- The paper presents a novel gripper design with soft polyhedral fingers that integrate active surfaces and passive adaptation for enhanced in-hand manipulation.
- It details a comprehensive model and experimental validation highlighting a stiffness gradient and effective repositioning across various object shapes.
- The study underscores the design's potential to augment rigid grippers with improved dexterity and stability while identifying areas for further optimization.
Active Surface with Passive Omni-Directional Adaptation of Soft Polyhedral Fingers for In-Hand Manipulation
The paper presents a comprehensive paper on the design and efficacy of soft robotic fingers equipped with active surfaces and a passive omni-adaptive network structure, tailored for in-hand manipulation tasks. This innovative approach aims to seamlessly integrate with existing rigid grippers, significantly enhancing their stability and dexterity.
Introduction
Robotic manipulation and locomotion share fundamental principles rooted in evolutionary adaptation, which can be replicated in robotic systems through evolutionary reinforcement learning. Traditional robotic locomotion systems, such as those utilizing wheeled, legged, or tracked mechanisms, distribute loads effectively, enhancing traction and maneuverability. The authors draw a parallel between robotic locomotion and manipulation, especially focusing on the concept of active surfaces similar to tracked mobility. This paper leverages these principles to design a soft robotic finger capable of handling both regular and irregular objects with enhanced dexterity and stability.
Gripper Design
The research introduces a novel gripper design featuring soft polyhedral fingers equipped with active surfaces. These fingers are designed using a universal approach, transforming all edges of a polyhedron into soft-material beam structures, complemented by rolling guidance mechanisms and lattice layers. Each finger integrates an active surface mechanism composed of a timing belt, pulleys, and a driving gear part. This configuration allows the gripper to adapt to various object shapes and sizes, facilitating both compliant grasping and in-hand manipulation.
Model and Analysis
Key performance metrics for the gripper include the stiffness distribution and the dynamics of repositioning and reorienting grasped objects. The stiffness distribution is characterized based on unidirectional compression tests, revealing a consistent trend of decreasing stiffness from the finger base to the tip. This gradient enhances the finger’s adaptability to different object shapes. The dynamics of repositioning and reorientation are modeled using simplified physical equations that account for the forces and torques acting on the grasped objects. These models are validated through experiments, demonstrating the gripper's capability to lift, reposition, and rotate objects with varying geometric properties.
Experimental Validation
The experimental setup involves testing the gripper on four different objects: a cube, an irregular 3D-printed vase, a cylinder, and a sphere. The repositioning trials show a high success rate, especially for geometric shapes like the cube and the sphere. The reorientation trials further validate the gripper’s capability to handle cylindrical and spherical objects, with the active surfaces facilitating controlled rotation and translation.
Results and Discussion
The experimental outcomes demonstrate the gripper's effectiveness in achieving stable and dexterous in-hand manipulation. The robust performance across different object shapes indicates that the design maintains the necessary balance between adaptability, stability, and dexterity. However, some trials experienced failures when handling irregular shapes, pointing to potential improvements in the finger's structural design to enhance consistency.
Conclusion and Future Work
This paper proposes a transformative approach to robotic in-hand manipulation by integrating soft polyhedral fingers with active surfaces into existing rigid grippers. The design achieves a notable improvement in manipulation dexterity and stability, demonstrating its potential for various applications. Future work may focus on refining the finger design to handle larger loads while maintaining high performance with irregularly shaped objects. Further studies could explore scaling the active surface parameters and integrating advanced control algorithms to enhance the overall functionality and versatility of the proposed system.
In summary, this paper provides a solid foundation for advancing robotic manipulation capabilities, paving the way for more adaptable and dexterous robotic hands in practical applications.