In this work, a set of motion primitives is defined for use in an energy-aware motion planning problem. The motion primitives are defined as sequences of control inputs to a simplified four-DOF dynamics model and are used to replace the traditional continuous control space used in many sampling-based motion planners. The primitives are implemented in a Stable Sparse Rapidly Exploring Random Tree (SST) motion planner and compared to an identical planner using a continuous control space. The planner using primitives was found to run 11.0\% faster but yielded solution paths that were on average worse with higher variance. Also, the solution path travel time is improved by about 50\%. Using motion primitives for sampling spaces in SST can effectively reduce the run time of the algorithm, although at the cost of solution quality.

翻译：本文定义了一组运动基元，用于能量感知运动规划问题。这些运动基元被定义为简化四自由度动力学模型的控制输入序列，用于替代许多基于采样的运动规划器中传统的连续控制空间。该基元在稳定稀疏快速探索随机树（SST）运动规划器中实现，并与使用连续控制空间的相同规划器进行对比。研究发现，使用基元的规划器运行速度提升了11.0%，但生成的解路径平均质量较差且方差较高。此外，解路径的旅行时间改善了约50%。在SST中使用运动基元进行空间采样能有效降低算法运行时间，尽管是以牺牲解质量为代价。