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

TL;DR

BITKOMO is a novel motion planning algorithm that combines sampling-based and optimization-based methods to achieve fast convergence with optimality guarantees, effectively handling complex manipulation scenarios.

Contribution

The paper introduces BITKOMO, integrating BIT* and KOMO to leverage their strengths, providing a planner that is both asymptotically optimal and fast to converge.

Findings

BITKOMO outperforms KOMO in success rate.

BITKOMO converges faster than BIT*.

Effective in high-dimensional, obstacle-rich environments.

Abstract

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.