Proprioceptive Shape Estimation of Tensegrity Manipulators Using Energy Minimisation
Tufail Ahmad BHAT, Shuhei Ikemoto
AI summary
Problem
Tensegrity manipulators lack discrete joints, making proprioceptive shape estimation difficult without costly exteroceptive sensors or complex multi-sensor setups. Existing methods are largely unproven for large-scale, highly redundant structures.
Approach
The authors developed an energy minimization algorithm that reconstructs the manipulator's 3D nodal positions and overall shape using only the gravity-relative inclination angles measured by IMUs attached to each strut.
Key results
- Achieved ~2.1% shape estimation accuracy relative to total manipulator length
- Successfully converged from arbitrary initial conditions under static states
- Maintained stable shape estimation under external manual disturbances
- Validated on a full-scale, highly redundant five-layer TM-40 manipulator with 20 struts
Why it matters
Enables low-cost, scalable proprioceptive control for tensegrity robots in confined or unstructured environments where external sensors are impractical.
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.