Combined model-based and data-driven approach for the control of a soft robotic neck

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems Pub Date : 2025-08-20 DOI:10.1016/j.robot.2025.105155

Nicole A. Continelli , Luis F. Nagua , Pablo M. Olmos , Concepción A. Monje

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引用次数: 0

Abstract

This paper delves into the potential of integrating model-based and data-driven techniques for controlling the performance of a soft robotic neck. Artificial intelligence (AI) methods, such as machine learning and deep learning, have shown their applicability in modelling and controlling robotic systems with complex nonlinear behaviours. However, model-based approaches have also proven to be effective analytical alternatives, even if they rely on simplified approximations of the robot model. The control system proposed in this work combines the closed loop analytical model of the soft robotic neck with a Multi-Layer Perceptron (MLP) network trained to minimise the neck pose error. The MLP undergoes training with three different data treatments, and the results are compared to determine the most effective one. The experimental results obtained demonstrate the robustness of the proposed technique and its potential as an alternative to classical solutions, whether purely based on analytical models or data-driven models.

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基于模型和数据驱动的柔性机器人颈部控制方法

本文深入研究了基于模型和数据驱动技术集成的潜力，以控制柔性机器人颈部的性能。人工智能（AI）方法，如机器学习和深度学习，已经显示出它们在具有复杂非线性行为的机器人系统建模和控制中的适用性。然而，基于模型的方法也被证明是有效的分析替代方案，即使它们依赖于机器人模型的简化近似。本文提出的控制系统将软机器人颈部的闭环分析模型与多层感知器（MLP）网络相结合，以最小化颈部姿态误差。MLP接受三种不同数据处理的训练，并将结果进行比较，以确定最有效的一种。所获得的实验结果证明了所提出的技术的鲁棒性及其作为经典解决方案的替代方案的潜力，无论是纯粹基于分析模型还是数据驱动模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Robotics and Autonomous Systems 工程技术-机器人学

CiteScore

9.00

自引率

7.00%

发文量

164

审稿时长

4.5 months

期刊介绍： Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems. Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.