# Fault Diagnosis of a Reconfigurable Crawling-Rolling Robot Based on Support Vector Machines
## 1 Introduction
The case study indicates that this approach can detect faults accurately. A fault diagnosis approach for robotic manipulation using a Linear SVM is proposed in [11]. The robotic manipulator used an external thread fastening application, which provided two feature parameters; namely, estimated insertion length and maximum reaction force. The result indicates that an accuracy of above 90% was achieved when evaluating the performance of the classifier. In [12], SVM and Cuckoo Search (CS) algorithms are used for fault diagnosis in a hydraulic system equipped with quadruped robots. The Rough set is used in the dimensionality reduction of the features. The proposed method, which uses SVM along with a CS algorithm and a rough set, provides a higher accuracy than the traditional SVM model. In [13], SVM is used for fault diagnosis in and analysis of the control software of mobile robots. The operation parameter of the robot under various scenarios is used to train and test the SVM classifier. Even though numerous studies apply machine learning techniques for fault diagnosis in robotic systems, these works are often limited to fixed-morphology robotic systems. This article reports an application of a machine learning approach for fault diagnosis of a reconfigurable robot that assumes multiple morphologies. The main departure from the state-of-the-art in this area is that a large set of fault types are generated and handled due to shapeshifting characteristics and extended locomotion gaits in the case of reconfigurable robots. Our aim is to build reconfigurable robots that can autonomously detect and overcome faults by changing their morphologies and any associated locomotion modes. There have been many advances in the field of reconfigurable robotics over the past decade. The reconfigurable robot presented in [14] transforms itself from a four-wheeled driving mechanism to a two-legged walking mechanism having two degrees of freedom, with each leg controlled using the same set of actuators. The legged mode in this reconfigurable robot would provide it with the capability to get past any obstacles, while the wheeled mode would often be used to travel faster and provide better efficiency. Another research study, detailed in [15], discusses system modeling issues for an interconnected underwater reconfigurable robot. Hydrostatic and hydrodynamic tests were used to develop a dynamic model for the robot of interest. A simulation environment was proposed for a modular and reconfigurable robotic manipulator in [16]. This work validated the use of the proposed simulator for the testing of newly designed robots using the automatically generated codes for kinematics and a controller. The robot proposed in [17] can crawl, jump, travel through rough terrain, and get past obstacles in its path. Experimental results show that it moves at a rate of 260 mm per 10 s, can climb a sliding surface with a slope of 20 degrees, and can jump 80 mm. Even though a vast amount of literature on reconfigurable robotics is available, they are often limited to mechanism designs with primitive pointers of fault diagnosis. Additionally, none of the work on reconfigurable robotics deals with the application of machine learning approaches to the fault diagnosis problem, presenting numerous opportunities for research and development. Fault diagnosis is a critical feature required for a reconfigurable robot, as it provides the knowledge of optimal morphology to compensate for a fault. In this paper, we present an SVM-based fault diagnostics system for our bioinspired reconfigurable robot, Scorpio. The diagnostic system detects and classifies faults for crawling and rolling locomotion modes. Specifically, we classify between faulty and non-faulty conditions across nine different locomotion gaits, each with rolling and crawling modes, over three different speeds.