Rapidly Exploring Random Tree (RRT) algorithms, notably used for nonholonomic vehicle navigation in complex environments, are often not thoroughly evaluated for their specific challenges. This paper presents a first such comparison study of the variants Potential-Quick RRT* (PQ-RRT*), Informed RRT* (IRRT*), RRT*, and RRT, in maritime single-query nonholonomic motion planning. Additionally, the practicalities of using these algorithms in maritime environments are discussed and outlined. We also contend that these algorithms are beneficial not only for trajectory planning in Collision Avoidance Systems (CAS) but also for CAS verification when used as vessel behavior generators. Optimal RRT variants tend to produce more distance-optimal paths but require more computational time due to complex tree wiring and nearest neighbor searches. Our findings, supported by Welch`s t-test at a significance level of Alpha = 0.05, indicate that PQ-RRT* slightly outperform IRRT* and RRT* in achieving shorter trajectory length but at the expense of higher tuning complexity and longer run-times. Based on the results, we argue that these RRT algorithms are better suited for smaller-scale problems or environments with low obstacle congestion ratio. This is attributed to the curse of dimensionality, and trade-off with available memory and computational resources.

翻译：快速扩展随机树（RRT）算法常用于复杂环境中的非完整车辆导航，但其针对特定挑战的性能尚未得到充分评估。本文首次对Potential-Quick RRT*（PQ-RRT*）、Informed RRT*（IRRT*）、RRT*和RRT四种变体在海洋单查询非完整运动规划中进行比较研究，并讨论这些算法在海洋环境中的实际应用。我们提出，这些算法不仅可用于碰撞规避系统（CAS）的轨迹规划，还可作为船舶行为生成器用于CAS验证。最优RRT变体能够产生更符合距离最优的路径，但由于复杂的树结构连接和最近邻搜索，需要更长的计算时间。在显著性水平Alpha=0.05的Welch t检验支持下，我们的结果表明：PQ-RRT*在实现更短轨迹长度方面略优于IRRT*和RRT*，但代价是更高的调参复杂度和更长的运行时间。基于实验结果，我们认为这些RRT算法更适合小规模问题或障碍物密集度较低的环境，这归因于维度灾难以及可用内存与计算资源之间的权衡。