A fault tolerance framework for cooperative robotic manipulators

Abstract

The problem of fault tolerance in cooperative manipulators rigidly connected to an undeformable load is addressed in this paper. Four categories of faults are considered: free-swinging joint faults (FSJFs), locked joint faults (LJFs), incorrectly measured joint position faults (JPFs), and incorrectly measured joint velocity faults (JVFs). Free-swinging and locked joint faults are detected via artificial neural networks (ANNs). Incorrectly measured joint position and velocity faults are detected by considering the kinematic constraints of the cooperative system. When a fault is detected, the control system is reconfigured according to the nature of the isolated fault and the task is resumed to the largest extent possible. The fault tolerance framework is applied to an actual system composed of two cooperative robotic manipulators. The results presented demonstrate the feasibility and performance of the methodology.