TY - GEN
T1 - Mechanical specialization of robotic limbs
AU - Cahill, Nathan M.
AU - Ren, Yi
AU - Sugar, Thomas
N1 - Funding Information:
*Research supported by National Science Foundation Graduate Research Fellowship Program and SpringActive, Inc. 1Nathan M. Cahill with Arizona State University, NSF Fellow (corresponding author); (e-mail: nathan.cahill@asu.edu) 4Yi Ren is a professor with Ira. A. Fulton Schools of Engineering, Arizona State University 5Thomas G. Sugar is a professor with The Polytechnic School, Ira. A. Fulton Schools of Engineering, Arizona State University, (e-mail:thomas.sugar@asu.edu).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/21
Y1 - 2017/7/21
N2 - In this paper we introduce a design framework that permits task-specific complex geometries in robotic limbs with the minimal power consumption. Additionally we present a optimal gear ratio selection algorithm with realistic constraints, which we use as a subroutine within the geometry optimization. As a case study we optimize the a spatial, hybrid parallel-serial robotic limb structure with a large set of geometric parameters. Optimal design with respect to this mechanism produces three locally optimal families of designs. These are analyzed rigorously and a best design was chosen. A prototype has been constructed from the chosen design family, proving that the approach is practical. This serves as evidence that the design optimization method is an effective tool to minimize the electrical cost of a given task, and thus specialize the design.
AB - In this paper we introduce a design framework that permits task-specific complex geometries in robotic limbs with the minimal power consumption. Additionally we present a optimal gear ratio selection algorithm with realistic constraints, which we use as a subroutine within the geometry optimization. As a case study we optimize the a spatial, hybrid parallel-serial robotic limb structure with a large set of geometric parameters. Optimal design with respect to this mechanism produces three locally optimal families of designs. These are analyzed rigorously and a best design was chosen. A prototype has been constructed from the chosen design family, proving that the approach is practical. This serves as evidence that the design optimization method is an effective tool to minimize the electrical cost of a given task, and thus specialize the design.
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U2 - 10.1109/ICRA.2017.7989482
DO - 10.1109/ICRA.2017.7989482
M3 - Conference contribution
AN - SCOPUS:85027996890
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 4187
EP - 4192
BT - ICRA 2017 - IEEE International Conference on Robotics and Automation
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Robotics and Automation, ICRA 2017
Y2 - 29 May 2017 through 3 June 2017
ER -