混合C-PRM路径改善非完整运动质量
Improving the Quality of Non-Holonomic Motion by Hybridizing C-PRM Paths
摘要：
基于采样的运动规划器是生成无碰撞运动路径的有效手段。然而，这些运动路径的质量，相对于不同的质量测量，例如路径长度、间隙、平滑度或能量，通常是非常低的。在基于非完整采样的运动规划中，可行运动轨迹的空间受到限制，这一问题更加突出。在这项研究中，我们结合了C-PRM算法宋和阿马托与我们最近推出的路径杂交的方法，用于创建高品质的非完整运动路径，与几个不同的质量措施，如路径长度，平滑度或间隙的组合，以及反向汽车运动的数量。我们的实现包括各种代码优化，这些代码优化可以实现近乎实时的性能，我们相信可以通过进一步优化扩展到用于规划高质量汽车运动的实时工具。
英文摘要：
Sampling-based motion planners are an effective means for generating collision-free motion paths. However, the quality of these motion paths, with respect to different quality measures such as path length, clearance, smoothness or energy, is often notoriously low. This problem is accentuated in the case of non-holonomic sampling-based motion planning, in which the space of feasible motion trajectories is restricted. In this study, we combine the C-PRM algorithm by Song and Amato with our recently introduced path-hybridization approach, for creating high quality non-holonomic motion paths, with combinations of several different quality measures such as path length, smoothness or clearance, as well as the number of reverse car motions. Our implementation includes a variety of code optimizations that result in nearly real-time performance, and which we believe can be extended with further optimizations to a real-time tool for the planning of high-quality car-like motion.
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。
