Control Barrier Functions for Singularity Avoidance in Passivity-Based Manipulator Control
This work addresses a critical issue for robotic manipulation systems, particularly in human-robot interaction, by enabling safe and robust control in singular configurations, though it is incremental as it builds on existing PBC methods.
The paper tackled the problem of singularity avoidance and passivity guarantees in task-space Passivity-Based Control (PBC) for manipulators, proposing a convex-optimization-based control scheme that ensures singularity avoidance, passivity, and feasibility, validated in simulation on a 7-DOF manipulator.
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.