用随机框架实现连续机器人状态的高精度实时估计。
A Stochastic Framework for Continuous-Time State Estimation of Continuum Robots
- 基于连续时间运动学与高斯噪声建模，构建稀疏优化框架。
- 支持高频率传感下外部干扰和数据丢失的自适应估计。
- 可连续插值姿态、速度、应变，适合真实机器人系统部署。
连续体机器人（CRs）的状态估计通常依赖计算复杂的动态模型、简化的形状假设，或局限于准静态方法，对未建模扰动敏感。受因子图优化启发，本文提出一种连续时间随机状态估计框架。通过引入受白噪声高斯过程污染的连续时间运动学因子，结合简单机器人模型与高频传感，实现了对外部力和数据丢失的自适应估计。结果提供姿态、速度、应变的均值与协方差估计，支持时空连续插值。该插值机制也可用于估计过程，使非显式估计状态的测量得以融合。方法固有的稀疏性使其求解复杂度随时间线性增长，插值查询为常数时间。我们在配备陀螺仪和位姿传感器的连续体机器人上验证了方法，展现了其在真实系统中的泛化能力。
原文摘要 · Abstract (English)
State estimation techniques for continuum robots (CRs) typically involve using computationally complex dynamic models, simplistic shape approximations, or are limited to quasi-static methods. These limitations can be sensitive to unmodelled disturbances acting on the robot. Inspired by a factor-graph optimization paradigm, this work introduces a continuous-time stochastic state estimation framework for continuum robots. We introduce factors based on continuous-time kinematics that are corrupted by a white noise Gaussian process (GP). By using a simple robot model paired with high-rate sensing, we show adaptability to unmodelled external forces and data dropout. The result contains an estimate of the mean and covariance for the robot's pose, velocity, and strain, each of which can be interpolated continuously in time or space. This same interpolation scheme can be used during estimation, allowing for inclusion of measurements on states that are not explicitly estimated. Our method's inherent sparsity leads to a linear solve complexity with respect to time and interpolation queries in constant time. We demonstrate our method on a CR with gyroscope and pose sensors, highlighting its versatility in real-world systems.
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。