Tensegrity Robot Proprioceptive State Estimation with Geometric Constraints

TL;DR

This paper introduces a novel proprioceptive state estimator for tensegrity robots that combines geometric constraints, IMU, and encoder data with a contact-aided Kalman filter, achieving accurate real-time state estimation comparable to traditional robots.

Contribution

It presents the first integrated geometric and sensor-based state estimator for tensegrity robots, enabling improved autonomy and control in complex environments.

Findings

Achieves 4.2% average drift in state estimation

Performs well in both simulation and real-world tests

Potential for real-time onboard implementation

Abstract

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.