State and Trajectory Estimation of Tensegrity Robots via Factor Graphs and Chebyshev Polynomials
This addresses a critical problem for robotics researchers and engineers working with tensegrity robots, enabling better control and system identification, though it is an incremental advance applying existing techniques to a new domain.
The paper tackled state and trajectory estimation for tensegrity robots, which are challenging due to nonlinear dynamics, by proposing a factor-graph-based method that fuses RGB-D camera and cable length sensor data, achieving high-fidelity estimates in simulations and real-world tests.
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.