提出地图感知的轨迹采样方法，提升复杂环境导航成功率。
C-Free-Uniform: A Map-Conditioned Trajectory Sampler for Model Predictive Path Integral Control
- 基于地图条件生成控制输入，均匀覆盖自由配置空间
- 在复杂障碍环境中成功率显著提升，采样量减少近半
- 适合需要高效路径规划的机器人导航场景
轨迹采样是基于采样的控制机制的核心。现有方法依赖于从分布 p(u | x) 中生成控制输入 u，其中 x 为当前状态。本文提出自由配置空间均匀性（C-Free-Uniform），具备两大特性：(i) 使控制输入分布能均匀采样自由配置空间；(ii) 与以往独立于环境的采样机制不同，该方法显式依赖于当前局部地图。我们将此采样器集成到新型模型预测路径积分（MPPI）控制器中，称为 CFU-MPPI。实验表明，在复杂多边形障碍环境中，CFU-MPPI 在挑战性导航任务中成功率更高，且所需采样预算大幅降低。
原文摘要 · Abstract (English)
Trajectory sampling is a key component of sampling-based control mechanisms. Trajectory samplers rely on control input samplers, which generate control inputs u from a distribution p(u | x) where x is the current state. We introduce the notion of Free Configuration Space Uniformity (C-Free-Uniform for short) which has two key features: (i) it generates a control input distribution so as to uniformly sample the free configuration space, and (ii) in contrast to previously introduced trajectory sampling mechanisms where the distribution p(u | x) is independent of the environment, C-Free-Uniform is explicitly conditioned on the current local map. Next, we integrate this sampler into a new Model Predictive Path Integral (MPPI) Controller, CFU-MPPI. Experiments show that CFU-MPPI outperforms existing methods in terms of success rate in challenging navigation tasks in cluttered polygonal environments while requiring a much smaller sampling budget.
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