  This paper presents a method for online trajectory planning in known
environments. The proposed algorithm is a fusion of sampling-based techniques
and model-based optimization via quadratic programming. The former is used to
efficiently generate an obstacle-free path while the latter takes into account
the robot dynamical constraints to generate a time-dependent trajectory. The
main contribution of this work lies on the formulation of a convex optimization
problem over the generated obstacle-free path that is guaranteed to be
feasible. Thus, in contrast with previously proposed methods, iterative
formulations are not required. The proposed method has been compared with
state-of-the-art approaches showing a significant improvement in success rate
and computation time. To illustrate the effectiveness of this approach for
online planning, the proposed method was applied to the fluid autonomous
navigation of a quadcopter in multiple environments consisting of up to two
hundred obstacles. The scenarios hereinafter presented are some of the most
densely cluttered experiments for online planning and navigation reported to
date. See video at https://youtu.be/DJ1IZRL5t1Q
