用全身分布的接近传感器，同时估计机器人姿态并构建环境地图。
State Estimation and Environment Recognition for Articulated Structures via Proximity Sensors Distributed over the Whole Body
- 将传统时序状态估计算法扩展到空间方向，融合全身传感器数据。
- 仿真结果显示姿态估计误差显著降低。
- 适合柔性机器人或全身体接触环境交互的应用场景。
对于低刚性机器人，仅依靠运动学确定其状态具有挑战性，尤其当整个机器人身体与环境接触时，精确的状态估计对环境交互至关重要。本文提出一种通过整合分布在全身的接近传感器数据，实现关节结构姿态估计与环境映射的联合方法。该方法将通常用于状态估计的离散时间模型扩展至关节结构的空间维度。仿真结果表明，该方法能显著降低估计误差。
原文摘要 · Abstract (English)
For robots with low rigidity, determining the robot's state based solely on kinematics is challenging. This is particularly crucial for a robot whose entire body is in contact with the environment, as accurate state estimation is essential for environmental interaction. We propose a method for simultaneous articulated robot posture estimation and environmental mapping by integrating data from proximity sensors distributed over the whole body. Our method extends the discrete-time model, typically used for state estimation, to the spatial direction of the articulated structure. The simulations demonstrate that this approach significantly reduces estimation errors.
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