State and Trajectory Estimation of Tensegrity Robots via Factor Graphs and Chebyshev Polynomials

TL;DR

This paper introduces a novel factor-graph-based method for continuous-time state and trajectory estimation of tensegrity robots, effectively fusing sensor data and handling nonlinear dynamics for improved accuracy.

Contribution

It is the first to apply factor graphs to tensegrity robot state estimation, integrating multiple sensors and Chebyshev polynomials for robust, real-time estimation.

Findings

The method outperforms ICP-based algorithms on simulated and real data.

Provides high-fidelity, continuous-time estimates for complex robot motions.

Successfully fuses RGB-D and cable sensors for robust state estimation.

Abstract

Tensegrity robots offer compliance and adaptability, but their nonlinear, and underconstrained dynamics make state estimation challenging. Reliable continuous-time estimation of all rigid links is crucial for closed-loop control, system identification, and machine learning; however, conventional methods often fall short. This paper proposes a two-stage approach for robust state or trajectory estimation (i.e., filtering or smoothing) of a cable-driven tensegrity robot. For online state estimation, this work introduces a factor-graph-based method, which fuses measurements from an RGB-D camera with on-board cable length sensors. To the best of the authors' knowledge, this is the first application of factor graphs in this domain. Factor graphs are a natural choice, as they exploit the robot's structural properties and provide effective sensor fusion solutions capable of handling nonlinearities in practice. Both the Mahalanobis distance-based clustering algorithm, used to handle noise, and the Chebyshev polynomial method, used to estimate the most probable velocities and intermediate states, are shown to perform well on simulated and real-world data, compared to an ICP-based algorithm. Results show that the approach provides high fidelity, continuous-time state and trajectory estimates for complex tensegrity robot motions.