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Goldman, Daniel I.

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Author: Goldman, Daniel I. × End date: 2019 × Start date: 2019 ×

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    Robophysics: Physics Meets Robotics
    (Georgia Institute of Technology, 2019-10-30) Goldman, Daniel I.
    Robots will soon move from the factory floor and into our lives (e.g., autonomous cars, package delivery drones, and search-and-rescue devices). However, compared to living systems, robot capabilities in complex environments are limited. I believe the mindset and tools of physics can help facilitate the creation of robust self-propelled autonomous systems. This “robophysics” approach – the systematic search for novel dynamics and principles in robotic systems – can aid the computer science and engineering approaches that have proven successful in less complex environments. The rapidly decreasing cost of constructing sophisticated robot models with easy access to significant computational power bodes well for such interactions. Drawing from examples in the work of my group and our collaborators, I will discuss how robophysical studies have inspired new physics questions in low dimensional dynamical systems (e.g., creation of analog quantum mechanics and gravity systems) and soft matter physics (e.g., emergent capabilities in ensembles of active “particles”). These studies have been useful to develop insight for biological locomotion in complex terrain (e.g., control targets via optimizing geometric phase) and have begun to aid engineers in the creation of devices that begin to achieve life-like locomotor abilities on and within complex environments (e.g., semi-soft myriapod robots).
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    Tracked data for Chionactis occipitalis through a post array
    (Georgia Institute of Technology, 2019-01-25) Schiebel, Perrin E. ; Rieser, Jennifer M. ; Hubbard, Alex M. ; Chen, Lillian ; Rocklin, D. Zeb ; Goldman, Daniel I.
    Limbless animals like snakes inhabit most terrestrial environments, generating thrust to overcome drag on the elongate body via contacts with heterogeneities. The complex body postures of some snakes and the unknown physics of most terrestrial materials frustrates understanding of strategies for effective locomotion. As a result, little is known about how limbless animals contend with unplanned obstacle contacts. We studied a desert snake, Chionactis occipitalis, which uses a stereotyped head-to-tail traveling wave to move quickly on homogeneous sand. In laboratory experiments, we challenged snakes to move across a uniform substrate and through a regular array of force sensitive posts. The snakes were reoriented by the array in a manner reminiscent of the matter-wave diffraction of subatomic particles. Force patterns indicated the animals did not change their self-deformation pattern to either avoid or grab the posts. A model using open-loop control incorporating previously described snake muscle activation patterns and body-buckling dynamics reproduced the observed patterns, suggesting a similar control strategy may be used by the animals. Our results reveal how passive dynamics can benefit limbless locomotors by allowing robust transit in heterogeneous environments with minimal sensing.
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    Mitigating memory effects during undulatory locomotion on hysteretic materials dataset
    ( 2019) Schiebel, Perrin E. ; Rieser, Jennifer M. ; Astley, Henry C. ; Agarwall, S. ; Hubicki, C. ; Hubbard, Alex M. ; Cruz, K. ; Mendelson, J. ; Kamrin, K. ; Goldman, Daniel I.
    Undulatory swimming in flowing media like water is well studied, but little is known about locomotion in environments that are permanently deformed by body-substrate interactions like snakes in sand, eels in mud, and nematode worms in rotting fruit. We study the desert-specialist snake Chionactis occipitalis traversing granular matter and find body inertia is negligible despite rapid transit. New surface resistive force theory (RFT) calculation reveals this snake's waveform minimizes material memory effects and optimizes speed given anatomical limitations (power). RFT explains the morphology and waveform dependent performance of a diversity of non-sand-specialists, but over-predicts the capability of snakes with high slip. Robophysical experiments recapitulate aspects of these failure-prone snakes, elucidating how reencountering previously remodeled material hinders performance. This study reveals how memory effects stymied the locomotion of snakes in our previous study [Marvi et al, Science, 2014] and suggests the existence of a predictive model for history-dependent locomotion.