Aerial navigation in obstructed environments with embedded nonlinear model predictive control

We propose a methodology for autonomous aerial navigation and obstacle avoidance of micro aerial vehicles (MAVs) using non-linear model predictive control (NMPC) and we demonstrate its effectiveness with laboratory experiments. The proposed methodology can accommodate obstacles of arbitrary, potenti...

Full description

Saved in:
Bibliographic Details
Published in2019 18th European Control Conference (ECC) pp. 3556 - 3563
Main Authors Small, Elias, Sopasakis, Pantelis, Fresk, Emil, Patrinos, Panagiotis, Nikolakopoulos, George
Format Conference Proceeding
LanguageEnglish
Published EUCA 01.06.2019
Subjects
Online AccessGet full text
DOI10.23919/ECC.2019.8796236

Cover

More Information
Summary:We propose a methodology for autonomous aerial navigation and obstacle avoidance of micro aerial vehicles (MAVs) using non-linear model predictive control (NMPC) and we demonstrate its effectiveness with laboratory experiments. The proposed methodology can accommodate obstacles of arbitrary, potentially non-convex, geometry. The NMPC problem is solved using PANOC: a fast numerical optimization method which is completely matrix-free, is not sensitive to ill conditioning, involves only simple algebraic operations and is suitable for embedded NMPC. A c89 implementation of PANOC solves the NMPC problem at a rate of 20 Hz on board a lab-scale MAV. The MAV performs smooth maneuvers moving around an obstacle. For increased autonomy, we propose a simple method to compensate for the reduction of thrust over time, which comes from the depletion of the MAV's battery, by estimating the thrust constant.
DOI:10.23919/ECC.2019.8796236