Knowing the state of a robot is critical for many problems, such as feedback control. For continuum robots, state estimation is an incredible challenge. First, the motion of a continuum robot involves many kinematic states, including poses, strains, and velocities. Second, all these states are infinite-dimensional due to the robot's flexible property. It has remained unclear whether these infinite-dimensional states are observable at all using existing sensing techniques. Recently, we presented a solution to this challenge. It was a mechanics-based dynamic state estimation algorithm, called a Cosserat theoretic boundary observer, which could recover all the infinite-dimensional robot states by only measuring the velocity twist of the tip. In this work, we generalize the algorithm to incorporate tip pose measurements for more tuning freedom. We also validate this algorithm offline using experimental data recorded from a tendon-driven continuum robot. We feed the recorded tendon force and tip measurements into a numerical solver of the Cosserat rod model based on our robot. It is observed that, even with purposely deviated initialization, the state estimates by our algorithm quickly converge to the recorded ground truth states and closely follow the robot's actual motion.

翻译：了解机器人状态对于反馈控制等诸多问题至关重要。对于连续体机器人而言，状态估计是一项巨大挑战。首先，连续体机器人的运动涉及多种运动学状态，包括位姿、应变和速度。其次，由于机器人的柔性特性，所有这些状态都是无限维的。目前尚不清楚现有传感技术能否观测到这些无限维状态。近期，我们提出了一种解决该挑战的方案。这是一种基于力学的动态状态估计算法，称为Cosserat理论边界观测器，仅通过测量末端的速度旋量即可恢复所有无限维机器人状态。本文中，我们将该算法推广至融合末端位姿测量，以获得更多调节自由度。我们还利用从肌腱驱动连续体机器人获取的实验数据离线验证了该算法。我们将记录的肌腱拉力和末端测量值输入基于我们机器人的Cosserat杆模型数值求解器中。结果表明，即使初始化存在刻意偏差，该算法的状态估计值也能快速收敛至记录的真实状态，并紧密跟踪机器人的实际运动。