Optimizing Motion Parameters in Soft Robotic Hands Using Bayesian Optimization: Enhancing Cycle Time, Addressing Vibration, and Repeatability

This study proposes a novel motion optimization method for a manipulator equipped with a soft robotic hand for object transportation. The flexibility of the soft robotic hand induces large vibrations. The manipulator must pause until the vibration converges, leading to an increase in the cycle time. The robotic system also has issue with the low motion repeatability in the robotic hand, even under identical operational conditions. In this study, a method based on Bayesian algorithms is developed to optimize the motion of a manipulator. The objective is to minimize the cycle time for object transportation tasks, while considering the challenges of vibration and low repeatability in soft robotic hand systems. The optimization is performed through an exploratory search using actual experiments. To achieve a low-cost and versatile measurement system, this study proposes a method for deriving the cycle time, which is a key metric for optimization, based on the measurement results obtained using a web camera with standard specifications. The proposed optimization method is evaluated through a comparison with existing optimization methods, including the grid-search-based, conventional Bayesian optimization, particle swarm optimization, S. Lin’s heuristic algorithm, and sparrow search algorithm. The proposed method achieves optimization results comparable in accuracy to those obtained using the grid-search-based optimization method, whereas requires 95% fewer searches. Furthermore, it provides more stable optimization results than those obtained using the conventional Bayesian optimization method.