  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
