ScholarWorks@동국대학교

초록

Many RRT*-based sampling path planning algorithms consider neighboring nodes around a newly added node. The neighbor radius parameter rnear determines which nodes are included. The performance of RRT*-based algorithms can vary significantly with rnear. This variation can weaken generalization across environments. This paper quantitatively analyzes the effect of rnear on performance in sampling-based path planning for mobile robots in a warehouse environment. We evaluate RRT*-based algorithms by varying rnear. We then select the heuristic chosen rnear for each algorithm and compare the algorithms under the same conditions. Experiments are conducted in a warehouse environment with a fixed start position and five goal positions. Performance is evaluated using planning time, path length, and cumulative change in turning angle. Lower values indicate better performance for all three metrics. Based on the experimental results, we derive a heuristic value of rnear for each case. We also identify algorithm characteristics in computational efficiency and path quality under the heuristically chosen parameter settings. The final goal of this study is to provide quantitative evidence for selecting rnear in warehouse applications. We also present guidelines for parameter setting and algorithm selection for RRT*-based sampling path planning.

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