sbp-env: Sampling-based Motion Planners' Testing Environment
It provides a tool for researchers in robotics and motion planning to accelerate algorithm development, though it is incremental as it builds on existing testing environments.
The paper introduces sbp-env, a framework for testing sampling-based motion planning algorithms by separating samplers and planners, enabling quick swapping of components to evaluate novel ideas.
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.