提出C-Uniform采样法，让机器人轨迹更均匀覆盖配置空间，提升规划效率。
C-Uniform Trajectory Sampling For Fast Motion Planning
- 通过网络流优化预计算控制概率，实现配置空间均匀采样
- 在模拟中使MPPI类控制器性能提升最高达40%覆盖率
- 已在1/10比例赛车上验证，适合高速运动规划场景
本文研究机器人轨迹采样问题，提出C-Uniform性概念。与传统均匀采样控制输入（导致配置空间采样偏差）不同，C-Uniform轨迹通过控制动作实现配置空间的均匀采样。针对一维随机游走，给出直观闭式解；对一般机器人系统，提出基于网络流的优化方法以预计算C-Uniform轨迹。将C-Uniform性应用于模型预测路径积分（MPPI）控制器设计。仿真结果显示，采用C-Uniform轨迹可显著提升MPPI类控制器性能，覆盖率最高提升40%。方法已在1/10比例赛车上实现并验证其实用性。
原文摘要 · Abstract (English)
We study the problem of sampling robot trajectories and introduce the notion of C-Uniformity. As opposed to the standard method of uniformly sampling control inputs (which lead to biased samples of the configuration space), C-Uniform trajectories are generated by control actions which lead to uniform sampling of the configuration space. After presenting an intuitive closed-form solution to generate C-Uniform trajectories for the 1D random-walker, we present a network-flow based optimization method to precompute C-Uniform trajectories for general robot systems. We apply the notion of C-Uniformity to the design of Model Predictive Path Integral controllers. Through simulation experiments, we show that using C-Uniform trajectories significantly improves the performance of MPPI-style controllers, achieving up to 40% coverage performance gain compared to the best baseline. We demonstrate the practical applicability of our method with an implementation on a 1/10th scale racer.
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