Chenevier Jean, González David, Aguado J Vicente, Chinesta Francisco, Cueto Elías
Aragon Institute of Engineering Research, Universidad de Zaragoza, Zaragoza, Spain.
ESI Group chair at Ecole Centrale Nantes and the High Performance Computing Institute, Nantes, France.
PLoS One. 2018 Feb 22;13(2):e0192052. doi: 10.1371/journal.pone.0192052. eCollection 2018.
We present a general strategy for the modeling and simulation-based control of soft robots. Although the presented methodology is completely general, we restrict ourselves to the analysis of a model robot made of hyperelastic materials and actuated by cables or tendons. To comply with the stringent real-time constraints imposed by control algorithms, a reduced-order modeling strategy is proposed that allows to minimize the amount of online CPU cost. Instead, an offline training procedure is proposed that allows to determine a sort of response surface that characterizes the response of the robot. Contrarily to existing strategies, the proposed methodology allows for a fully non-linear modeling of the soft material in a hyperelastic setting as well as a fully non-linear kinematic description of the movement without any restriction nor simplifying assumption. Examples of different configurations of the robot were analyzed that show the appeal of the method.
我们提出了一种基于建模和仿真的软机器人控制通用策略。尽管所提出的方法完全通用，但我们将自己限制在对由超弹性材料制成并由电缆或腱驱动的模型机器人的分析上。为了符合控制算法所施加的严格实时约束，提出了一种降阶建模策略，该策略可以最小化在线CPU成本。相反，提出了一种离线训练程序，该程序可以确定一种表征机器人响应的响应面。与现有策略相反，所提出的方法允许在超弹性环境中对软材料进行完全非线性建模，以及对运动进行完全非线性运动学描述，而无需任何限制或简化假设。分析了机器人不同配置的示例，这些示例展示了该方法的吸引力。
