Benchmarking anthropomorphic hands through grasping simulations

• Published in: Journal of computational design and engineering 2022-04, Vol.9 (2), p.330-342
• Main Authors: Llop-Harillo, Immaculada, Iserte, José L, Pérez-González, Antonio
• Format: Article
• Language: English
• ISSN: 2288-5048; 2288-5048
• DOI: 10.1093/jcde/qwac002

Abstract In recent decades, the design of anthropomorphic hands has been developed greatly improving both cosmesis and functionality. Experimentation, simulation, and combined approaches have been used in the literature to assess the effect of design alternatives (DAs) on the final performance of artificial hands. However, establishing standard benchmarks for grasping and manipulation is a need recognized among the robotics community. Experimental approaches are costly, time consuming, and inconvenient in early design stages. Alternatively, computer simulation with the adaptation of metrics based on experimental benchmarks for anthropomorphic hands could be useful to evaluate and rank DAs. The aim of this study is to compare the anthropomorphism of the grasps performed with 28 DAs of the IMMA hand, developed by the authors, using either (i) the brute-force approach and grasp quality metrics proposed in previous works or (ii) a new simulation benchmark approach. The new methodology involves the generation of efficient grasp hypotheses and the definition of a new metric to assess stability and human likeness for the most frequently used grasp types in activities of daily living, pulp pinch and cylindrical grip, adapting the experimental Anthropomorphic Hand Assessment Protocol to the simulation environment. This new simulation benchmark, in contrast to the other approach, resulted in anthropomorphic and more realistic grasps for the expected use of the objects. Despite the inherent limitations of a simulation analysis, the benchmark proposed provides interesting results for selecting optimal DAs in order to perform stable and anthropomorphic grasps. Graphical Abstract Graphical Abstract