As significant advances have been made in the computation devices, the mechanical technologies and the autonomy science, the public has recently found that having unmanned aerial vehicles(UAVs) involved in our work and daily lives is within the near future. However, despite the maturity of estimation, control and structure techniques, such applications require efficient planning of trajectories for UAVs to safely operate in cluttered environments, which remains a challenging problem and thus is worth our efforts. In this thesis, 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 an initial...[ Read more ]

As significant advances have been made in the computation devices, the mechanical technologies and the autonomy science, the public has recently found that having unmanned aerial vehicles(UAVs) involved in our work and daily lives is within the near future. However, despite the maturity of estimation, control and structure techniques, such applications require efficient planning of trajectories for UAVs to safely operate in cluttered environments, which remains a challenging problem and thus is worth our efforts. In this thesis, 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 an initially unknown environment. During 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 are both 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 the 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 is therefore suitable for online target tracking with a 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.