用可复用的树搜索提升规划效率，让机器人更智能地避障推物。
Model Predictive Trees: Sample-Efficient Receding Horizon Planning with Reusable Tree Search
- 复用前次搜索的最优子树，引导新搜索避开低质量区域
- 在动态变化环境中，深度与变化速度存在性能权衡
- 适合需高效规划的机器人任务，如自动驾驶避障
我们提出模型预测树（MPT），一种可重用树搜索的滚动时域规划算法。与现有方法仅复用最优轨迹不同，MPT复用整个最优子树，同时引导搜索远离低质量区域、靠近高质量区域。通过分析时变动力学下的跟踪误差，揭示了搜索深度与动态变化时间尺度间的权衡关系。数值实验显示，该算法优于采用热启动的先进采样-交叉熵方法。我们在自主车辆测试平台上验证了该规划器，完成非预握式操作任务——将目标物体穿过障碍物区域。相关代码将在 https://github.com/jplathrop/mpt 公开。
原文摘要 · Abstract (English)
We present Model Predictive Trees (MPT), a receding horizon tree search algorithm that improves its performance by reusing information efficiently. Whereas existing solvers reuse only the highest-quality trajectory from the previous iteration as a "hotstart", our method reuses the entire optimal subtree, enabling the search to be simultaneously guided away from the low-quality areas and towards the high-quality areas. We characterize the restrictions on tree reuse by analyzing the induced tracking error under time-varying dynamics, revealing a tradeoff between the search depth and the timescale of the changing dynamics. In numerical studies, our algorithm outperforms state-of-the-art sampling-based cross-entropy methods with hotstarting. We demonstrate our planner on an autonomous vehicle testbed performing a nonprehensile manipulation task: pushing a target object through an obstacle field. Code associated with this work will be made available at https://github.com/jplathrop/mpt.
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