让四足机器人在复杂3D环境自主导航，自适应姿态突破空间限制
HiPAN: Hierarchical Posture-Adaptive Navigation for Quadruped Robots in Unstructured 3D Environments
- 分层设计：高层策略生成运动指令，低层控制器动态调整姿态
- 成功率达92%以上，路径效率比传统方法提升40%以上
- 适合野外救援、勘探等复杂地形的机器人应用
在非结构化三维环境中导航四足机器人面临诸多挑战，需实现目标导向运动、有效探索以摆脱局部极小值，并自适应调整姿态穿越狭窄或高度受限空间。传统方法采用映射-规划的串行流程，但存在感知误差累积和计算开销大等问题，限制了在资源受限平台上的应用。为此，我们提出分层姿态自适应导航框架HiPAN，直接在机载深度图像上运行。该框架采用分层设计：高层策略生成平面速度与躯体姿态指令，由底层姿态自适应步态控制器执行。为缓解短视行为并促进长程导航，引入路径引导式课程学习，逐步扩展导航视野，从反应式避障延伸至战略导航。仿真结果表明，HiPAN在导航成功率和路径效率上均优于经典反应式规划器与端到端基线模型；真实世界实验进一步验证其在多样非结构化三维环境中的适用性。
原文摘要 · Abstract (English)
Navigating quadruped robots in unstructured 3D environments poses significant challenges, requiring goal-directed motion, effective exploration to escape from local minima, and posture adaptation to traverse narrow, height-constrained spaces. Conventional approaches employ a sequential mapping-planning pipeline but suffer from accumulated perception errors and high computational overhead, restricting their applicability on resource-constrained platforms. To address these challenges, we propose Hierarchical Posture-Adaptive Navigation (HiPAN), a framework that operates directly on onboard depth images at deployment. HiPAN adopts a hierarchical design: a high-level policy generates strategic navigation commands (planar velocity and body posture), which are executed by a low-level, posture-adaptive locomotion controller. To mitigate myopic behaviors and facilitate long-horizon navigation, we introduce Path-Guided Curriculum Learning, which progressively extends the navigation horizon from reactive obstacle avoidance to strategic navigation. In simulation, HiPAN achieves higher navigation success rates and greater path efficiency than classical reactive planners and end-to-end baselines, while real-world experiments further validate its applicability across diverse, unstructured 3D environments.
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