Examination Committee

Prof Ling SHI, ECE/HKUST (Chairperson)
Prof Shaojie SHEN, ECE/HKUST (Thesis Supervisor)
Prof Lu FANG, ECE/HKUST

Abstract

As significant advances arise in the computation devices, the mechanic technologies, and the autonomy science, the public recently finds that having Unmanned Aerial Vehicles(UAV) involving in our work and daily life is within the near future. However, despite the maturity of the estimation, control and aerodynamics techniques, such applications require efficiently planning trajectories for UAVs safely operating in the cluttered environments, which remains a challenging problem and thus is worth our efforts. We present an online method for generating collision-free trajectories for autonomous quadrotor flight through cluttered environments. We consider the real-world scenario that the quadrotor aerial robot is equipped with limited sensing and operates in initially unknown environments. During a flight, an octree-based environment representation is incrementally built using onboard sensors. Utilizing efficient operations in the octree data structure, we are able to generate free-space flight corridors consisting of large overlapping 3-D grids in an online fashion. A novel optimization-based method then generates smooth trajectories that both are bounded entirely within the safe flight corridor and satisfy higher order dynamical constraints. Our method computes valid trajectories within fractions of a second on a moderately fast computer, thus permitting online re-generation of trajectories for reaction to new obstacles. This method is also extended to address the challenging problem of tracking a moving target in cluttered environments using a quadrotor. Our online trajectory planning method generates smooth, dynamically feasible, and collision-free polynomial trajectories that follow a visually tracked moving target. As visual observations of the target are obtained, the target trajectory can be estimated and used to predict the target motion for a short time horizon. We propose a formulation to embed both limited horizon tracking error and quadrotor control costs in the cost function for a quadratic programming (QP), while encoding both collision avoidance and dynamical feasibility as linear inequality constraints for the QP. Our method generates tracking trajectories in the order of milliseconds and therefore suitable for online target tracking with limited sensing range. We build a complete quadrotor testbed with onboard sensing, state estimation, mapping, and control, and integrate the proposed method to demonstrate the effectiveness of our approach.