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 (AE) potential energy field, which “pushes” system trajectories toward 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 single degree-of-freedom elbow joint, which closely matches the AE and optimal potential energy functions and admissible motions remain within the observation set.
Empirical Potential Functions for Driving Bioinspired Joint Design
Department of Mechanical Engineering,
Blacksburg, VA 24060;
Jinan, Shandong 250100, China
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received February 13, 2018; final manuscript received September 6, 2018; published online October 31, 2018. Assoc. Editor: Jongeun Choi.
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Bender, M., George, A., Powell, N., Kurdila, A., and Müller, R. (October 31, 2018). "Empirical Potential Functions for Driving Bioinspired Joint Design." ASME. J. Dyn. Sys., Meas., Control. March 2019; 141(3): 031004. https://doi.org/10.1115/1.4041446
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