发表机构
School of Integrated Circuits, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区集成电路学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对四足机器人二维规划中低障碍物处理低效的问题，提出基于CMP-IRRT*的感知辅助高度自适应规划器，通过高度条件碰撞检测和Mamba引导采样，减少探索节点，在可穿越场景中路径长度最多缩短16.3%。
AI 中文摘要
四足机器人能够穿越低矮障碍物，但许多二维规划流程仍将障碍物建模为二值占据区域，并依赖基于采样的搜索，这在有限预算下可能效率低下。我们提出了一种基于CMP-IRRT*（一种通道Mamba PointNet引导的Informed RRT*规划器）的感知辅助高度自适应规划框架。给定校准的俯视RGB观测，感知模块估计障碍物区域，并将深度预测转换为相对于地面的高度图。随后，规划器执行高度条件碰撞检测，将高障碍物视为阻挡，同时允许穿越低障碍物，并利用CMP引导将采样偏向有前景的区域，同时保留标准的自由空间和informed采样回退机制。在二维规划基准上的实验表明，与经典和神经引导基线相比，CMP-IRRT*减少了探索节点和迭代次数，且受控消融实验支持了基于Mamba引导的贡献。在构建的可穿越性感知场景中，当低障碍物可穿越时，所提规划器将路径长度最多缩短16.3%，Unitree Go2演示进一步展示了可执行的绕行和穿越行为。我们的代码在此https URL公开可用。
英文摘要
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.
CommentsAccepted at the 18th Asian Conference on Machine Learning (ACML 2026)
