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

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.

References in corpus (8)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• TensorFlow: A system for large-scale machine learning
• Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics
• PythonRobotics: a Python code collection of robotics algorithms
• Learning to Plan Optimally with Flow-based Motion Planner
• Rapidly-exploring Random Forest: Adaptively Exploits Local Structure with Generalised Multi-Trees Motion Planning
• Parallelised Diffeomorphic Sampling-based Motion Planning
• Rapid Replanning in Consecutive Pick-and-Place Tasks with Lazy Experience Graph