An energy landscape approach to locomotor transitions in complex 3D terrain

Abstract

Effective locomotion in nature happens by transitioning across multiple modes (e.g., walk, run, climb). Using laboratory experiments on a model system, we demonstrate that an energy landscape approach helps understand how multipathway transitions across locomotor modes in complex 3D terrain statistically emerge from physical interaction. Animals’ and robots’ locomotor modes are attracted to basins of a potential energy landscape. They can use kinetic energy fluctuation from oscillatory self-propulsion to cross potential energy barriers, escaping from one basin and reaching another to make locomotor transitions. Our first-principle energy landscape approach is the beginning of a statistical physics theory of locomotor transitions in complex terrain. It will help understand and predict how animals, and how robots should, move through the real world.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jun 15, 2020
Source ID
10.1073/pnas.1918297117

Entities

People

  • Chen Li
  • George Thoms
  • Ratan Othayoth

Organizations

  • Army Research Office
  • Arnold and Mabel Beckman Foundation
  • Burroughs Wellcome Fund
  • Johns Hopkins University

Tags

Fields of Study

  • Physics

Readers

  • Neural Network Machine Learning.
  • Quantum Chemistry
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy