Teaching RoboClam to dig: The design, testing, and genetic algorithm optimization of a biomimetic robot

Abstract

Razor clams (Ensis directus) are one of nature's most adept burrowing organisms, able to dig to 70cm at nearly 1cm/s using only 0.21J/cm. We discovered that Ensis reduces burrowing drag by using motions of its shell to fluidize a thin layer of substrate around its body. We have developed RoboClam, a robot that digs using the same mechanisms as Ensis, to explore how localized fluidization burrowing can be extended to engineering applications. In this work we present burrowing performance results of RoboClam in Ensis' habitat. Using a genetic algorithm to optimize RoboClam's kinematics, the machine was able to burrow at speeds comparable to Ensis, with a power law relationship between digging energy and depth of n = 1.17, close to the n = 1 achieved by the animal. Pushing through static soil has a theoretical energy-depth power law of n = 2, which means that Ensis-inspired digging motions can provide exponential energetic savings over existing burrowing methods.