Pregled bibliografske jedinice broj: 1113610
Intelligent Algorithms for Non-parametric Robot Calibration:
Intelligent Algorithms for Non-parametric Robot Calibration: // Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems / Péter, Galambos ; Kurosh, Madani (ur.).
Budimpešta: SCITEPRESS, 2020. str. 51-58 doi:10.5220/0010176900510058 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), ostalo)
CROSBI ID: 1113610 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Intelligent Algorithms for Non-parametric Robot
Calibration:
Autori
Turković, Marija ; Švaco, Marko ; Jerbić, Bojan
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), ostalo
Izvornik
Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems
/ Péter, Galambos ; Kurosh, Madani - Budimpešta : SCITEPRESS, 2020, 51-58
ISBN
978-989-758-479-4
Skup
International Conference on Robotics, Computer Vision and Intelligent Systems
Mjesto i datum
Budimpešta, Mađarska, 04.11.2020. - 06.11.2020
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Non-parametric Robot Calibration ; Neural Networks ; Genetic Algorithms ; Robot Precision
Sažetak
In this paper, a novel method for non-parametric robot calibration which uses intelligent algorithms is proposed. The non-parametric calibration should prove very useful, because it does not need to identify the geometric parameters of the robot as is the case in parametric calibration. Instead, only the position measurements need to be provided. This could potentially lead to a cheaper and faster calibration process which could simplify its application on different and unique robot geometries. The biggest issue of using neural networks is that they require a lot of data, while for the process of robot calibration a very limited number of measurements is usually collected. In this experiment, the improvement of the hyperparameters of the neural network was attempted by using the genetic algorithms. Simulations also showed that the parametric optimization converges faster and that feed-forward back- propagating neural networks could not correctly simulate the behaviour of complex robots, or problems which used small datasets. However, for simple robot geometries and massive datasets, the neural network successfully simulated the behaviour of the robot. Although the number of measurements needed was well beyond the scope for real world applications, a few possible improvements were suggested for future research.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Strojarstvo
POVEZANOST RADA
Projekti:
EK-EFRR-KK.01.1.1.02.0008 - Regionalni centar izvrsnosti za robotske tehnologije (CRTA) (Jerbić, Bojan, EK - KK.01.1.1.02) ( CroRIS)
Ustanove:
Fakultet strojarstva i brodogradnje, Zagreb