State and Trajectory Estimation of Tensegrity Robots via Factor Graphs and Chebyshev Polynomials
2026-04-09 • Robotics
Robotics
AI summaryⓘ
The authors studied how to better estimate the positions and movements of a special type of robot called a tensegrity robot, which is tricky because its parts move in complex ways. They developed a two-step method that combines data from a camera and sensors on the robot's cables using a mathematical tool called factor graphs, which helps handle complicated shapes and noises in the data. Their new method works better than older techniques in both computer simulations and real-life tests, giving more accurate and continuous information about the robot's state. This improvement could help control and understand these flexible robots more effectively.
tensegrity robotstate estimationfactor graphRGB-D cameracable length sensorMahalanobis distanceChebyshev polynomialtrajectory smoothingnonlinear dynamicssensor fusion
Authors
Edgar Granados, Patrick Meng, Charles Tang, Shrimed Sangani, William R. Johnson, Rebecca Kramer-Bottiglio, Kostas Bekris
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.