论文标题
BITKOMO：在最佳运动计划中结合采样和优化以快速收敛
BITKOMO: Combining Sampling and Optimization for Fast Convergence in Optimal Motion Planning
论文作者
论文摘要
基于最佳抽样的运动计划和轨迹优化是两个竞争框架，以生成最佳运动计划。这两个框架都有互补的属性：基于抽样的计划者通常会趋于收敛速度，但提供最佳保证。但是，轨迹优化器通常很快就可以收敛，但在非凸问题中不提供全球最佳保证，例如场景有障碍。为了达到两全其美，我们介绍了一个新的计划者Bitkomo，该计划者将渐近最佳的批量知情树（BIT*）计划者与K-order Markov优化（KOMO）轨迹优化框架集成在一起。我们的计划者随时随地，并保持BIT*提供的相同的渐近优化性保证，同时还利用KOMO轨迹优化器的快速收敛性。我们通过实验性地评估了我们的计划者的操纵场景，该方案涉及高维空间，最多有两个7多型操纵器，障碍物和狭窄的通道。即使Komo失败，Bitkomo的表现也比Komo更好，并且在收敛到最佳解决方案方面，它的表现优于Bit*。
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.
