Safe Obstacle Avoidance Planning-Control Scheme for Multiconstrained Mobile Manipulators

Abstract

To achieve high precision and safety in the operation of a wheeled mobile manipulator, it is imperative that the robot possesses the capability for high-precision tracking while adhering to multiple physical constraints and avoiding obstacles. This article introduces a novel approach that combines model predictive control (MPC) with prescribed performance function (PPF) to address these challenges. At the kinematic level, we leverage MPC's predictive capabilities to optimize the robot's motion for a better reference velocity while taking into account the preestablished velocity tracking error bounds defined by PPF. On the dynamics level, the control law is designed based on PPF, ensuring precise tracking of the reference velocity and desired end-effector trajectory. The validity and effectiveness of this method are rigorously validated through a series of empirical experiments.