让四足机器人在易塌陷地形上安全行走，无需额外传感器。
Load-bearing Assessment for Safe Locomotion of Quadruped Robots on Collapsing Terrain
- 用关节数据探测地形稳定性，不改硬件。
- 通过模型预测控制优化动作，兼顾稳定与探查。
- 实测验证可在塌陷平台和岩石地安全行进。
易塌陷地形常见于搜救任务或行星探测，对四足机器人构成严峻挑战。本文提出一种融合地形探测、承载力分析、运动规划与控制策略的鲁棒运动框架，实现不稳定表面的安全导航。不同于依赖专用传感器或外部地图的传统方法，本方案仅利用关节测量数据评估地形稳定性，无需硬件改造。采用模型预测控制（MPC）系统优化机器人运动，在保持稳定性和满足探测约束间取得平衡；状态机协调地形探测行为，使机器人能识别可塌区域并动态调整落脚点。在自建塌陷平台及岩石地形上的实验表明，该框架能有效穿越不稳地形，维持稳定并优先保障安全。
原文摘要 · Abstract (English)
Collapsing terrains, often present in search and rescue missions or planetary exploration, pose significant challenges for quadruped robots. This paper introduces a robust locomotion framework for safe navigation over unstable surfaces by integrating terrain probing, load-bearing analysis, motion planning, and control strategies. Unlike traditional methods that rely on specialized sensors or external terrain mapping alone, our approach leverages joint measurements to assess terrain stability without hardware modifications. A Model Predictive Control (MPC) system optimizes robot motion, balancing stability and probing constraints, while a state machine coordinates terrain probing actions, enabling the robot to detect collapsible regions and dynamically adjust its footholds. Experimental results on custom-made collapsing platforms and rocky terrains demonstrate the framework's ability to traverse collapsing terrain while maintaining stability and prioritizing safety.
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