Combining multi-localization methods for fault diagnosis in autonomous mobile robot systems

Autonomous mobile robots have been widely employed for many applications in indoor and outdoor environments. Most of these robots have to operate in environments where human intervention is expensive, slow, unreliable or even impossible. It is therefore essential for robots to monitor their behavior to diagnose and address faults before they result in catastrophic failures. In this paper we introduce a new approach to diagnose faults of autonomous mobile robot systems. The proposed methodology firstly computes the poses of the robot by using the onboard stereo camera, the wheels' encoders and the commanded velocities, respectively. Then, the residuals between each pair of the localization methods are used to evaluate the occurrence of faults. Experimental tests, in ideal fault free cases, have been carried out to find a reference threshold for each residual. A bool value is assigned to each residual by comparing it with the respective threshold. The bool values of all residuals are then combined and used to detect and isolate a fault in the robotic system. The pose of ground truth, obtained from a motion capture system, is used here to evaluate the errors of the poses obtained from three localization methods and validate their accuracy. Our approach can potentially detect and identify different faults of the robot systems. Experimental tests have shown its effectiveness in determine fault on the robot's wheel.