  This paper presents a two-step algorithm for online trajectory planning in
indoor environments with unknown obstacles. In the first step, sampling-based
path planning techniques such as the optimal Rapidly exploring Random Tree
(RRT*) algorithm and the Line-of-Sight (LOS) algorithm are employed to generate
a collision-free path consisting of multiple waypoints. Then, in the second
step, constrained quadratic programming is utilized to compute a smooth
trajectory that passes through all computed waypoints. The main contribution of
this work is the development of a flexible trajectory planning framework that
can detect changes in the environment, such as new obstacles, and compute
alternative trajectories in real time. The proposed algorithm actively
considers all changes in the environment and performs the replanning process
only on waypoints that are occupied by new obstacles. This helps to reduce the
computation time and realize the proposed approach in real time. The
feasibility of the proposed algorithm is evaluated using the Intel Aero
Ready-to-Fly (RTF) quadcopter in simulation and in a real-world experiment.
