A constrained control-planning strategy for redundant manipulators

Abstract

This paper presents an interconnected control-planning strategy for redundant manipulators, subject to system and environmental constraints. The method incorporates low-level control characteristics and high-level planning components into a robust strategy for manipulators acting in complex environments, subject to joint limits. This strategy is formulated using an adaptive control rule, a computational efficient estimation of the robot's mathematical model and the nullspace of the constraints. A path is generated that takes into account the capabilities of the platform. The proposed method is computationally efficient, enabling its implementation on a real multi-body robotic system. Through experimental results with a 7 degree-of-freedom (DOF) manipulator, we demonstrate the performance of the method in real-world scenarios.