sbp-env: Sampling-based Motion Planners' Testing Environment

TL;DR

sbp-env is a flexible testing framework that enables rapid experimentation with various sampling-based motion planning algorithms by modularizing key components like samplers and planners.

Contribution

It introduces a modular framework that separates samplers and planners, facilitating quick testing and comparison of different sampling-based motion planning methods.

Findings

Enables rapid testing of different sampling-based algorithms

Supports flexible swapping of planning components

Facilitates research on sampling efficiency improvements

Abstract

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.