Fishing for Data: Modeling, Optimal Planning, and Iterative Learning Control for Flexible Link Robots

Abstract

In this work, we address the problem of precise motion planning and control of flexible-link robots for throwing small objects. Thanks to lightweight materials and elastic bodies, flexible robots can perform fast motions with few actuators. However, they need a planning and control strategy capable of exploiting the robot’s elasticity, negotiating with the system’s underactuation, and compensating for the model’s uncertainties. To solve this challenging task, we 1) compare multiple discrete models for continuum robots’ dynamics and, after selecting a lumped-parameter (LPs) model, experimentally identify its parameters; 2) plan the robot motion via a differential-dynamic-programming-based strategy tailored for flexible-link robots; and 3) employ an iterative learning control (ILC) approach to close the reality-gap. Combining these three steps allows us to execute precise throwing tasks with flexible-link robots. The strategy’s effectiveness has been validated via simulations and experiments with varying trajectories and payloads. We applied the aforementioned approach to realize a throwing motion with a fishing rod for environmental monitoring.