用冗余网络提升四足机器人在视觉失效时的运动容错能力
RENet: Fault-Tolerant Motion Control for Quadruped Robots via Redundant Estimator Networks under Visual Collapse
- 双估计算法在线切换，应对视觉感知不确定性
- 实机测试中复杂户外环境仍保持稳定运动
- 适合野外部署的高可靠性机器人系统
基于视觉的四足机器人在户外环境中面临严峻挑战。准确的环境预测与深度传感器噪声的有效处理在实际部署中仍难以实现，严重限制了此类算法的室外应用。为解决视觉运动控制中的部署难题，本文提出冗余估计算法（RENet）框架。该框架采用双估计算法结构，在机载视觉失效时仍能保证运动性能与部署稳定性。通过在线估计算法自适应调整，方法可无缝切换估计模块以应对视觉感知不确定性。在真实机器人上的实验验证了该框架在复杂户外环境下的有效性，尤其在视觉感知退化场景中表现突出。该框架展示了在恶劣野外条件下可靠机器人部署的潜力。项目网站：https://RENet-Loco.github.io/
原文摘要 · Abstract (English)
Vision-based locomotion in outdoor environments presents significant challenges for quadruped robots. Accurate environmental prediction and effective handling of depth sensor noise during real-world deployment remain difficult, severely restricting the outdoor applications of such algorithms. To address these deployment challenges in vision-based motion control, this letter proposes the Redundant Estimator Network (RENet) framework. The framework employs a dual-estimator architecture that ensures robust motion performance while maintaining deployment stability during onboard vision failures. Through an online estimator adaptation, our method enables seamless transitions between estimation modules when handling visual perception uncertainties. Experimental validation on a real-world robot demonstrates the framework's effectiveness in complex outdoor environments, showing particular advantages in scenarios with degraded visual perception. This framework demonstrates its potential as a practical solution for reliable robotic deployment in challenging field conditions. Project website: https://RENet-Loco.github.io/
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