Departmental Papers (ESE)
Abstract
Legged robots are by nature strongly non-linear, high-dimensional systems whose full complexity permits neither tractable mathematical analysis nor comprehensive numerical study. In consequence, a growing body of literature interrogates simplified “template” [1], [2] models—to date almost exclusively confined to sagittal- or horizontal-plane motion—with the aim of gaining insight into the design or control of the far messier reality. In this paper we introduce a simple bounding-in-place (“BIP”) model as a candidate frontal plane template for straight-ahead level ground running and explore its use in formulating hypotheses about whether and why rolling motion is important in legged locomotion. Numerical study of left-right compliance asymmetry in the BIP model suggests that compliance ratios yielding lowest steady state roll suffer far longer disturbance recovery transients than those promoting greater steady state roll. We offer preliminary experimental data obtained from video motion capture data of the frontal plane disturbance recovery patterns of a RHex-like hexapod suggesting a correspondence to the conclusions of the numerical study. Fig. 1. EduBot [19], a RHex-like [20] hexapedal robot.
Jonathan Clark was supported by the IC Postdoctoral Fellow Program under grant number HM158204-1-2030. Samuel Burden was supported by the SUNFEST REU program at the University of Pennsylvania. This work was also partially supported by the NSF FIBR grant #0425878. For more information: Kod*Lab
Document Type
Conference Paper
Subject Area
GRASP, Kodlab
Date of this Version
4-2007
Date Posted: 05 November 2013
This document has been peer reviewed.
Comments
BibTeX entry
@inproceedings{Burden-Clark-Weingarten.ICRA2007, author = {Burden, S. and Clark, J. and Weingarten, J. and Komsuoglu, H. and Koditschek, D. E.}, title = {Heterogeneous Leg Stiffness and Roll in Dynamic Running}, booktitle = {Proceedings of IEEE Conference of Robotics and Automation}, year = {2007} }
Jonathan Clark was supported by the IC Postdoctoral Fellow Program under grant number HM158204-1-2030. Samuel Burden was supported by the SUNFEST REU program at the University of Pennsylvania. This work was also partially supported by the NSF FIBR grant #0425878.
Copyright 2007 IEEE. Reprinted from Proceedings of IEEE Conference of Robotics and Automation.
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