Search-based versus Sampling-based Robot Motion Planning: A Comparative Study

TL;DR

This study compares sampling-based and search-based robot motion planning algorithms under identical conditions, revealing their relative strengths and limitations in high-dimensional spaces through a fair benchmarking process.

Contribution

It provides a fair benchmarking framework for different motion planning paradigms, highlighting their performance differences and potential advantages in various scenarios.

Findings

Sampling-based planners like RRT-Connect are more consistent in high-dimensional spaces.

Search-based planners like ARA* can outperform others with proper action-space sampling.

Benchmarking across paradigms requires careful adaptation to ensure fair comparison.

Abstract

Robot motion planning is a challenging domain as it involves dealing with high-dimensional and continuous search space. In past decades, a wide variety of planning algorithms have been developed to tackle this problem, sometimes in isolation without comparing to each other. In this study, we benchmark two such prominent types of algorithms: OMPL's sampling-based RRT-Connect and SMPL's search-based ARA* with motion primitives. To compare these two fundamentally different approaches fairly, we adapt them to ensure the same planning conditions and benchmark them on the same set of planning scenarios. Our findings suggest that sampling-based planners like RRT-Connect show more consistent performance across the board in high-dimensional spaces, whereas search-based planners like ARA* have the capacity to perform significantly better when used with a suitable action-space sampling scheme. Through this study, we hope to showcase the effort required to properly benchmark motion planners from different paradigms thereby contributing to a more nuanced understanding of their capabilities and limitations. The code is available at https://github.com/gsotirchos/benchmarking_planners