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Optimal sampling based motion planning and trajectory optimization are twocompeting frameworks to generate optimal motion plans. Both frameworks havecomplementary properties: Sampling based planners are typically slow toconverge, but provide optimality guarantees. Trajectory optimizers, however,are typically fast to converge, but do not provide global optimality guaranteesin nonconvex problems, e.g. scenarios with obstacles. To achieve the best ofboth worlds, we introduce a new planner, BITKOMO, which integrates theasymptotically optimal Batch Informed Trees (BIT*) planner with the K-OrderMarkov Optimization (KOMO) trajectory optimization framework. Our planner isanytime and maintains the same asymptotic optimality guarantees provided byBIT*, while also exploiting the fast convergence of the KOMO trajectoryoptimizer. We experimentally evaluate our planner on manipulation scenariosthat involve high dimensional configuration spaces, with up to two 7-DoFmanipulators, obstacles and narrow passages. BITKOMO performs better than KOMOby succeeding even when KOMO fails, and it outperforms BIT* in terms ofconvergence to the optimal solution.

BITKOMO: Combining Sampling and Optimization for Fast Convergence in Optimal Motion Planning