Exploiting Artificial Swarms for the Virtual Measurement of Backlash in Industrial Robots

Abstract

The backlash is a lost motion in a mechanism created by gaps between its parts. It causes vibrations that increase over time and negatively affect accuracy and performance. The quickest and most precise way to measure the backlash is to use specific sensors, that have to be added to the standard equipment of the robot. However, this solution is little used in practice because raises the manufacturing costs. An alternative solution can be to exploit a virtual sensor, i.e., the information about phenomena that are not directly measured is reconstructed by signals from sensors used for other measurements.This work evaluates the use of bio-inspired swarm algorithms as the processing core of a virtual sensor for the backlash of a robotic joint. Swarm-based approaches, with their relatively modest occupation of memory and low computational load, could be ideal candidates to solve the problem. In this paper, we exploit four state-of-the-art swarm-based optimization algorithms: the Dragonfly Algorithm, the Ant Lion Optimizer, the Grasshopper Optimization Algorithm, and the Grey Wolf Optimizer. The four candidate algorithms are compared on 20 different datasets covering a range of backlash values that reflect an industrial case scenario. Numerical results indicate that, unfortunately, none of the algorithms considered provides satisfactory solutions for the problem analyzed. Therefore, even if promising, these algorithms cannot represent the final choice for the problem of interest.

A virtual sensor for backlash in robotic manipulators

“…The benchmarks used for the comparison were: the ease of implementation, and quality of the solution in terms of accuracy and precision. The results reported by Giovannitti et al (2021) showed that, even if promising in terms of ease of implementation and memory occupation, none of the swarm algorithms considered for the test proved suitable for the problem of interest. The best performance was delivered by the evolutionary algorithm Covariance Matrix Adaptation Evolution Strategy (CMA-ES).…”

“…Finally, to provide an estimate of the backlash level in the joint, a meta-heuristic algorithm was used to scan the encoder signal and fit the signature to the data. Giovannitti et al (2019) presented a feasibility study of the approach on a few simulated case studies in 2019, then a comparison of the performance of different meta-heuristic algorithms for backlash detection was published in 2021 (Giovannitti et al, 2021). In the present work, the analysis is extended considering new simulated data affected by noise and considering real-world data from robotic manipulators operating in a manufacturing plant.…”