Tensegrity robots, characterized by a synergistic assembly of rigid rods and elastic cables, form robust structures that are resistant to impacts. However, this design introduces complexities in kinematics and dynamics, complicating control and state estimation. This work presents a novel proprioceptive state estimator for tensegrity robots. The estimator initially uses the geometric constraints of 3-bar prism tensegrity structures, combined with IMU and motor encoder measurements, to reconstruct the robot's shape and orientation. It then employs a contact-aided invariant extended Kalman filter with forward kinematics to estimate the global position and orientation of the tensegrity robot. The state estimator's accuracy is assessed against ground truth data in both simulated environments and real-world tensegrity robot applications. It achieves an average drift percentage of 4.2%, comparable to the state estimation performance of traditional rigid robots. This state estimator advances the state of the art in tensegrity robot state estimation and has the potential to run in real-time using onboard sensors, paving the way for full autonomy of tensegrity robots in unstructured environments.

翻译：张拉整体机器人以刚性杆与弹性缆绳协同组装为特征，形成具有抗冲击性的鲁棒结构。然而，这种设计在运动学与动力学层面引入了复杂性，为控制与状态估计带来挑战。本研究提出一种新型的张拉整体机器人本体状态估计器。该估计器首先利用三棱柱张拉整体结构的几何约束，结合惯性测量单元与电机编码器测量值，重构机器人的形态与方位。随后，采用基于接触辅助的不变扩展卡尔曼滤波器结合正向运动学，估计张拉整体机器人的全局位置与姿态。通过在仿真环境与真实张拉整体机器人应用场景中与基准真值数据对比，评估了该状态估计器的精度。其平均漂移率为4.2%，与传统刚性机器人的状态估计性能相当。该状态估计器推动了张拉整体机器人状态估计领域的技术前沿，并具备利用机载传感器实现实时运行的潜力，为张拉整体机器人在非结构化环境中实现完全自主性奠定了基础。