受重力载荷作用的大型高力软机器人机械臂的设计优化
Design Optimization for Large High-Force Soft Robot Manipulators Under Gravitational Loads
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中文总结 AI 辅助
针对大型高力软机器人机械臂设计难题，提出带抗屈曲约束的几何优化方法，通过闭式解预测满足约束且力最大的构型，可预先判断大尺寸软机械臂是否适用于物理交互。
中文摘要 AI 辅助
设计能够产生高力以实现物理人机交互的大型软机器人，仍是软机器人领域的一项重大挑战。现有大型软机器人研究多聚焦于概念验证原型，尚未存在用于确定设计范式是否适用于目标任务的系统性框架。本文提出一种软机器人肢体的几何优化方法，在自身重力载荷下的抗屈曲约束条件下，最大化其阻塞力。我们证明，在特定假设下，所提出的优化问题存在显式解。对一款大型气动驱动软机械臂的三种几何构型开展实验，结果表明该方法可准确预测哪些设计满足约束，以及哪种设计能产生最大的末端执行器力。该方法及其闭式解可让设计者预先确定，预期类型的软机械臂在大尺寸尺度下是否适合用于物理交互。
英文摘要
Designing large soft robots capable of generating high forces for physical human-robot interaction remains a significant challenge in soft robotics. Prior work in large soft robots has focused on proof-of-concept prototypes, and no systematic framework exists for determining the suitability of a design paradigm for a desired task. This manuscript introduces a method for optimizing the geometry of a soft robot limb, maximizing its blocking force subject to an anti-bucking constraint under its own gravitational loading. We demonstrate that an explicit solution exists to the proposed optimization problem under certain assumptions. Experiments with three geometries of a large, soft, pneumatically-actuated manipulator demonstrate that the method correctly predicts which designs meet constraints and which produces the largest end-effector forces. This method, with its closed-form solution, can allow designers to determine a-priori if an intended class of soft manipulators is an appropriate choice for physical interaction at large size scales.
发表机构
- Boston University（波士顿大学）
机构由 AI 辅助整理，请以论文原文为准。
