TY - JOUR
T1 - Reconfigurable curved beams for selectable swimming gaits in an underwater robot
AU - Sharifzadeh, Mohammad
AU - Jiang, Yuhao
AU - Aukes, Daniel M.
N1 - Funding Information:
Manuscript received October 15, 2020; accepted February 6, 2021. Date of publication March 4, 2021; date of current version March 23, 2021. This letter was recommended for publication by Associate Editor G. Endo and Editor C. Gosselin upon evaluation of the reviewers’ comments. This work was supported by the National Science Foundation under Grant 1935324. (Corresponding author: Daniel Aukes.) Mohammad Sharifzadeh and Daniel M. Aukes are with the Polytechnic School, Fulton Schools of Engineering, Arizona State University, Mesa, AZ 85212 USA (e-mail: sharifzadeh@asu.edu; danaukes@asu.edu).
Publisher Copyright:
© 2016 IEEE.
PY - 2021/4
Y1 - 2021/4
N2 - Rowing is a swimming motion employed by a number of animals via tuned passive biomechanics and active gait strategies. This gait generates positive net thrust (or moment) by having a higher drag profile in the power stroke compared with the recovery stroke, which is obtained via faster actuation speed or higher effective area. In this letter, we show that using the preferential buckling of curved beams in swimming robots can, via a passive reduction of effective area in recovery stroke, be used to generate positive net thrust and moment. Additionally, these curved beams can be actively tuned to alter their behavior on demand for use in swimming applications, and can be used in an underwater robot to switch between rowing and flapping gaits. A dynamic model has been developed to model the swimming behavior of a robot using buckling joints. A design optimization has been carried out, using the Covariance Matrix Adaption Evolution Strategy (CMA-ES), to find the design and gait parameters that maximize the robot's forward swimming speed. A series of experimental gait searches have subsequently been conducted on the resulting optimal design, again using CMA-ES with the goal of finding the optimal gait pattern across a number of swimming strategies such as paddling, flapping, and undulation. By actively altering the curved beam's buckling limits, an untethered robot has been developed that maneuvers in water across each of these swimming strategies. The findings suggest that tuning the preferential buckling limits of curved beams can be an effective and potentially advantageous approach for producing directional thrust and moments.
AB - Rowing is a swimming motion employed by a number of animals via tuned passive biomechanics and active gait strategies. This gait generates positive net thrust (or moment) by having a higher drag profile in the power stroke compared with the recovery stroke, which is obtained via faster actuation speed or higher effective area. In this letter, we show that using the preferential buckling of curved beams in swimming robots can, via a passive reduction of effective area in recovery stroke, be used to generate positive net thrust and moment. Additionally, these curved beams can be actively tuned to alter their behavior on demand for use in swimming applications, and can be used in an underwater robot to switch between rowing and flapping gaits. A dynamic model has been developed to model the swimming behavior of a robot using buckling joints. A design optimization has been carried out, using the Covariance Matrix Adaption Evolution Strategy (CMA-ES), to find the design and gait parameters that maximize the robot's forward swimming speed. A series of experimental gait searches have subsequently been conducted on the resulting optimal design, again using CMA-ES with the goal of finding the optimal gait pattern across a number of swimming strategies such as paddling, flapping, and undulation. By actively altering the curved beam's buckling limits, an untethered robot has been developed that maneuvers in water across each of these swimming strategies. The findings suggest that tuning the preferential buckling limits of curved beams can be an effective and potentially advantageous approach for producing directional thrust and moments.
KW - And learning for soft robots
KW - Compliant joints and mechanisms
KW - Control
KW - Modeling
KW - Soft robot applicationsc
KW - Soft robot materials and design
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U2 - 10.1109/LRA.2021.3063961
DO - 10.1109/LRA.2021.3063961
M3 - Article
AN - SCOPUS:85102312983
SN - 2377-3766
VL - 6
SP - 3437
EP - 3444
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 2
M1 - 9369911
ER -