Computing optimal, collision-free trajectories for high-dimensional systems is a challenging problem.Samplingbased planners struggle with the dimensionality, whereas trajectory optimizers may get stuck in local minima due to inherent nonconvexities in the optimization landscape.The use of mixedinteger programming to encapsulate these nonconvexities and find globally optimal trajectories has recently shown great promise, thanks in part to tight convex relaxations and efficient approximation strategies that greatly …