Controlling Pivoting Gait using Graph Model Predictive Control

TL;DR

This paper presents a graph model predictive control approach for robustly managing pivoting gait in robots, enabling stable object manipulation under external disturbances by switching between two gait modes.

Contribution

It introduces a novel control framework that adaptively switches gait modes using graph model predictive control for improved stability during pivoting gait.

Findings

Robust pivoting gait achieved with external disturbance handling.

Adaptive switching between gait modes enhances stability.

Effective manipulation of objects with varying weights.

Abstract

Pivoting gait is efficient for manipulating a big and heavy object with relatively small manipulating force, in which a robot iteratively tilts the object, rotates it around the vertex, and then puts it down to the floor. However, pivoting gait can easily fail even with a small external disturbance due to its instability in nature. To cope with this problem, we propose a controller to robustly control the object motion during the pivoting gait by introducing two gait modes, i.e., one is the double-support mode, which can manipulate a relatively light object with faster speed, and the other is the quadruple-support mode, which can manipulate a relatively heavy object with lower speed. To control the pivoting gait, a graph model predictive control is applied taking into account of these two gait modes. By adaptively switching the gait mode according to the applied external disturbance, a robot can stably perform the pivoting gait even if the external disturbance is applied to the object.