Robotic systems are essential to technological development in the industrial, medical, and aerospace sectors. Nevertheless, their use in different applications requires that the robot have the best possible execution efficiency. For this, a robot with specific optimized characteristics is necessary to impact the performance of the specific task. Different techniques have been used in robot optimization, the most widely used being genetic algorithms (GA) and particle swarm optimization (PSO). However, there are optimization algorithms with high convergence speeds inspired by animal behavior, whose application in robot optimization has not been reported. In this work, bio-inspired algorithms Harris hawks optimization (HHO) and Grey wolf optimizer (GWO) are applied to a six-degree-of-freedom (6DOF) robot arm design through kinematic optimization. The lengths of the main robot links are optimized to improve the workspace volume and obtain a better-conditioned robot using the structural length index (SLI) and global condition index (GCI) as objective functions. A comparison is made between the proposed algorithms and GA and PSO regarding convergence speed, computational load, and optimality. Similar behavior has been found for HHO, GWO, and PSO compared to GA for both indexes. For the GCI problem, an average improvement of 14% was found when optimizing an industrial robot arm. Furthermore, multiple runs experiment is performed to test the robustness of the algorithms. The results show that HHO is the best technique to obtain an optimal robot design because it needs less than ten iterations to provide a better result despite its computational load.INDEX TERMS Evolutionary computation, genetic algorithms, grey wolf optimizer, harris hawk optimizer, particle swarm optimization, robot kinematics.

Industrial processes involving manipulator robots require accurate positioning and orienting for high-quality results. Any decrease in positional accuracy can result in resource wastage. Machine learning methodologies have been proposed to analyze failures and wear in electronic and mechanical components, affecting positional accuracy. These methods are typically implemented in software for offline analysis. In this regard, this work proposes a methodology for detecting a positional deviation in the robot’s joints and its implementation in a digital system of proprietary design based on a field-programmable gate array (FPGA) equipped with several developed intellectual property cores (IPcores). The method implemented in FPGA consists of the analysis of current signals from a UR5 robot using discrete wavelet transform (DWT), statistical indicators, and a neural network classifier. IPcores are developed and tested with synthetic current signals, and their effectiveness is validated using a real robot dataset. The results show that the system can classify the synthetic robot signals for joints two and three with 97% accuracy and the real robot signals for joints five and six with 100% accuracy. This system aims to be a high-speed reconfigurable tool to help detect robot precision degradation and implement timely maintenance strategies.

Robotic systems are a fundamental part of modern industrial development. In this regard, they are required for long periods, in repetitive processes that must comply with strict tolerance ranges. Hence, the positional accuracy of the robots is critical, since degradation of this can represent a considerable loss of resources. In recent years, prognosis and health management (PHM) methodologies, based on machine and deep learning, have been applied to robots, in order to diagnose and detect faults and identify the degradation of robot positional accuracy, using external measurement systems, such as lasers and cameras; however, their implementation is complex in industrial environments. In this respect, this paper proposes a method based on discrete wavelet transform, nonlinear indices, principal component analysis, and artificial neural networks, in order to detect a positional deviation in robot joints, by analyzing the currents of the actuators. The results show that the proposed methodology allows classification of the robot positional degradation with an accuracy of 100%, using its current signals. The early detection of robot positional degradation, allows the implementation of PHM strategies on time, and prevents losses in manufacturing processes.