sbp-env: A Python Package for Sampling-based Motion Planner and Samplers

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