Rapidly Exploring Random Tree (RRT) algorithms are popular for sampling-based planning for nonholonomic vehicles in unstructured environments. However, we argue that previous work does not illuminate the challenges when employing such algorithms. Thus, in this article, we do a first comparison study of the performance of the following previously proposed RRT algorithm variants; Potential-Quick RRT* (PQ-RRT*), Informed RRT* (IRRT*), RRT* and RRT, for single-query nonholonomic motion planning over several cases in the unstructured maritime environment. The practicalities of employing such algorithms in the maritime domain are also discussed. On the side, we contend that these algorithms offer value not only for Collision Avoidance Systems (CAS) trajectory planning, but also for the verification of CAS through vessel behavior generation. Naturally, optimal RRT variants yield more distance-optimal paths at the cost of increased computational time due to the tree wiring process with nearest neighbor consideration. PQ-RRT* achieves marginally better results than IRRT* and RRT*, at the cost of higher tuning complexity and increased wiring time. Based on the results, we argue that for time-critical applications the considered RRT algorithms are, as stand-alone planners, more suitable for use in smaller problems or problems with low obstacle congestion ratio. This is attributed to the curse of dimensionality, and trade-off with available memory and computational resources.

翻译：快速探索随机树（RRT）算法因其在非结构化环境中对非完整运动体进行基于采样的规划而广受欢迎。然而，我们认为现有研究未能充分揭示应用此类算法所面临的挑战。为此，本文首次对先前提出的以下RRT算法变体在非结构化海洋环境中的多个案例进行单查询非完整运动规划性能比较研究：Potential-Quick RRT*（PQ-RRT*）、Informed RRT*（IRRT*）、RRT*和RRT。同时，本文还讨论了这些算法在海洋领域应用的实际问题。此外，我们认为这些算法不仅对避碰系统（CAS）的轨迹规划具有价值，还可通过船舶行为生成用于CAS的验证。自然，最优RRT变体通过考虑最近邻节点的树连接过程，能够生成更优距离的路径，但需以增加计算时间为代价。PQ-RRT*在提升调参复杂度和连接时间的前提下，仅取得略优于IRRT*和RRT*的结果。基于实验结果，我们认为在时间关键型应用中，所研究的RRT算法作为独立规划器，更适合用于较小规模问题或障碍物密度较低的问题。这归因于维度灾难以及可用内存与计算资源之间的权衡。