arXiv:2509.09093cs.RO2025-09被引 3

无需额外电机,机械臂自重构+自适应抓取,轻松应对多变任务。

Kinetostatics and Particle-Swarm Optimization of Vehicle-Mounted Underactuated Metamorphic Loading Manipulators

  • 通过几何约束实现无额外电机的结构重组与灵活运动
  • 粒子群优化提升夹持器参数,适配多种物体抓取
  • 适合需要高效柔性作业的工业搬运场景

固定自由度的装载机构常因执行器过多、控制复杂且适应性差而受限。本文提出一种欠驱动可变构型装载机械臂(UMLM),结合可变构型臂与被动自适应夹持器。变构型臂利用几何约束实现拓扑重构和灵活运动轨迹,无需额外执行器;夹持器完全由臂驱动,通过被动柔顺适应不同物体。建立结构模型并进行静力学分析,研究同构抓握配置。采用粒子群优化(PSO)优化夹持器尺寸参数,提升跨场景适应能力。仿真验证了该设计控制简单、操作灵活,在动态环境中有效抓取多样物体。本工作展示了欠驱动可变构型机制在高效柔性装载中的应用潜力。所提出的建模与优化框架可推广至更广泛的机械臂系统,为高效率、强适应性机器人开发提供可扩展方案。

原文摘要 · Abstract (English)

Fixed degree-of-freedom (DoF) loading mechanisms often suffer from excessive actuators, complex control, and limited adaptability to dynamic tasks. This study proposes an innovative mechanism of underactuated metamorphic loading manipulators (UMLM), integrating a metamorphic arm with a passively adaptive gripper. The metamorphic arm exploits geometric constraints, enabling the topology reconfiguration and flexible motion trajectories without additional actuators. The adaptive gripper, driven entirely by the arm, conforms to diverse objects through passive compliance. A structural model is developed, and a kinetostatics analysis is conducted to investigate isomorphic grasping configurations. To optimize performance, Particle-Swarm Optimization (PSO) is utilized to refine the gripper's dimensional parameters, ensuring robust adaptability across various applications. Simulation results validate the UMLM's easily implemented control strategy, operational versatility, and effectiveness in grasping diverse objects in dynamic environments. This work underscores the practical potential of underactuated metamorphic mechanisms in applications requiring efficient and adaptable loading solutions. Beyond the specific design, this generalized modeling and optimization framework extends to a broader class of manipulators, offering a scalable approach to the development of robotic systems that require efficiency, flexibility, and robust performance.

机械臂欠驱动自适应优化

Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。