外观
论文arXiv 2610.1047021 天前
CMP-IRRT*：一种用于四足机器人的感知辅助高度自适应规划器
针对低矮障碍可通行而2D规划效率受限的问题，提出感知辅助的CMP-IRRT*高度自适应框架，并在2D基准、构造场景和Unitree Go2演示中验证。
宇树 Go2原文链接：CMP-IRRT*: A Perception-Assisted Height-Adaptive Planner for Quadruped Robots（arXiv 2610.10470 · 发布日期：2026-10-07）
CMP-IRRT: A Perception-Assisted Height-Adaptive Planner for Quadruped Robots*
针对低矮障碍可通行而2D规划效率受限的问题，提出感知辅助的CMP-IRRT*高度自适应框架，并在2D基准、构造场景和Unitree Go2演示中验证。
核心要点
- 许多2D规划仍将障碍二值占据建模，有限采样预算下搜索效率可能不足。
- 框架由顶视RGB估计障碍和相对地面高度图，按高度碰撞检测并用CMP引导采样。
- 在2D规划基准上，相比经典和神经引导基线减少探索节点与迭代。
- 构造可通行场景中低障碍可穿越时路径最多缩短16.3%，代码已公开。
验证范围与局限：验证范围为2D规划基准、构造可通行场景及Unitree Go2演示。
英文摘要 (Abstract)
Quadruped robots can traverse low obstacles, but many 2D planning pipelines still model obstacles as binary occupied regions and rely on sampling-based search that can be inefficient under a limited budget. We propose a perception-assisted height-adaptive planning framework based on CMP-IRRT*, a Channel Mamba PointNet-guided Informed RRT* planner. Given a calibrated top-view RGB observation, the perception module estimates obstacle regions and converts depth predictions into a ground-relative height map. The planner then performs height-conditioned collision checking, treating high obstacles as blocked while allowing low obstacles to be traversed, and uses the CMP guide to bias sampling toward promising regions while retaining standard free-space and informed sampling fallbacks. Experiments on 2D planning benchmarks show that CMP-IRRT* reduces explored nodes and iterations compared with classical and neural-guided baselines, and a controlled ablation supports the contribution of the Mamba-based guide. In constructed traversability-aware scenarios, the proposed planner reduces path length by up to 16.3% when low obstacles are traversable, and a Unitree Go2 demonstration further shows executable bypassing and traversal behaviors. Our code is publicly available at https://github.com/MingfanZhao/height-adaptive-planner.
作者：Mingfan Zhao, Wendong Mao, Zhongfeng Wang
来源
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