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

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

Lin, Jiahao (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Cognitive Robotics)

de Croon, Guido (mentor)
Alonso Mora, Javier (mentor)
Ferrari, Riccardo M.G. (graduation committee)
Zhu, Hai (mentor)

Delft University of Technology

Mechanical Engineering | Systems and Control

2019-10-23

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.

Autonomous Navigation
Micro Aerial Vehicle
Obstacle Avoidance
Model Predictive Contrl

http://resolver.tudelft.nl/uuid:91f200f7-4966-4504-bc83-5a87e5550a91

Student theses

master thesis

© 2019 Jiahao Lin