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An Input-to-State Stability Perspective on Robust Locomotion

Maegan Tucker, Aaron D. Ames

Year
2023
Citations
4

Abstract

Uneven terrain necessarily transforms periodic walking into a non-periodic motion. As such, traditional stability analysis tools no longer adequately capture the ability of a bipedal robot to locomote in the presence of such disturbances. This motivates the need for analytical tools aimed at generalized notions of stability – robustness. Towards this, we propose a novel definition of robustness, termed <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula><i>-robustness</i>, to characterize the domain on which a nominal periodic orbit remains stable despite uncertain terrain. This definition is derived by treating perturbations in ground height as disturbances in the context of the input-to-state-stability (ISS) of the extended Poincaré map associated with an orbit. The main theoretic result is the formulation of robust Lyapunov functions that certify <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula>-robustness of periodic orbits. This yields an optimization framework for verifying <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula>-robustness, which is demonstrated in simulation with a bipedal robot walking on uneven terrain.

Keywords

Robustness (evolution)TerrainControl theory (sociology)Lyapunov functionComputer scienceRobotPeriodic orbitsLyapunov stabilityMathematicsArtificial intelligence

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