  Sampling-Based Optimal(SBO) path planning has been mainly used for robotic
arm manipulation tasks. Several research works have been carried out in order
to evaluate performances of various SBO planners for arm manipulation. However,
not much of work is available that highlights performances of SBO planners in
context of mobile robot navigation in outdoor 3D environments. This paper
evaluates performances of major SBO planners in Open Motion Planning
Library(OMPL) for that purpose. Due to large number of existing SBO planners,
experimenting and selecting a proper planner for a planning problem can be
burdensome and ambiguous. SBO planner's probabilistic nature can also add a
bias to this procedure. To address this, we evaluate performances of all
available SBO planners in OMPL with a randomized planning problem generation
method iteratively. Evaluations are done in various state spaces suiting for
different differential constraints of mobile robots. The planning setups are
focused for navigation of mobile robots in outdoor environments. The outdoor
environment representation is done with prebuilt OctoMaps, collision checks are
performed between a 3D box representing robot body and OctoMap for validation
of sampled states. Several evaluation metrics such as resulting path's length,
smoothness and status of acquired final solutions are selected. According to
selected metrics, performances from different SBO planners are presented
comparatively. Experimental results shows the significance of parallel
computing towards quicker convergence rates for optimal solutions. Several SBO
methods that takes advantage of parallel computing produced better results
consistently in all state spaces for different planning inquiries.
