State Estimation for Tensegrity Robots

TL;DR

This paper develops and tests a state estimation method for tensegrity robots using an unscented Kalman filter that integrates inertial, ranging, and actuator data, facilitating real-world control transfer.

Contribution

It introduces a novel UKF-based state estimator tailored for tensegrity robots, combining multiple sensor inputs for improved accuracy and robustness.

Findings

Successful global position estimation during rolling maneuvers

Effective local deformation tracking due to cable actuation

Demonstrated applicability on the SUPERball planetary robot prototype

Abstract

Tensegrity robots are a class of compliant robots that have many desirable traits when designing mass efficient systems that must interact with uncertain environments. Various promising control approaches have been proposed for tensegrity systems in simulation. Unfortunately, state estimation methods for tensegrity robots have not yet been thoroughly studied. In this paper, we present the design and evaluation of a state estimator for tensegrity robots. This state estimator will enable existing and future control algorithms to transfer from simulation to hardware. Our approach is based on the unscented Kalman filter (UKF) and combines inertial measurements, ultra wideband time-of-flight ranging measurements, and actuator state information. We evaluate the effectiveness of our method on the SUPERball, a tensegrity based planetary exploration robotic prototype. In particular, we conduct tests for evaluating both the robot's success in estimating global position in relation to fixed ranging base stations during rolling maneuvers as well as local behavior due to small-amplitude deformations induced by cable actuation.