Literature DB >> 21708704

Quantifying dynamic stability and maneuverability in legged locomotion.

Robert J Full1, Timothy Kubow, John Schmitt, Philip Holmes, Daniel Koditschek.   

Abstract

Animals can swerve, dodge, dive, climb, turn and stop abruptly. Their stability and maneuverability are remarkable, but a challenge to quantify. Formal stability analysis can allow for quantitative comparisons within and among species. Stability analysis used in concert with a template (a simple, general model that serves as a guide for control) can lead to testable hypotheses of function. Neural control models postulated without knowledge of the animal's mechanical (musculo-skeletal) system can be counterproductive and even destabilizing. Perturbations actively corrected by reflex feedback in one direction can result in perturbations in other directions because the system is coupled dynamically. The passive rate of recovery from a perturbation in one direction differs from rates in other directions. We hypothesize that animals might exert less neural control in directions that rapidly recover via passive dynamics (e.g., in body orientation and rotation). By contrast, animals are likely to exert more neural control in directions that recover slowly or not at all via passive dynamics (e.g., forward velocity and heading). Neural control best enhances stability when it works with the natural, passive dynamics of the mechanical system. Measuring maneuverability is more challenging and new, general metrics are needed. Templates reveal that simple analyses of summed forces and quantification of the center of pressure can lead to valuable hypotheses, whereas kinematic descriptions may be inadequate. The study of stability and maneuverability has direct relevance to the behavior and ecology of animals, but is also critical if animal design is to be understood. Animals appear to be grossly over-built for steady-state, straight-ahead locomotion, as they appear to possess too many neurons, muscles, joints and even too many appendages. The next step in animal locomotion is to subject animals to perturbations and reveal the function of all their parts.

Entities:  

Year:  2002        PMID: 21708704     DOI: 10.1093/icb/42.1.149

Source DB:  PubMed          Journal:  Integr Comp Biol        ISSN: 1540-7063            Impact factor:   3.326


  38 in total

1.  Integration of intrinsic muscle properties, feed-forward and feedback signals for generating and stabilizing hopping.

Authors:  D F B Haeufle; S Grimmer; K-T Kalveram; A Seyfarth
Journal:  J R Soc Interface       Date:  2012-01-04       Impact factor: 4.118

2.  A direct comparison of local dynamic stability during unperturbed standing and walking.

Authors:  Hyun Gu Kang; Jonathan B Dingwell
Journal:  Exp Brain Res       Date:  2006-01-24       Impact factor: 1.972

3.  Running stability is enhanced by a proximo-distal gradient in joint neuromechanical control.

Authors:  M A Daley; G Felix; A A Biewener
Journal:  J Exp Biol       Date:  2007-02       Impact factor: 3.312

4.  Control of swing movement: influences of differently shaped substrate.

Authors:  Michael Schumm; Holk Cruse
Journal:  J Comp Physiol A Neuroethol Sens Neural Behav Physiol       Date:  2006-07-08       Impact factor: 1.836

Review 5.  Living in a physical world XII. Keeping up upward and down downward.

Authors:  Steven Vogel
Journal:  J Biosci       Date:  2007-09       Impact factor: 1.826

6.  Unsteady locomotion: integrating muscle function with whole body dynamics and neuromuscular control.

Authors:  Andrew A Biewener; Monica A Daley
Journal:  J Exp Biol       Date:  2007-09       Impact factor: 3.312

7.  Climbing, falling, and jamming during ant locomotion in confined environments.

Authors:  Nick Gravish; Daria Monaenkova; Michael A D Goodisman; Daniel I Goldman
Journal:  Proc Natl Acad Sci U S A       Date:  2013-05-20       Impact factor: 11.205

8.  A single muscle's multifunctional control potential of body dynamics for postural control and running.

Authors:  Simon Sponberg; Andrew J Spence; Chris H Mullens; Robert J Full
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2011-05-27       Impact factor: 6.237

9.  Variability in motor learning: relocating, channeling and reducing noise.

Authors:  R G Cohen; D Sternad
Journal:  Exp Brain Res       Date:  2008-10-25       Impact factor: 1.972

Review 10.  Movement variability near goal equivalent manifolds: fluctuations, control, and model-based analysis.

Authors:  Joseph P Cusumano; Jonathan B Dingwell
Journal:  Hum Mov Sci       Date:  2013-11-07       Impact factor: 2.161

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