Sampling-based motion planners' testing environment (sbp-env) is a full feature framework to quickly test different sampling-based algorithms for motion planning. sbp-env focuses on the flexibility of tinkering with different aspects of the framework, and had divided the main planning components into two categories (i) samplers and (ii) planners. The focus of motion planning research had been mainly on (i) improving the sampling efficiency (with methods such as heuristic or learned distribution) and (ii) the algorithmic aspect of the planner using different routines to build a connected graph. Therefore, by separating the two components one can quickly swap out different components to test novel ideas.

翻译：基于抽样的运动规划者测试环境(sbp-env)是快速测试不同抽样算法以进行运动规划的完整特征框架。 sbp-env侧重于与框架不同方面进行修补的灵活性,并将主要规划组成部分分为两类:(一) 取样员和(二) 规划者。动作规划研究的重点主要是:(一) 提高取样效率(采用超常或知识分布等方法)和(二) 规划员使用不同程序进行算法方面,以构建一个连接的图表。因此,通过将两个组成部分分开,一可以迅速将不同的组成部分转换为测试新想法。