论文标题
分段仿射曲率模型:一个降低的订购模型,用于PCC以外的软机器人 - 环境相互作用
Piecewise Affine Curvature model: a reduced-order model for soft robot-environment interaction beyond PCC
论文作者
论文摘要
软机器人因其倾向于实现合规性和复杂的机器人环境相互作用而闻名。软机器人操纵器或细长的连续结构机器人具有利用这些相互作用的潜力,以实现新的探索和操纵能力以及安全的人类机器人相互作用。但是,外力的相互作用或扰动导致软结构在无限程度的自由度(DOF)空间中变形。为了控制此类系统,需要减少订单模型;通常,模型考虑恒定曲率的分段部分,尽管外部力经常从恒定曲率假设中变形。在这项工作中,我们对计算治疗性和建模准确性之间的权衡进行了分析。然后,我们提出了一个新的运动学模型,即分段仿射曲率(PAC),我们在理论上和实验上验证该模型,表明该高阶模型在受外力扰动时可以更好地捕获软连续体机器人的配置。与当前的碎裂恒定曲率(PCC)模型相比,我们证明了软连续体机器人的末端位置的误差降低30 \%。
Soft robot are celebrated for their propensity to enable compliant and complex robot-environment interactions. Soft robotic manipulators, or slender continuum structure robots have the potential to exploit these interactions to enable new exploration and manipulation capabilities and safe human-robot interactions. However, the interactions, or perturbations by external forces cause the soft structure to deform in an infinite degree of freedom (DOF) space. To control such system, reduced order models are needed; typically models consider piecewise sections of constant curvature although external forces often deform the structure out of the constant curvature hypothesis. In this work we perform an analysis of the trade-off between computational treatability and modelling accuracy. We then propose a new kinematic model, the Piecewise Affine Curvature (PAC) which we validate theoretically and experimentally showing that this higher-order model better captures the configuration of a soft continuum body robot when perturbed by the external forces. In comparison to the current state of the art Piecewise Constant Curvature (PCC) model we demonstrate up to 30\% reduction in error for the end position of a soft continuum body robot.