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...
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Published in | 2019 18th European Control Conference (ECC) pp. 3556 - 3563 |
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Main Authors | , , , , |
Format | Conference Proceeding |
Language | English |
Published |
EUCA
01.06.2019
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Subjects | |
Online Access | Get full text |
DOI | 10.23919/ECC.2019.8796236 |
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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. |
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DOI: | 10.23919/ECC.2019.8796236 |