# FPGA-Based Methodology for Detecting Positional Accuracy Degradation in Industrial Robots
###### Abstract
An expert system for detecting a deviation in the joints of a robot is proposed in [18] and is based on DWT analysis, neural networks, and fractal and energy features. Additionally, methods based on artificial vision systems to carry out studies on the deviation of the robot have been proposed in [19; 20; 21; 22], they have even been complemented with re-calibration methods through kinematic analysis of the robot as the work presented in [23]. It is important to note that some of the presented methods involve performing analysis on personal computers (PCs) after the necessary data acquisition, thus operating offline. Therefore, integrating robot failure or position degradation analysis methods into reconfigurable processing systems, such as Field Programmable Gate Arrays (FPGAs), provides the opportunity to perform non-invasive information analysis, online or offline, quickly and efficiently. In this sense, it is worth noting that numerous FPGA-based approaches have been developed tailored for robotics. For example, [PERSON] et al. [24] propose an FPGA-based torque predictor control focused on legged robots. The torque estimation is performed considering the delay in the calculation and measurement, improving the prediction and period of the controller, and obtaining a better controller performance. The acceleration of the kinematic models of a 6-degree-of-freedom (DOF) robot arm implemented in FPGA is presented in [25], emphasizing the inverse kinematic model required in the robot motion. The speed and accuracy of the processing of the proposed method demonstrate its effectiveness in real-time robot motion. Besides, the implementation in FPGA of a control system for a 3 DOF robot with pneumatic actuators is presented in [26]. Proportional integral derivative controllers (PID) are used for air valve management and position control of the robot. As well as a neural network is used to tune the position controller gains, and although it obtains some disturbances attributed to the ANN tuning process, the results are satisfactory. [PERSON] et al. [27] introduced a balance controller for a humanoid robot. The system, implemented on an FPGA, comprises several stages, including external force detection, recovery balance control, trajectory planning, and inverse kinematics. Their method enables the humanoid robot to effectively recover its balance after experiencing external force. In addition, in [28], a method based on artificial vision is proposed to detect the deviation in a conveyor belt in real time using FPGAs and the Line Segment Detection algorithm (LSD), obtaining a high value of images processed per second, which improves the performance in real-time of the fault detection method. There have also been developed works focused on modular, reconfigurable robots (MRR) as [29], where a distributed computing system for multiple object tracking based on FPGA and Kalman filters is presented. It uses specialized computational cores (SCCs) and a strategy to use the least amount of FPGA resources for real-time motion control tasks of MRR. They reconfigure the FPGA by replacing complex models with simpler SCCs to reduce power consumption. The results show better performance, resource usage, and computational accuracy than high-performance CPUs. Furthermore, [PERSON] et al. [30] propose implementing the dynamic model of robots on CPU, GPU, and FPGA through the Recursive Newton-Euler Algorithm (RNEA) computation. The results demonstrate the superior performance of the GPU and FPGA over the CPU as the number of computations increases. Furthermore, works have also been developed on manufacturing equipment such as computer numerical control machines (CNC); for example, [PERSON]. [31] proposes an anomaly detection system in the milling process based on FPGA. The extraction of features in the frequency domain of machine vibration signals is performed using discrete Fourier transform (DFT), and this information is used to enter an auto-associative neural network (AANN) for anomaly detection, reporting satisfactory results offline.