Empirical Potential Functions for Driving Bioinspired Joint Design

[+] Author and Article Information
Matthew Bender

PhD Candidate, ASME Student Member, Dept. of Mech. Eng., Virginia Tech, Blacksburg, Virginia 24060

Aishwarya George

Masters Student, Dept. of Elect. and Comp. Eng., Virginia Tech, Blacksburg, VA 24060

Nathan Powell

PhD Student, Dept of Mech. Eng., Virginia Tech, Blacksburg, Virginia 24060

Andrew Kurdila

W. Martin Johnson Professor, Fellow of ASME, Dept. of Mech. Eng., Virginia Tech, Blacksburg, VA 24060

Rolf Müller

Professor, Department of Mechanical Engineering, Virginia Tech, Blacksburg, Virginia 24060, Director, SDU - VT International Lab, Shandong University, Jinan, Shandong 250100 China

1Corresponding author.

ASME doi:10.1115/1.4041446 History: Received February 13, 2018; Revised September 06, 2018


Bioinspired design of robotic systems can offer many potential advantages in comparison to traditional architectures including improved adaptability, maneuverability, or efficiency. Substantial progress has been made in the design and fabrication of bioinspired systems. While many of these systems are bioinspired at a system architecture level, the design of linkage connections often assumes that motion is well approximated by ideal joints subject to designer-specified box constraints. However, such constraints can allow a robot to achieve unnatural and potentially unstable configurations. In contrast, this paper develops a methodology which identifies the set of admissible configurations from experimental observations and optimizes a compliant structure around the joint such that motions evolve on or close to the observed configuration set. This approach formulates an analytical-empirical potential energy field which "pushes" system trajectories towards the set of observations. Then, the strain energy of a compliant structure is optimized to approximate this energy field. While our approach requires that kinematics of a joint be specified by a designer, the optimized compliant structure enforces constraints on joint motion without requiring an explicit definition of box-constraints. To validate our approach we construct a 1-DOF elbow joint which closely matches the analytical-empirical and optimal potential energy functions and admissible motions remain within the observation set.

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