mlsanityData to 7 Oct 2026

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

arXiv 2110.08402 ↗First posted 15 Oct 2021cs.RO cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 25 references that are in the map. First posted on arXiv 15 Oct 2021.

Builds on

9 of 25 references.

References · 9

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2020Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristicscited by 20
  3. 2018PythonRobotics: a Python code collection of robotics algorithmscited by 12
  4. 2018Balancing Global Exploration and Local-connectivity Exploitation with Rapidly-exploring Random disjointed-Treescited by 8
  5. 2019Bayesian Local Sampling-based Planningcited by 7
  6. 2020Learning to Plan Optimally with Flow-based Motion Plannercited by 3
  7. 2021Rapidly-exploring Random Forest: Adaptively Exploits Local Structure with Generalised Multi-Trees Motion Planningcited by 3
  8. 2019Cooperative Pathfinding based on memory-efficient Multi-agent RRT*cited by 2
  9. 2021Rapid Replanning in Consecutive Pick-and-Place Tasks with Lazy Experience Graphcited by 1

Built on by

Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Discovering Multiple Algorithm Configurationscited by 1