Probabilistic Motion Model Learning for Tendon Actuated Continuum Robots with Backlash

In this paper, we propose a probabilistic motion model for tendon actuated continuum robots that experience actuation transmission non-linearities due to cable slack and cable-sheath friction. The model is based on a Lie group formulation of the robot’s end-effector pose that incorporates a new simple backlash model. Bayesian parameter estimation is then employed to learn a probability distribution over the model’s parameters. This allows the uncertainty over the parameters to be propagated in the prediction of the end-effector’s trajectory. The model’s predictive capabilities are compared against the static Cosserat-rod-based model and the Kirchhoff model in simulation and are validated with experiments on a robotized medical endoscope.