Concerning the primary and secondary objectives in robot task definition - the "learn from humans" principle

Potkonjak, V; Tzafestas, S; Kostic, D

dc.contributor.author	Potkonjak, V	en
dc.contributor.author	Tzafestas, S	en
dc.contributor.author	Kostic, D	en
dc.date.accessioned	2014-03-01T01:15:31Z
dc.date.available	2014-03-01T01:15:31Z
dc.date.issued	2000	en
dc.identifier.issn	0378-4754	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13562
dc.subject	robot task	en
dc.subject	optimal trajectory	en
dc.subject	biomechanics	en
dc.subject	handwriting	en
dc.subject	legibility	en
dc.subject	spray-coating	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Computer Science, Software Engineering	en
dc.subject.classification	Mathematics, Applied	en
dc.subject.other	MOVEMENTS	en
dc.subject.other	ARM	en
dc.title	Concerning the primary and secondary objectives in robot task definition - the "learn from humans" principle	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0378-4754(00)00225-1	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0378-4754(00)00225-1	en
heal.language	English	en
heal.publicationDate	2000	en
heal.abstract	This paper is concerned with the trajectory definition in robot tasks. Although very often ignored, the specification of robot motion is not the first step in the definition of a robot task. The task definition starts with the description of the final outcome, i.e. with the specification of the job to be performed. When this is done, the proper robot kinematics (motion law) is defined. This step, from the outcome of the task to the robot motion, is sometimes straightforward and dictated by the technology applied. However, even if a multiple choice of motion is possible leading to the same outcome, this possibility is usually avoided by preassuming a certain optimality criterion for the quality of work. Clearly, such an approach does not leave the possibility for some additional optimization in the sense of a secondary objective. So, some potential benefits are lost. This paper starts from the observation that successful operation of a robot does not necessarily imply the maximum quality of its outcome. It is sufficient if the quality is kept at a given (lower) level. Such a suboptimal task execution offers the possibility of some additional secondary optimization. The kinematics of the task is modified according to a secondary objective function. The quality is then treated as a constraint in the minimization of this secondary objective function. This optimization can be based on biomechanical principles. Here the principle: "learn from humans' is adopted, i.e. the kinematics modification is done so as to resemble the behavior of a human worker. The benefits of the approach of the paper are illustrated by two specific examples, namely the handwriting and spray-coating tasks. (C) 2000 IMACS. Published by Elsevier Science B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	MATHEMATICS AND COMPUTERS IN SIMULATION	en
dc.identifier.doi	10.1016/S0378-4754(00)00225-1	en
dc.identifier.isi	ISI:000165813000009	en
dc.identifier.volume	54	en
dc.identifier.issue	1-3	en
dc.identifier.spage	145	en
dc.identifier.epage	157	en