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Research Papers

Deriving Humanlike Arm Hand System Poses

[+] Author and Article Information
Minas Liarokapis

School of Engineering and Applied Science,
Yale University,
9 Hillhouse Avenue,
New Haven, CT 06511
e-mail: minas.liarokapis@yale.edu

Charalampos P. Bechlioulis

School of Mechanical Engineering,
National Technical University of Athens,
Athens 15780, Greece
e-mail: chmpechl@mail.ntua.gr

Panagiotis K. Artemiadis

School for Engineering of Matter,
Transport and Energy,
Arizona State University,
Tempe, AZ 85287
e-mail: panagiotis.artemiadis@asu.edu

Kostas J. Kyriakopoulos

School of Mechanical Engineering,
National Technical University of Athens,
Athens 15780, Greece
e-mail: kkyria@mail.ntua.gr

1Corresponding author.

Manuscript received May 30, 2016; final manuscript received December 10, 2016; published online January 9, 2017. Assoc. Editor: Marcia K. O'Malley.

J. Mechanisms Robotics 9(1), 011012 (Jan 09, 2017) (9 pages) Paper No: JMR-16-1156; doi: 10.1115/1.4035505 History: Received May 30, 2016; Revised December 10, 2016

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.

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Figures

Grahic Jump Location
Fig. 1

Illustrations of the proposed metrics of anthropomorphism. Human arm is the right kinematic chain that consists of two links, while the hypothetical robot arm is the left kinematic chain that has 5 links.

Grahic Jump Location
Fig. 2

Mapping human to robot motion experiments. The proposed methodology has been used to extract humanlike robot poses for three different applications. (a) The real-time teleoperation of a simulated Mitsubishi PA10–DLR/HIT II arm-hand system. (b) The teleoperation of a simulated arm-hand system that consists of a hyper-redundant robot arm (21DOF) and the DLR/HIT II robot hand. (c) An example of autonomous, anthropomorphic grasp planning using the Mitsubishi PA10–DLR/HIT II arm-hand system.

Grahic Jump Location
Fig. 3

Comparison of different mapping methodologies for the case of the Barrett WAM robot arm [44]. The left kinematic chain for all cases is the Barrett WAM robot arm which is longer that then human arm (right kinematic chain). The two kinematic chains are depicted with an offset in the x-axis of their base frames, in order to facilitate comparisons. The sphere denotes the desired end-effector position for the robot arm. For all cases, the human arm pose is the same.

Grahic Jump Location
Fig. 4

Deriving humanlike poses for m-fingered robot hands with size equal to the 110% of the human hand size. The selection of the desired robot fingertip positions (crosses) is performed via interpolation between the human fingertips positions (circles).

Grahic Jump Location
Fig. 5

Deriving humanlike robot poses for (1) a 18DOF hyper-redundant arm and (2) an arm-hand system that consists of a 44DOF arm and a five-fingered hand

Grahic Jump Location
Fig. 6

(a) A trajectory tracking example that involves a 20DOF hyper-redundant robot arm, the end-effector of which should attain same position and orientation with the human arm end-effector. (b) A trajectory tracking example that involves a hyper-redundant robot arm-hand system that consists of a 23DOF hyper-redundant arm and a five-fingered hand with size equal to the 110% of the human hand size. For this example, the robot fingertips should track the human fingertip positions. The lines denote the human trajectories, and the markers the derived robot trajectories.

Grahic Jump Location
Fig. 7

Mapping human to robot motion for hyper-redundant robot arms with 21DOF and total length equal to the 80%, 90%, 100%, 110%, and 120% of the total human arm length. HA is the human arm.

Grahic Jump Location
Fig. 8

Solutions of the mapping problem for a 21DOF robot arm (that has the same length as the human arm) and different terms included in the objective function

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