When facing a new motion-planning problem, most motion planners solve it from scratch, e.g., via sampling and exploration or starting optimization from a straight-line path. However, most motion planners have to experience a variety of planning problems throughout their lifetimes, which are yet to be leveraged for future planning. In this paper, we present a simple but efficient method called Motion Memory, which allows different motion planners to accelerate future planning using past experiences. Treating existing motion planners as either a closed or open box, we present a variety of ways that Motion Memory can contribute to reduce the planning time when facing a new planning problem. We provide extensive experiment results with three different motion planners on three classes of planning problems with over 30,000 problem instances and show that planning speed can be significantly reduced by up to 89% with the proposed Motion Memory technique and with increasing past planning experiences.

翻译：当面对一个新的运动规划问题时，大多数运动规划器会从头开始求解，例如通过采样和探索，或从直线路径开始优化。然而，大多数运动规划器在其生命周期中会经历多种规划问题，这些经验尚未被用于未来的规划。在本文中，我们提出一种简单而高效的方法，称为运动记忆（Motion Memory），它使不同的运动规划器能够利用过去的经验加速未来的规划。将现有运动规划器视为封闭或开放的盒子，我们展示了运动记忆在面临新规划问题时能够减少规划时间的多种方式。我们提供了广泛的实验结果，涉及三个不同运动规划器在三类规划问题上的超过30,000个问题实例，结果显示，使用所提出的运动记忆技术，随着过去规划经验的增加，规划速度可显著降低高达89%。