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

TL;DR

This study benchmarks two fundamentally different robot motion planning paradigms under unified conditions: sampling-based RRT-Connect and search-based ARA* with motion primitives. It demonstrates that RRT-Connect offers more consistent performance in high-dimensional settings, while ARA* can achieve substantially faster planning with carefully tailored action-space sampling, albeit with sensitivity to collision models. The authors implement cross-framework compatibility, analyze the impact of collision representations, and provide insights into fair benchmarking of diverse planners. The findings inform when to favor variability-friendly sampling methods versus customization-enabled search methods in practical robotics contexts.

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

Figures (8)

• Figure 1: Generic elements of robot motion planning algorithms.
• Figure 2: Planning elements of Sample/Decompose-then-Search algorithms.
• Figure 3: Generic elements of RRT algorithm.
• Figure 4: Generic elements of search-and-sample algorithms.
• Figure 5: Generic elements of Trajectory Optimization.