  Gradient-based trajectory optimization (GTO) has gained wide popularity for
quadrotor trajectory replanning. However, it suffers from local minima, which
is not only fatal to safety but also unfavorable for smooth navigation. In this
paper, we propose a replanning method based on GTO addressing this issue
systematically. A path-guided optimization (PGO) approach is devised to tackle
infeasible local minima, which improves the replanning success rate
significantly. A topological path searching algorithm is developed to capture a
collection of distinct useful paths in 3-D environments, each of which then
guides an independent trajectory optimization. It activates a more
comprehensive exploration of the solution space and output superior replanned
trajectories. Benchmark evaluation shows that our method outplays
state-of-the-art methods regarding replanning success rate and optimality.
Challenging experiments of aggressive autonomous flight are presented to
demonstrate the robustness of our method. We will release our implementation as
an open-source package.

Robotics