Real-time Vision-based Autonomous Navigation of MAV in Dynamic Environments

Master Thesis (2019)
Author(s)

J. Lin (TU Delft - Mechanical Engineering)

Contributor(s)

Guido Cornelis Henricus Eugene de Croon – Mentor (TU Delft - Control & Simulation)

J. Alonso-Mora – Mentor (TU Delft - Learning & Autonomous Control)

Riccardo Maria Giorgio Ferrari – Graduation committee member (TU Delft - Team Jan-Willem van Wingerden)

Hai Zhu – Mentor (TU Delft - Learning & Autonomous Control)

Faculty
Mechanical Engineering
Copyright
© 2019 Jiahao Lin
More Info
expand_more
Publication Year
2019
Language
English
Copyright
© 2019 Jiahao Lin
Graduation Date
23-10-2019
Awarding Institution
Delft University of Technology
Programme
Mechanical Engineering | Systems and Control
Faculty
Mechanical Engineering
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Safe navigation in unknown environments is a challenging task for autonomous Micro Aerial Vehicle (MAV) systems. Previous works generally avoid obstacles by assuming that the environment is static. The purpose of this thesis work is to develop a MAV system that can navigate autonomously and safely in dynamic environments. We present an onboard vision-based approach for the avoidance of moving obstacles in dynamic environments. This approach uses a state-of-art visual odometry algorithm to estimate the pose of MAV and an efficient obstacle sensing method based on stereo image pairs to estimate the center position, velocity, and size of the obstacles. Considering the uncertainties of the estimations, a chance-constrained Model Predictive Controller (MPC) is applied to achieve robust collision avoidance. The method takes into account the MAV’s dynamics, state estimation and the obstacle sensing results ensuring that the collision probability between the MAV and each obstacle is below a specified threshold. The proposed approach is implemented on a designed experimental platform that consists of a quadrotor, a depth camera, and a single-board computer, and is successfully tested in a variety of environments, showing effective online collision avoidance of moving obstacles.

Files

MscThesis_JiahaoLIN.pdf
(pdf | 4.46 Mb)
License info not available