  Optimal sampling based motion planning and trajectory optimization are two
competing frameworks to generate optimal motion plans. Both frameworks have
complementary properties: Sampling based planners are typically slow to
converge, but provide optimality guarantees. Trajectory optimizers, however,
are typically fast to converge, but do not provide global optimality guarantees
in nonconvex problems, e.g. scenarios with obstacles. To achieve the best of
both worlds, we introduce a new planner, BITKOMO, which integrates the
asymptotically optimal Batch Informed Trees (BIT*) planner with the K-Order
Markov Optimization (KOMO) trajectory optimization framework. Our planner is
anytime and maintains the same asymptotic optimality guarantees provided by
BIT*, while also exploiting the fast convergence of the KOMO trajectory
optimizer. We experimentally evaluate our planner on manipulation scenarios
that involve high dimensional configuration spaces, with up to two 7-DoF
manipulators, obstacles and narrow passages. BITKOMO performs better than KOMO
by succeeding even when KOMO fails, and it outperforms BIT* in terms of
convergence to the optimal solution.
