Deriving humanlike arm hand system poses

Minas Liarokapis, Charalampos P. Bechlioulis, Panagiotis Artemiadis, Kostas J. Kyriakopoulos

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Robots are rapidly becoming part of our lives, coexisting, interacting, and collaborating with humans in dynamic and unstructured environments. Mapping of human to robot motion has become increasingly important, as human demonstrations are employed in order to "teach" robots how to execute tasks both efficiently and anthropomorphically. Previous mapping approaches utilized complex analytical or numerical methods for the computation of the robot inverse kinematics (IK), without considering the humanlikeness of robot motion. The scope of this work is to synthesize humanlike robot trajectories for robot arm-hand systems with arbitrary kinematics, formulating a constrained optimization scheme with minimal design complexity and specifications (only the robot forward kinematics (FK) are used). In so doing, we capture the actual human arm-hand kinematics, and we employ specific metrics of anthropomorphism, deriving humanlike poses and trajectories for various arm-hand systems (e.g., even for redundant or hyper-redundant robot arms and multifingered robot hands). The proposed mapping scheme exhibits the following characteristics: (1) it achieves an efficient execution of specific human-imposed goals in task-space, and (2) it optimizes anthropomorphism of robot poses, minimizing the structural dissimilarity/distance between the human and the robot arm-hand systems.

Original languageEnglish (US)
Article number011012
JournalJournal of Mechanisms and Robotics
Volume9
Issue number1
DOIs
StatePublished - Feb 1 2017

ASJC Scopus subject areas

  • Mechanical Engineering

Fingerprint

Dive into the research topics of 'Deriving humanlike arm hand system poses'. Together they form a unique fingerprint.

Cite this