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.
翻译:以软机器人的倾向来庆祝软机器人, 以其符合和复杂的机器人- 环境互动。 软机器人操纵器或细体连续结构机器人有潜力利用这些互动来进行新的探索和操作能力以及安全的人类- 机器人互动。 但是, 外部力量的相互作用或扰动导致软结构在无限自由空间( DOF) 的变形。 为了控制这种系统, 需要降低排序模型; 典型的模型会考虑恒定曲线的片段, 尽管外部力量经常使结构从常态曲线假设中变形。 在这项工作中, 我们分析了计算可处理性和建模准确性之间的取舍。 我们然后提出了一个新的运动模型, 即小巧缩形曲线( PAC ), 我们从理论上和实验上证实, 这个更高顺序模型在受到外部力量的干扰时, 更好地捕捉到软连续体机器人的配置。 与目前最先进的Pasky Contin Curvat 模型( PC) 相比, 我们证明软体连续体机器人最终位置的错误减少到 30° 。