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Sampling-Based Optimal(SBO) path planning has been mainly used for roboticarm manipulation tasks. Several research works have been carried out in orderto evaluate performances of various SBO planners for arm manipulation. However,not much of work is available that highlights performances of SBO planners incontext of mobile robot navigation in outdoor 3D environments. This paperevaluates performances of major SBO planners in Open Motion PlanningLibrary(OMPL) for that purpose. Due to large number of existing SBO planners,experimenting and selecting a proper planner for a planning problem can beburdensome and ambiguous. SBO planner's probabilistic nature can also add abias to this procedure. To address this, we evaluate performances of allavailable SBO planners in OMPL with a randomized planning problem generationmethod iteratively. Evaluations are done in various state spaces suiting fordifferent differential constraints of mobile robots. The planning setups arefocused for navigation of mobile robots in outdoor environments. The outdoorenvironment representation is done with prebuilt OctoMaps, collision checks areperformed between a 3D box representing robot body and OctoMap for validationof sampled states. Several evaluation metrics such as resulting path's length,smoothness and status of acquired final solutions are selected. According toselected metrics, performances from different SBO planners are presentedcomparatively. Experimental results shows the significance of parallelcomputing towards quicker convergence rates for optimal solutions. Several SBOmethods that takes advantage of parallel computing produced better resultsconsistently in all state spaces for different planning inquiries.

Evaluation of Sampling-Based Optimizing Planners for Outdoor Robot Navigation