State Estimation for Tensegrity Robots
This addresses the lack of state estimation methods for tensegrity robots, enabling practical deployment of control algorithms in real-world applications like planetary exploration, though it is incremental as it applies an existing filter to a new domain.
The paper tackles the problem of state estimation for tensegrity robots, which is crucial for transferring control algorithms from simulation to hardware, and presents a method based on the unscented Kalman filter that combines inertial, ranging, and actuator data, achieving evaluation on the SUPERball prototype for global position estimation during rolling and local deformation analysis.
Tensegrity robots are a class of compliant robots that have many desirable traits when designing mass efficient systems that must interact with uncertain environments. Various promising control approaches have been proposed for tensegrity systems in simulation. Unfortunately, state estimation methods for tensegrity robots have not yet been thoroughly studied. In this paper, we present the design and evaluation of a state estimator for tensegrity robots. This state estimator will enable existing and future control algorithms to transfer from simulation to hardware. Our approach is based on the unscented Kalman filter (UKF) and combines inertial measurements, ultra wideband time-of-flight ranging measurements, and actuator state information. We evaluate the effectiveness of our method on the SUPERball, a tensegrity based planetary exploration robotic prototype. In particular, we conduct tests for evaluating both the robot's success in estimating global position in relation to fixed ranging base stations during rolling maneuvers as well as local behavior due to small-amplitude deformations induced by cable actuation.