贝叶斯连续体机器人动力学与状态估计
Bayesian Continuum Robot Dynamics and State Estimation
浏览论文内容
中文总结 AI 辅助
针对连续体机器人在动态运动下现有状态估计方法忽略惯性效应的问题，提出基于Cosserat杆动力学的贝叶斯框架，将惯性和阻尼等效为载荷，实现随机前向仿真与联合状态估计，并经仿真和实验验证。
中文摘要 AI 辅助
近期基于因子图的连续体机器人状态估计方法在准静态应用和利用白噪声运动学先验的时空估计中已取得成功。然而，当惯性效应显著时，这些近似可能无法捕捉底层物理特性，从而限制动态运动期间的精度。相比之下，我们的方法近似连续体机器人的Cosserat杆动力学。我们将惯性和阻尼写为等效施加载荷，使得动态平衡保持先前准静态机器人工作中静态平衡的代数形式。在没有骨干观测的情况下，该框架退化为机器人运动的随机前向仿真。在有观测的情况下，它联合优化运动学和动态状态，并推断外部载荷及其他状态。我们通过仿真和实验验证了该方法，展示了肌腱驱动连续体机器人的随机前向仿真以及状态估计。
英文摘要
Recent factor graph approaches to continuum robot state estimation have been successful for quasi-static applications and spatiotemporal estimation using white-noise kinematic motion priors. However, when inertial effects are significant, these approximations may fail to capture the underlying physics, limiting accuracy during dynamic motions. In contrast, our approach approximates the Cosserat rod dynamics of continuum robots. We write inertia and damping as equivalent applied loads, so that the dynamic balance retains the algebraic form of the static one from prior work with quasi-static robots. Without backbone observations, the framework reduces to a stochastic forward simulation of the robot's motion. Given observations, it jointly refines kinematic and dynamic states and infers external loads, among other states. We validate the approach through simulation and experiments, demonstrating stochastic forward simulation as well as state estimation on tendon-driven continuum robots.
发表机构
- Vanderbilt University（范德堡大学）
- University of Utah（犹他大学）
机构由 AI 辅助整理，请以论文原文为准。
