Automatic design of vision-based obstacle avoidance controllers using genetic programming

Renaud Barate; Antoine Manzanera

doi:10.1007/978-3-540-79305-2_3

Automatic design of vision-based obstacle avoidance controllers using genetic programming

Renaud Barate
, Antoine Manzanera

ENSTA ParisTech

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The work presented in this paper is part of the development of a robotic system able to learn context dependent visual clues to navigate in its environment. We focus on the obstacle avoidance problem as it is a necessary function for a mobile robot. As a first step, we use an off-line procedure to automatically design algorithms adapted to the visual context. This procedure is based on genetic programming and the candidate algorithms are evaluated in a simulation environment. The evolutionary process selects meaningful visual primitives in the given context and an adapted strategy to use them. The results show the emergence of several different behaviors outperforming hand-designed controllers.

Original language	English
Title of host publication	Artificial Evolution - 8th International Conference Evolution Artificielle, EA 2007, Revised Selected Papers
Pages	25-36
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-540-79305-2_3
Publication status	Published - 9 Jun 2008
Externally published	Yes
Event	8th International Conference on Artificial Evolution, EA 2007 - Tours, France Duration: 29 Oct 2007 → 31 Oct 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4926 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Conference on Artificial Evolution, EA 2007
Country/Territory	France
City	Tours
Period	29/10/07 → 31/10/07

Access to Document

10.1007/978-3-540-79305-2_3

Cite this

Barate, R., & Manzanera, A. (2008). Automatic design of vision-based obstacle avoidance controllers using genetic programming. In Artificial Evolution - 8th International Conference Evolution Artificielle, EA 2007, Revised Selected Papers (pp. 25-36). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4926 LNCS). https://doi.org/10.1007/978-3-540-79305-2_3

Barate, Renaud ; Manzanera, Antoine. / Automatic design of vision-based obstacle avoidance controllers using genetic programming. Artificial Evolution - 8th International Conference Evolution Artificielle, EA 2007, Revised Selected Papers. 2008. pp. 25-36 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Automatic design of vision-based obstacle avoidance controllers using genetic programming",

abstract = "The work presented in this paper is part of the development of a robotic system able to learn context dependent visual clues to navigate in its environment. We focus on the obstacle avoidance problem as it is a necessary function for a mobile robot. As a first step, we use an off-line procedure to automatically design algorithms adapted to the visual context. This procedure is based on genetic programming and the candidate algorithms are evaluated in a simulation environment. The evolutionary process selects meaningful visual primitives in the given context and an adapted strategy to use them. The results show the emergence of several different behaviors outperforming hand-designed controllers.",

author = "Renaud Barate and Antoine Manzanera",

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Barate, R & Manzanera, A 2008, Automatic design of vision-based obstacle avoidance controllers using genetic programming. in Artificial Evolution - 8th International Conference Evolution Artificielle, EA 2007, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4926 LNCS, pp. 25-36, 8th International Conference on Artificial Evolution, EA 2007, Tours, France, 29/10/07. https://doi.org/10.1007/978-3-540-79305-2_3

Automatic design of vision-based obstacle avoidance controllers using genetic programming. / Barate, Renaud; Manzanera, Antoine.
Artificial Evolution - 8th International Conference Evolution Artificielle, EA 2007, Revised Selected Papers. 2008. p. 25-36 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4926 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - The work presented in this paper is part of the development of a robotic system able to learn context dependent visual clues to navigate in its environment. We focus on the obstacle avoidance problem as it is a necessary function for a mobile robot. As a first step, we use an off-line procedure to automatically design algorithms adapted to the visual context. This procedure is based on genetic programming and the candidate algorithms are evaluated in a simulation environment. The evolutionary process selects meaningful visual primitives in the given context and an adapted strategy to use them. The results show the emergence of several different behaviors outperforming hand-designed controllers.

AB - The work presented in this paper is part of the development of a robotic system able to learn context dependent visual clues to navigate in its environment. We focus on the obstacle avoidance problem as it is a necessary function for a mobile robot. As a first step, we use an off-line procedure to automatically design algorithms adapted to the visual context. This procedure is based on genetic programming and the candidate algorithms are evaluated in a simulation environment. The evolutionary process selects meaningful visual primitives in the given context and an adapted strategy to use them. The results show the emergence of several different behaviors outperforming hand-designed controllers.

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Barate R, Manzanera A. Automatic design of vision-based obstacle avoidance controllers using genetic programming. In Artificial Evolution - 8th International Conference Evolution Artificielle, EA 2007, Revised Selected Papers. 2008. p. 25-36. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-79305-2_3

Automatic design of vision-based obstacle avoidance controllers using genetic programming

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