Vitolo, Ferdinando (2017) Multi-Attribute Task Sequencing Optimisation with Neighbourhoods for Robotic Systems. [Tesi di dottorato]

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Tipologia del documento:	Tesi di dottorato
Lingua:	English
Titolo:	Multi-Attribute Task Sequencing Optimisation with Neighbourhoods for Robotic Systems
Autori:	Autore Email Vitolo, Ferdinando ferdinando.vitolo@unina.it
Data:	10 Aprile 2017
Numero di pagine:	84
Istituzione:	Università degli Studi di Napoli Federico II
Dipartimento:	Ingegneria Industriale
Scuola di dottorato:	Ingegneria industriale
Dottorato:	Ingegneria industriale
Ciclo di dottorato:	29
Coordinatore del Corso di dottorato:	nome email Grassi, Michele michele.grassi@unina.it
Tutor:	nome email Patalano, Stanislao [non definito]
Data:	10 Aprile 2017
Numero di pagine:	84
Parole chiave:	Task sequencing, TSPN, multi-attribute, Industrial robot
Settori scientifico-disciplinari del MIUR:	Area 09 - Ingegneria industriale e dell'informazione > ING-IND/15 - Disegno e metodi dell'ingegneria industriale
Depositato il:	23 Apr 2017 13:09
Ultima modifica:	13 Mar 2018 07:47
URI:	http://www.fedoa.unina.it/id/eprint/11509
DOI:	10.6093/UNINA/FEDOA/11509

Abstract

Modern manufacturing processes have to be continuously updated to catch up with fast-evolving requirements, as dictated my competitive and dynamic markets, which demand high product variety. Indeed, in the era of smart factories and cyber-physical production systems (CPPS) we are experiencing a fast transition from mass production to mass customisation. Key Enabling Technologies (KETs) are then necessary to hinge business and market needs on digital solutions which enable the rapid delivery of new and innovative products. If on one side mass customisation imposes high level of product variety, on the other hand customers wish to receive high quality products, which reflect the need for near-zero defects manufacturing systems. Therefore, the combination of macro-level changes (product variety) and micro-level variety (product defects) leads to the concept of self-evolving production systems, one of the KETs to enable CPPS. In this context, industrial robots play a key role to deploy automation and fast responsiveness. Currently, robots are programmed following off-line methods. Tough those methods are still a premium solution to model and simulate production systems, they suffer the capability to incorporate dynamic changes. Therefore, it is crucial to introduce the new concept of dynamic robot programming which enables real-time robot adjustments. Robot programming usually consists of four steps: (1) task planning; (2) task sequencing; (3) path planning and (4) motion planning. These steps are strictly coupled although robot trajectory is mainly affected by defined tasks. In literature, task sequencing is modelled as Travelling Salesman Problem with Neighbourhoods (TSPN). There exist several methods for solving TSPN, but no one enables the dynamic programming. This thesis aims to develop robot tasks sequencing methodology with the ultimate goal of finding the near-optimum task sequence, by minimising computational time to enable dynamic robot programming in the case of multiple and coupled tasks’ attributes. The thesis introduces two methodologies: (1) “Enhanced Heuristic with Hierarchical Clustering” (EH2C); and, (2) “Augmented-EH2C” (A-EH2C). EH2C is a general framework to solve TSPN-like problems. The method uses a novel approach which hinges on the key idea of pre-computed feasible robot poses based on analytical formulation of Euclidian weighted functions. Results and benchmarking studies have showed that this approach allows to reach a faster convergence rate, when compared to the top-1method available in the public domain. The EH2C methods has been then deployed to solve robotic task sequencing problem, with multiple attributes. This has led to the A-EH2C method, which introduces the concept of multi-attribute task sequencing, as a paradigm to solve coupled and hierarchical robotic task sequencing and path planning problems. The thesis poses the following contributions: (1) enhanced heuristic approach based on Euclidian distance to define the initial guess points for constructing tour in TSPN; (2) multi-attribute approach to find the optimised task sequencing via candidate poses solving inverse kinematics in T-space; (3) break-through paradigm shift from static robot path planning to dynamic robot path to enable on-the-fly robot re-programming to facilitate product and process adjustments. The proposed solutions have been tested in the context of automotive body assembly systems. However, results could impact a wider area, from navigation systems, game and graph theory, to autonomous systems.

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