Latest Papers

ASME Journal of Mechanisms and Robotics

    Direct Encoding of Tunable Stiffness Into an Origami-Inspired Jumping Robot Leg

    Abstract

    The stiffness of robot legs greatly affects legged locomotion performance; tuning that stiffness, however, can be a costly and complex task. In this paper, we directly tune the stiffness of jumping robot legs using an origami-inspired laminate design and fabrication method. In addition to the stiffness coefficient described by Hooke’s law, the nonlinearity of the force-displacement curve can also be tuned by optimizing the geometry of the mechanism. Our method reduces the number of parts needed to realize legs with different stiffness while simplifying manual redesign effort, lowering the cost of legged robots while speeding up the design and optimization process. We have fabricated and tested the leg across six different stiffness profiles that vary both the nonlinearity and coefficient. Through a vertical jumping experiment actuated by a DC motor, we also show that proper tuning of the leg stiffness can result in an 18% improvement in lift-off speed and an increase of 19% in peak power output.

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    Journal of Mechanisms and Robotics Open Issues