为可变形柔性机器人设计了实时状态估计算法，提升复杂地形自主导航能力。
State Estimation for Compliant and Morphologically Adaptive Robots
- 用神经网络融合历史状态与传感器校正机制，联合估计刚体态与柔形态。
- 在GOAT机器人上实现形状误差<4.2%、速度误差<6.3%、姿态误差<1.5°。
- 故障下闭环运行使行进距离提升300%，适合野外极端环境应用。
具有主动或被动柔性的移动机器人在不确定场景中表现出强鲁棒性，适用于农业、科研和环境领域。然而，由于缺乏刚体假设且形态变化导致运动学改变，这类机器人的状态估计极具挑战。本文提出一种方法，同时估计典型刚体状态及与柔度相关的状态，如不同形态与运动模式下的软体机器人形状。所提出的基于神经网络的状态估计算法利用状态历史并直接修正不可靠传感器。我们在能够适应极端户外地形的可被动柔性和主动变形的GOAT平台上测试该框架。网络在一种新型以柔度为中心的坐标系中，基于运动捕捉数据进行训练，能将形状相关测量值预测误差控制在机器人尺寸的4.2%以内，速度误差分别低于最高线速度的6.3%和角速度的2.4%，姿态误差小于1.5度。实验还表明，在电机故障情况下，使用该估计算法进行闭环自主运行，旅行范围提升300%。
原文摘要 · Abstract (English)
Locomotion robots with active or passive compliance can show robustness to uncertain scenarios, which can be promising for agricultural, research and environmental industries. However, state estimation for these robots is challenging due to the lack of rigid-body assumptions and kinematic changes from morphing. We propose a method to estimate typical rigid-body states alongside compliance-related states, such as soft robot shape in different morphologies and locomotion modes. Our neural network-based state estimator uses a history of states and a mechanism to directly influence unreliable sensors. We test our framework on the GOAT platform, a robot capable of passive compliance and active morphing for extreme outdoor terrain. The network is trained on motion capture data in a novel compliance-centric frame that accounts for morphing-related states. Our method predicts shape-related measurements within 4.2% of the robot's size, velocities within 6.3% and 2.4% of the top linear and angular speeds, respectively, and orientation within 1.5 degrees. We also demonstrate a 300% increase in travel range during a motor malfunction when using our estimator for closed-loop autonomous outdoor operation.
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