Comprehensive Performance Optimization of a Novel Anthropomorphic Dexterous Hand
Grasp and in-hand manipulation of dexterous hands are essential for daily operational tasks. In this study, an optimized design method for a novel anthropomorphic dexterous hand is proposed, which improves the comprehensive performance of grasp and manipulability of the dexterous hand. The constraints of multi-fingered contact force and kinematics are established with full consideration of the applicability to the objects of different sizes and the shape characteristics of finger contact surface. The performance index of in-hand manipulation ability is proposed, and the optimization problem is constructed through the technique for order preference by similarity to ideal solution (TOPSIS) to achieve multi-objective optimization of precision grasp and in-hand manipulation. Finally, simulation and experiment verify the feasibility of our prototype and optimization method. The results show that the in-hand translation space and in-hand rotation range are increased by 20% and 8.5%, respectively. Our dexterous hand has excellent abilities of multi-fingered interaction and in-hand manipulation, which can perform 33 grasp gestures and two/three-fingered in-hand manipulation. This study provides a valuable reference for the design of dexterous hands with anthropomorphic functions.
Authors
- Guotao Li (ORCID: https://orcid.org/0000-0001-9201-2700)
- Zhengkun Yi (ORCID: https://orcid.org/0000-0002-8714-1353)
- Weiwei Shang (ORCID: https://orcid.org/0000-0001-7541-2198)
- Shuqing Dai (ORCID: https://orcid.org/0009-0008-4017-5514)
- Shunxiang Pang (ORCID: https://orcid.org/0000-0003-2909-8518)
- Shuang Cong (ORCID: https://orcid.org/0000-0001-8101-0128)
- Fei Zhang (ORCID: https://orcid.org/0000-0002-0584-8962)
- Yifan Ma (ORCID: https://orcid.org/0009-0004-6153-672X)
Institutions
- University of Science and Technology of China (CN)
- Chinese Academy of Sciences (CN)
- Institute of Automation (CN)
- Shenzhen Institutes of Advanced Technology (CN)
Publication Details
- Journal
- IEEE Robotics and Automation Letters
- Published
- 2025-05-23
- DOI
- https://doi.org/10.1109/lra.2025.3572813
- Primary Topic
- Robot Manipulation and Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China