Proprioceptive Shape Estimation of Tensegrity Manipulators Using Energy Minimisation

TL;DR

This paper presents a novel proprioceptive method for estimating the shape of large-scale tensegrity manipulators using only IMU-based inclination angles, achieving high accuracy without external sensors.

Contribution

It introduces a shape estimation approach leveraging intrinsic inclination data, enabling effective control of large tensegrity structures without costly external sensors.

Findings

Achieves 2.1% shape estimation accuracy of total length

Works under static and disturbed conditions

Applicable to large-scale tensegrity manipulators

Abstract

Shape estimation is fundamental for controlling continuously bending tensegrity manipulators, yet achieving it remains a challenge. Although using exteroceptive sensors makes the implementation straightforward, it is costly and limited to specific environments. Proprioceptive approaches, by contrast, do not suffer from these limitations. So far, several methods have been proposed; however, to our knowledge, there are no proven examples of large-scale tensegrity structures used as manipulators. This paper demonstrates that shape estimation of the entire tensegrity manipulator can be achieved using only the inclination angle information relative to gravity for each strut. Inclination angle information is intrinsic sensory data that can be obtained simply by attaching an inertial measurement unit (IMU) to each strut. Experiments conducted on a five-layer tensegrity manipulator with 20 struts and a total length of 1160 mm demonstrate that the proposed method can estimate the shape with an accuracy of 2.1 \% of the total manipulator length, from arbitrary initial conditions under both static conditions and maintains stable shape estimation under external disturbances.