Path planners based on basic rapidly-exploring random trees (RRTs) are quick and efficient, and thus favourable for real-time robot path planning, but are almost-surely suboptimal. In contrast, the optimal RRT (RRT*) converges to the optimal solution, but may be expensive in practice. Recent work has focused on accelerating the RRT*'s convergence rate. The most successful strategies are informed sampling, path optimisation, and a combination thereof. However, informed sampling and its combination with path optimisation have not been applied to the basic RRT. Moreover, while a number of path optimisers can be used to accelerate the convergence rate, a comparison of their effectiveness is lacking. This paper investigates the use of informed sampling and path optimisation to accelerate planners based on both the basic RRT and the RRT*, resulting in a family of algorithms known as optimised informed RRTs. We apply different path optimisers and compare their effectiveness. The goal is to ascertain if applying informed sampling and path optimisation can help the quick, though almost-surely suboptimal, path planners based on the basic RRT attain comparable or better performance than RRT*-based planners. Analyses show that RRT-based optimised informed RRTs do attain better performance than their RRT*-based counterparts, both when planning time is limited and when there is more planning time.

翻译：基于基本快速随机扩展树（RRT）的路径规划器具有快速高效的特点，因此适用于实时机器人路径规划，但几乎必然存在次优性。而最优RRT（RRT*）虽能收敛至最优解，但在实际应用中计算成本较高。近期研究聚焦于加速RRT*的收敛速度，其中最具成效的策略包括信息采样、路径优化及其组合。然而，信息采样及其与路径优化的组合尚未被应用于基本RRT。此外，尽管存在多种可用于加速收敛的路径优化器，但对其有效性的系统性比较尚属空白。本文研究将信息采样与路径优化技术应用于基于基本RRT和RRT*的规划器，形成一类称为"优化信息RRT"的算法族。我们采用不同的路径优化器并比较其有效性，旨在验证通过应用信息采样与路径优化，能否使基于基本RRT的快速但次优路径规划器达到甚至超越基于RRT*规划器的性能。分析表明，无论在有限规划时间还是充裕规划时间条件下，基于RRT的优化信息RRT均展现出优于其RRT*对应算法的性能表现。