让四足机器人在关节故障时仍能稳定穿越复杂地形
DreamFLEX: Learning Fault-Aware Quadrupedal Locomotion Controller for Anomaly Situation in Rough Terrains
- 通过显式故障估计与调节网络实时感知关节故障
- 在仿真和真实场景中均显著提升故障容忍能力
- 适合需要高可靠性的野外作业机器人研发者
近年来，四足机器人的敏捷性和多样化地形通行能力取得显著进展。然而，在长距离行走或穿越复杂地形过程中，硬件问题（如电机过热或关节锁死）可能导致运动失败。尽管已有研究提出故障容错控制方法，但在非结构化地形中的适应性仍存在挑战。本文提出DreamFLEX，一种鲁棒的故障容错运动控制器，使四足机器人在关节失效条件下仍能穿越复杂环境。DreamFLEX集成显式故障估计与调制网络，联合估计机器人关节故障向量，并利用该信息实时调整运动模式，从而在粗糙地形中维持稳定与性能。实验结果表明，无论在仿真还是真实场景中，DreamFLEX均优于现有方法，有效应对硬件故障并保持稳健的运动表现。
原文摘要 · Abstract (English)
Recent advances in quadrupedal robots have demonstrated impressive agility and the ability to traverse diverse terrains. However, hardware issues, such as motor overheating or joint locking, may occur during long-distance walking or traversing through rough terrains leading to locomotion failures. Although several studies have proposed fault-tolerant control methods for quadrupedal robots, there are still challenges in traversing unstructured terrains. In this paper, we propose DreamFLEX, a robust fault-tolerant locomotion controller that enables a quadrupedal robot to traverse complex environments even under joint failure conditions. DreamFLEX integrates an explicit failure estimation and modulation network that jointly estimates the robot's joint fault vector and utilizes this information to adapt the locomotion pattern to faulty conditions in real-time, enabling quadrupedal robots to maintain stability and performance in rough terrains. Experimental results demonstrate that DreamFLEX outperforms existing methods in both simulation and real-world scenarios, effectively managing hardware failures while maintaining robust locomotion performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。