Control Barrier Functions for Singularity Avoidance in Passivity-Based Manipulator Control

TL;DR

This paper introduces a convex-optimization-based control scheme for manipulation robots that guarantees singularity avoidance, passivity, and feasibility, improving safety and robustness in task-space control, especially near singular configurations.

Contribution

It presents a novel convex-optimization approach that ensures singularity avoidance and passivity in task-space passivity-based control, addressing limitations of existing methods.

Findings

Validated in simulation on a 7-DOF manipulator.

Guarantees singularity avoidance and passivity.

Applicable to various constraints like joint limits and contact.

Abstract

Task-space Passivity-Based Control (PBC) for manipulation has numerous appealing properties, including robustness to modeling error and safety for human-robot interaction. Existing methods perform poorly in singular configurations, however, such as when all the robot's joints are fully extended. Additionally, standard methods for constrained task-space PBC guarantee passivity only when constraints are not active. We propose a convex-optimization-based control scheme that provides guarantees of singularity avoidance, passivity, and feasibility. This work paves the way for PBC with passivity guarantees under other types of constraints as well, including joint limits and contact/friction constraints. The proposed methods are validated in simulation experiments on a 7 degree-of-freedom manipulator.