Abstract:
Safe and autonomous operation of an Unmanned Aerial Vehicle (UAV) below the canopy
of a plantation forest is a challenging yet worthwhile task for precise forest management.
The research described in this thesis demonstrates the development and performance
evaluation of a UAV for autonomous below-canopy flights. The focus of this research was
on obstacle avoidance and motion estimation of the UAV, which are two critical tasks in
a successful autonomous operation.
A navigation block diagram for autonomous below-canopy flights was proposed in this
research and the environment in a typical New Zealand plantation forest was studied.
A below-canopy flight simulation environment, which includes two forest models, was
developed based on existing software.
Two obstacle avoidance (OA) controllers were implemented for autonomous below-canopy
operation: a Vector Field Histogram (VFH) OA controller and an Artificial Potential Field
(APF) OA controller. The implementations of the two OA controllers are slightly different
from conventional implementations for simplicity and better reliability in cluttered natural
environments. The performance of the two OA controllers was verified in the simulation
environment.
A velocity estimator was designed for the UAV. Raw velocities are estimated using the
Point-to-Line Iterative Corresponding Point (PLICP) scan matching algorithm. An Extended
Kalman Filter (EKF) is used to fuse the estimated raw velocities and readings
from other sensors to provide velocity measurements with better reliability and a higher
update rate.
The performance of the developed velocity estimator and the UAV system was quantified
in a series of indoor and outdoor experiments. The experimental results show that the
maximum RMS error in the velocity estimation was 0.0768 m/s and the developed UAV
successfully achieved autonomous flights both in the indoor test environment and in a
plantation forest. Factors which affect the performance of the velocity estimator or the
OA controllers are