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Proceedings Paper

AV control system which makes use of environment stabilizations
Author(s): Georgi Stojanov; Stevo Bozinovski; Liljana Bozinovska
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Paper Abstract

In the present paper we propose a new, biologically inspired conceptual scheme for introducing parallelism, redundancy and learning in control systems for autonomous vehicles (AV). AV are regarded as a special class of autonomous agents (AA). Most generally stated, an AA is defined with the set of its percepts (S), the set of its elementary actions (A), and its internal structure. Within our scheme expectation is the key concept, and an agent is said to be aware of its environment if it can anticipate the effects of the actions (A) it applies in particular situations (S). If the environment stabilizes for awhile, the effects of application of a particular action will remain same for particular situation. We can take advantage of this fact and avoid recomputation of the optical action in that situation by applying instead, the action generated by the stored anticipations for that situation. In the introduction we give a brief overview of the present AA architectures in the domain of autonomous vehicles and outline the basics of our architecture. In the next section the expecting agent is described in more details. The last section is devoted to an example of implementation of this scheme within the domain of obstacle avoiding path finding autonomous vehicles control. The implementation details and the simulation results of three experiments are discussed.

Paper Details

Date Published: 23 January 1997
PDF: 8 pages
Proc. SPIE 2903, Mobile Robots XI and Automated Vehicle Control Systems, (23 January 1997); doi: 10.1117/12.265347
Show Author Affiliations
Georgi Stojanov, SS Cyril and Methodius Univ. (Republic of Macedonia)
Stevo Bozinovski, SS Cyril and Methodius Univ. (Republic of Macedonia)
Liljana Bozinovska, SS Cyril and Methodius Univ. (Republic of Macedonia)

Published in SPIE Proceedings Vol. 2903:
Mobile Robots XI and Automated Vehicle Control Systems
Chase H. Kenyon; Pushkin Kachroo, Editor(s)

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