Machine learning-based inverse kinematics scalability for prismatic tensegrity structural manipulators

Tensegrity structures are gaining attention due to their distinctive features that stem from wire-driven mechanisms and their highly redundant nature. These features include a lightweight framework, improved resistance to impacts, and ability to carry high payloads. Nonetheless, controlling these structures and understanding their movement remain complex challenges. Our research introduces a pioneering control strategy that utilizes some machine learning algorithms (linear regression, ridge regression, and neural network feedforward) to achieve inverse kinematics for prismatic tensegrity manipulators. This approach has been experimentally validated on two different structures, one with a triangular and the other with a quadrangular configuration, each forming a dual-layer setup. Our experimental results indicate that each of the presented algorithms facilitates the approximate inverse kinematics required for the control of the manipulators with average precision error of 2 cm.