  Quadrotors are agile flying robots that are challenging to control.
Considering the full dynamics of quadrotors during motion planning is crucial
to achieving good solution quality and small tracking errors during flight.
Optimization-based methods scale well with high-dimensional state spaces and
can handle dynamic constraints directly, therefore they are often used in these
scenarios. The resulting optimization problem is notoriously difficult to solve
due to its nonconvex constraints. In this work, we present an analysis of four
solvers for nonlinear trajectory optimization (KOMO, direct collocation with
SCvx, direct collocation with CasADi, Crocoddyl) and evaluate their performance
in scenarios where the solvers are tasked to find minimum-effort solutions to
geometrically complex problems and problems requiring highly dynamic solutions.
Benchmarking these methods helps to determine the best algorithm structures for
these kinds of problems.
