,

This information is tentative and subject to change. Compiled on April 12, 2021

Keywords Sensor Fusion, Fail-Safe Systems

Abstract

A comparison of attitude estimation algorithms is performed to allow selection of an appropriate algorithm for a quadrotor collision recovery system. A Multiplicative Extended Kalman Filter (MEKF), an Unscented Kalman Filter (UKF), a complementary filter, an H Infinity Filter, and adaptive varieties of the selected filters are chosen for comparison. The adaptive modifications to the estimation algorithms are developed to better estimate the attitude during a collision. The algorithms are compared in simulated normal flight as well as during a simulated collision in order to show which estimation algorithm provides the best quadrotor attitude estimate in all conditions. An approach to modify simulated Inertial Measurement Unit (IMU) data to match experimental data during a quadrotor collision is developed. The results show that slight improvements can be found using the adaptive algorithms and that overall, the UKF algorithms are found to outperform other estimators during regular flight and after a collision.

 

 

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