Many RRT*-based sampling path planning algorithms consider neighboring nodes around a newly added node. The neighbor radius parameter determines which nodes are included. The performance of RRT*-based algorithms can vary significantly with . This variation can weaken generalization across environments. This paper quantitatively analyzes the effect of on performance in sampling-based path planning for mobile robots in a warehouse environment. We evaluate RRT*-based algorithms by varying . We then select the heuristic chosen 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 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 in warehouse applications. We also present guidelines for parameter setting and algorithm selection for RRT*-based sampling path planning.
Jeong et al. (Sat,) studied this question.