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

TL;DR

This work tackles the challenge of achieving flexible, efficient loading manipulation with few actuators by proposing an underactuated metamorphic loading manipulator (UMLM) that couples a metamorphic arm to a passively adaptive gripper. A closed-loop kinetostatic framework captures the mechanism’s topology changes and contact interactions, while a tailored Particle-Swarm Optimization (PSO) algorithm tunes gripper dimensions to ensure uniform force distribution under limited actuation. Simulation and optimization show that the UMLM can adapt to objects of varying geometry on a movable platform, achieving robust grasping with improved energy efficiency and reduced actuation requirements. The proposed modeling and optimization approach provides a scalable, generalizable pathway for designing metamorphic, underactuated manipulators for dynamic, unstructured environments.

Abstract

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.

Figures (18)

• Figure 1: Optimization methodology diagram of the UMLM design.
• Figure 2: 3D Diagram of the UMLM metamorphic mechanism. (a) Geometric definitions of the 3D UMLM model. (b) Geometric definitions and metamorphic process of the gripper model.
• Figure 3: Metamorphic process and kinematic variations of the angles in the loading process. (a) Schematic diagram of the UMLM metamorphic mechanism for loading. (b) Metamorphic process of different configurations and kinematic angle variations curves of $\theta_1$, $\theta_0$, $\theta_4$ in the loading process.
• Figure 4: Diagram of metamorphosis property and topological phase change of the UMLM. (a) and (b) are the schematic diagram and corresponding topological diagram of the topology 1 (lifting or descending configuration). (c) and (d) are the schematic diagram and corresponding topological diagram of the topology 2 (grasping or releasing configuration). Black circles 1–9 denote the links, white circle $C$ represents the composite hinge, letters $A$–$J$ denote the kinematic pairs.
• Figure 5: Schematic diagram of the slider metamorphic mechanism.