论文标题
基于抽样的运动动力学计划的州监督转向功能
State Supervised Steering Function for Sampling-based Kinodynamic Planning
论文作者
论文摘要
基于采样的运动计划者（例如RRT*和BIT*）应用于运动动力运动计划时，依赖于转向功能来生成连接采样状态的时间优势解决方案。实施精确的转向功能需要针对时间优势控制问题的分析解决方案，或者非线性编程（NLP）求解器以鉴于系统的动力学方程式解决边界值问题。不幸的是，对于许多实际域而言，分析解决方案不可用，而NLP求解器在计算上非常昂贵，因此快速，最佳的动力动力运动计划仍然是一个开放的问题。我们通过引入状态监督转向功能（S3F）来提供解决此问题的解决方案，这是一种学习时间优势转向功能的新方法。 S3F能够比其NLP对应物快于转向函数的数量级近乎最佳的解。在三个具有挑战性的机器人域进行的实验表明，使用S3F的RRT*在解决方案成本和运行时都显着优于最先进的计划方法。我们进一步提供了RRT*修改后的概率完整性证明。
Sampling-based motion planners such as RRT* and BIT*, when applied to kinodynamic motion planning, rely on steering functions to generate time-optimal solutions connecting sampled states. Implementing exact steering functions requires either analytical solutions to the time-optimal control problem, or nonlinear programming (NLP) solvers to solve the boundary value problem given the system's kinodynamic equations. Unfortunately, analytical solutions are unavailable for many real-world domains, and NLP solvers are prohibitively computationally expensive, hence fast and optimal kinodynamic motion planning remains an open problem. We provide a solution to this problem by introducing State Supervised Steering Function (S3F), a novel approach to learn time-optimal steering functions. S3F is able to produce near-optimal solutions to the steering function orders of magnitude faster than its NLP counterpart. Experiments conducted on three challenging robot domains show that RRT* using S3F significantly outperforms state-of-the-art planning approaches on both solution cost and runtime. We further provide a proof of probabilistic completeness of RRT* modified to use S3F.
