State Supervised Steering Function for Sampling-based Kinodynamic Planning
State Supervised Steering Function for Sampling-based Kinodynamic Planning
复制标题
DOI：
10.5555/3535850.3535856
复制
发表时间：
2022-06
期刊：
影响因子：
--
通讯作者：
P. Atreya;Joydeep Biswas
中科院分区：
文献类型：
--
作者：
P. Atreya;Joydeep Biswas
基于采样的运动规划器（例如 RRT* 和 BIT*）在应用于运动动力学运动规划时，依靠转向函数来生成连接采样状态的时间最优解决方案。实现精确的转向功能需要时间最优控制问题的解析解，或者非线性编程 (NLP) 求解器来求解给定系统运动动力学方程的边值问题。不幸的是，分析解决方案不适用于许多现实世界领域，并且 NLP 求解器的计算成本过高，因此快速且最优的运动动力学运动规划仍然是一个悬而未决的问题。我们通过引入状态监督转向函数（S3F）来解决这个问题，这是一种学习时间最优转向函数的新方法。 S3F 能够比 NLP 对应物更快地生成接近最优的转向函数解决方案。在三个具有挑战性的机器人领域进行的实验表明，使用 S3F 的 RRT* 在解决方案成本和运行时间方面均显着优于最先进的规划方法。我们进一步提供了修改为使用 S3F 的 RRT* 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.