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

Master thesis (2019)

Authors

J. Lin Mechanical Engineering

Contributors

G.C.H.E. de Croon (supervisor 1)
J. Alonso-Mora Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering (supervisor 1)
Riccardo M.G. Ferrari Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering (supervisor 2)
H. Zhu Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering (supervisor 1)
Faculty
Mechanical Engineering
Copyright: © 2019 Jiahao Lin
Micro Aerial Vehicle Obstacle Avoidance Autonomous Navigation Model Predictive Contrl
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Published Date

23-10-2019

Language

English

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Faculty

Mechanical Engineering

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.