Thomas Libby

Samuel A. Burden, Thomas Libby, Samuel D. Coogan

Infinitesimal contraction analysis, wherein global asymptotic convergence results are obtained from local dynamical properties, has proven to be a powerful tool for applications in biological, mechanical, and transportation systems. The technique has primarily been developed for systems governed by a single, possibly nonsmooth, differential or difference equation. We generalize infinitesimal contraction analysis to hybrid systems governed by interacting differential and difference equations. Importantly, we leverage an intrinsic distance function to derive the first contraction results for hybrid systems without restrictions on mode sequence or dwell time. Our theoretical results are illustrated in several examples and applications.

Thomas Libby, Aaron M. Johnson, Evan Chang-Siu et al.

This paper develops a comparative framework for the design of actuated inertial appendages for planar, aerial reorientation. We define the Inertial Reorientation template, the simplest model of this behavior, and leverage its linear dynamics to reveal the design constraints linking a task with the body designs capable of completing it. As practicable inertial appendage designs lead to morphology that is generally more complex, we advance a notion of "anchoring" whereby a judicious choice of physical design in concert with an appropriate control policy yields a system whose closed loop dynamics are sufficiently captured by the template as to permit all further design to take place in its far simpler parameter space. This approach is effective and accurate over the diverse design spaces afforded by existing platforms, enabling performance comparison through the shared task space. We analyze examples from the literature and find advantages to each body type, but conclude that tails provide the highest potential performance for reasonable designs. Thus motivated, we build a physical example by retrofitting a tail to a RHex robot and present empirical evidence of its efficacy.