Balancing and Step Recovery Capturability via Sums-of-Squares Optimization
Name
09.pdf
Description
Accepted version
Size
752.84 KB
Format
Adobe PDF
Checksum (MD5)
e76580dd72f9002008ad028c21c508e3
Author(s)
Posa, Michael Antonio
Koolen, Twan
Tedrake, Russell L
Date Issued
July 2017
Journal
Robotics: Science and Systems XIII
Publisher
Robotics: Science and Systems Foundation
Citation
Posa, Michael, et al. “Balancing and Step Recovery Capturability via Sums-of-Squares Optimization.” Robotics: Science and Systems XIII, July, 2017, Cambridge, Massachusetts, Robotics: Science and Systems Foundation, 2017.
Version
Author's final manuscript
Abstract
A fundamental requirement for legged robots is to maintain balance and prevent potentially damaging falls
whenever possible. As a response to outside disturbances, fall prevention can be achieved by a combination of active balancing actions, e.g. through ankle torques and upper-body motion, and through reactive step placement. While it is widely accepted that stepping is required to respond to large disturbances, the limits of active motions on balancing and step recovery are only well understood for the simplest of walking models. Recent advances in convex optimization-based verification and control techniques enable a more complete understanding of the limits and capabilities of more complex models. In this work, we present an algorithmic approach for formal analysis
of the viable-capture basins of walking robots, calculating both inner and outer approximations and corresponding push recovery control strategies. Extending beyond the classic Linear Inverted Pendulum Model (LIPM), we analyze a series of centroidal momentum based planar walking models, examining the effects of center of mass height, angular momentum, and impact dynamics during stepping on capturability. This formal analysis enables an
explicit calculation of the differences between these models, and assessment of whether the simplest models ultimately sacrifice capability, and thus stability, when designing push recovery control policies
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.15607/RSS.2017.XIII.032
