DCL Research Spotlight: Alex Chang

Biologically Inspired Locomotion in Autonomous Robots

Through their bio-inspired morphology and motion strategies, snake-like robots strive to appropriate locomotive versatilities similar to those observed in their biological counterpart. In particular, these mechanisms are advantaged in a variety of challenging locomotive scenarios for which traditionally wheeled or tracked platforms are often denied access or must operate in a degraded fashion. Tools and methods supporting practical locomotion and control of snake-like robots, however, remain scarce. We develop a shape-centric continuous body model of three distinctly advantaged gaits: traveling wave rectilinear motion, lateral undulation and sidewinding. Through repeated numerical simulation of each gait's dynamics, empirical characterizations of averaged steady-behavior body velocity are obtained, with respect to each gait's parameter space. This control-to-action map simplifies a complex dynamical system to a differential drive-like kinematic model where system motion is dictated by a set of intuitive, geometrically-oriented gait parameters. The control-to-action map is applied to pivotally inform optimal trajectory synthesis as well as follow-on feedback trajectory tracking in order to traverse arbitrary obstacle scenarios. The planning and control framework for each gait then serves as a foundation for higher-level locomotive planning, whereby navigation may additionally consider employing mixtures of gaits to accomplish locomotive objectives.

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  • Alex Chang

    Alex Chang