Real-time force/position control of soft growing robots: A data-driven model predictive approach

Abstract Vine robots represent a novel class of soft robots that achieve mobility through tip extension, a mechanism inspired by the natural growth processes of vine plants. This unique movement strategy enables effective navigation in constrained and cluttered environments, offering significant advantages over conventional robotic systems. However, the continuum nature and inherent compliance of vine robots introduce complex modeling and control challenges. Deep learning offer a powerful alternative for modeling systems with such complex dynamics. In this article, we present a data-driven dynamic model for a pressure-driven everting vine robot, utilizing a deep neural network (DNN)-based discrete-time dynamic model. This model was integrated into a model predictive control (MPC) framework, and a comparative analysis was conducted against the MPC framework using a nonlinear first-principle model of the vine robot. The results demonstrate that the DNN-MPC framework offers a better control performance and significantly improved computational efficiency compared to the MPC based on the nonlinear first-principles model. The DNN-MPC reduced computation time by a factor of 11, making it highly viable for real-time control applications.