RMPD --一种递归中点位移路径规划算法
RMPD - A Recursive Mid-Point Displacement Algorithm for Path Planning
摘要：
出于什么是实时路径规划所需的，本文首先提出sRMPD，一个新的递归的“本地”规划器的关键概念，除非有必要的障碍，必须没有偏离任何两点之间的最短路径，这通常是一个直线路径的配置空间。随后，我们增加了功率sRMPD使用它作为一个“连接”的子例程调用在一个更高级别的基于采样的算法mRMPD的启发多RRT。因此，mRMPD在以较高密度的障碍物为特征的配置空间的区域中产生大量的空间探索树。总体效果是一种混合树生长策略，在基于多RRT的逻辑的随机探索和sRMPD的立即利用机会连接两个状态之间进行权衡。mRMPD规划器可以关于这种权衡有偏，以有效地解决不同种类的规划问题。基于我们已经运行的测试用例，我们的实验表明，mRMPD可以减少高达80%的规划时间相比，基本RRT。
英文摘要：
Motivated by what is required for real-time path planning, the paper starts out by presenting sRMPD, a new recursive "local" planner founded on the key notion that, unless made necessary by an obstacle, there must be no deviation from the shortest path between any two points, which would normally be a straight line path in the configuration space. Subsequently, we increase the power of sRMPD by using it as a "connect" subroutine call in a higher-level sampling-based algorithm mRMPD that is inspired by multi-RRT. As a consequence, mRMPD spawns a larger number of space exploring trees in regions of the configuration space that are characterized by a higher density of obstacles. The overall effect is a hybrid tree growing strategy with a trade-off between random exploration as made possible by multi-RRT based logic and immediate exploitation of opportunities to connect two states as made possible by sRMPD. The mRMPD planner can be biased with regard to this trade-off for solving different kinds of planning problems efficiently. Based on the test cases we have run, our experiments show that mRMPD can reduce planning time by up to 80% compared to basic RRT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
