Snake robot uncovers secrets to sidewinders' maneuverability.

A hallmark of animal life is the ability to move through the environment to catch prey, avoid predators, or find mates. Animals achieve this using a staggering diversity of locomotor strategies despite having similar body shape and being subjected to similar physics—e.g., gazelles pronk and cheetahs gallop. These differences in strategy may allow animals to fill different ecological niches by affording more (or less) stability, maneuverability, speed, efficiency, and stealth. Many animals rely on specialized appendages—limbs, fins, and wings—that reciprocate to produce forward motion. However, some organisms move using a completely different strategy that involves the generation of undulatory traveling waves that propagate along the body or a specialized elongated fin. Most studies of such undulatory locomotion have focused on the role of a single, in-plane wave that travels from head-to-tail to produce forward thrust, as seen for example in aquatic animals such as eels, lampreys, and leeches (1, 2). In PNAS, Astley et al. (3) present behavioral data that suggest a role for multiplane body undulations in sidewinding snakes to achieve turning maneuvers. Specifically, they observe that rattlesnakes adjust the relative amplitude and timing of the horizontal and vertical waves and that these changes are, in turn, correlated with shallow and sharp turning. Of course, correlations do not prove a mechanistic relationship, so the investigators looked for a complementary approach to determine whether these shifts in the traveling waves are indeed responsible for the animal’s extraordinary maneuverability. A natural approach for understanding such biomechanical mechanisms is the use of models—either computational (simulations) or physical (robots). Complex and nonlinear interactions between an animal and its environment are often difficult to capture using computer simulations. Even as computers get faster, predicting the motion of a robot or animal subject to these complex interactions is akin to predicting the weather. This might be in part due to a lack of understanding of individual “components” (muscles, limbs, substrate) or due to lack of knowledge of [and extreme sensitivity to (4)] the detailed interactions between multiple components. These complexities can be addressed using experimental robotics, i.e., physical models. Recent improvements in the mechanical design and manufacturing of robots and the ability to reproduce naturalistic movements and morphologies (i.e., improved “biofidelity”) has increased the success of using robots to understand biological systems (4–9). In particular, strides have been made toward