Enhancing Object Manipulation and Transportation in Multi-Robot Systems with Soft Gripper Integration and Caging-Based Control
Juan C. Tejada, Alejandro Toro-Ossaba, María A. López-Quintero, David Rozo-Osorio, E. G. Hernández-Martínez, Mario Góngora, Isis Bonet
- 发表年份
- 2025
- 引用次数
- 1
- 访问权限
- 开放获取
摘要
Abstract Multi-Robot Systems (MRS) have shown great potential in object manipulation and transportation tasks by enabling the coordinated handling of complex and large objects. This paper presents a novel hybrid caging-based approach for cooperative object transport using only two omnidirectional mobile robots equipped with soft grippers inspired by the Fin-Ray effect. The proposed system integrates a Leader-Follower feedback controller that regulates distance and angle between robots to maintain the object closure condition throughout the transport. The soft grippers enhance adaptability to object contours and allow stable manipulation with fewer robots. Extensive real-world experiments were conducted with objects of various shapes, demonstrating the robustness and effectiveness of the proposed strategy. Results show accurate trajectory tracking and object retention under both static and dynamic orientation scenarios, highlighting the potential of soft robotic integration in cooperative manipulation tasks.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002