Tensegrity robots, which are composed of rigid compressive elements (rods) and flexible tensile elements (e.g., cables), have a variety of advantages, including flexibility, light weight, and resistance to mechanical impact. Nevertheless, the hybrid soft-rigid nature of these robots also complicates the ability to localize and track their state. This work aims to address what has been recognized as a grand challenge in this domain, i.e., the pose tracking of tensegrity robots through a markerless, vision-based method, as well as novel, onboard sensors that can measure the length of the robot's cables. In particular, an iterative optimization process is proposed to estimate the 6-DoF poses of each rigid element of a tensegrity robot from an RGB-D video as well as endcap distance measurements from the cable sensors. To ensure the pose estimates of rigid elements are physically feasible, i.e., they are not resulting in collisions between rods or with the environment, physical constraints are introduced during the optimization. Real-world experiments are performed with a 3-bar tensegrity robot, which performs locomotion gaits. Given ground truth data from a motion capture system, the proposed method achieves less than 1 cm translation error and 3 degrees rotation error, which significantly outperforms alternatives. At the same time, the approach can provide pose estimates throughout the robot's motion, while motion capture often fails due to occlusions.

翻译：由硬压缩元素(rod)和灵活的抗拉元素(例如电缆)组成的感官机器人具有各种优势,包括灵活性、轻重量和耐机械冲击的能力。然而,这些机器人的混合软硬性性质也使定位和跟踪其状态的能力复杂化。这项工作旨在应对这一领域公认的重大挑战,即通过无标记、基于视觉的方法以及新颖的、能够测量机器人电缆长度的机载传感器(例如电缆)对紧张性机器人进行跟踪。特别是,提议了一个迭代优化程序,从 RGB-D 视频中估算每个紧张性机器人硬性元素的6-DoF配置,以及从电缆传感器中估算尾端距离的能力。为了确保对僵化元素的构成进行实际可行的估计,即它们不会造成杆或与环境的碰撞,在优化过程中引入了物理限制。现实世界实验是用三巴性紧凑紧凑的电缆长度进行,同时从移动式机器人中测出一个方向定位不精确的模型,从移动式模型到移动式模型中测出一个方向的模型。