Generating Reactive Robots' Behaviors using Genetic Algorithms

Jesus Savage*, Stalin Munoz Gutierrez, Luis Contreras, Mauricio Matamoros, Marco Negrete, Carlos Rivera, Gerald Steinbauer, Oscar Fuentes, Hiroyuki Okada

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

Abstract

In this paper, we analize and benchmark three genetically-evolved reactive obstacle-avoidance behaviors for mobile robots. We built these behaviors with an optimization process using genetic algorithms to find the one allowing a mobile robot to best reactively avoid obstacles while moving towards its destination. We compare three approaches, the first one is a standard method based on potential fields, the second one uses on finite state machines (FSM), and the last one relies on HMM-based probabilistic finite state machines (PFSM). We trained the behaviors in simulated environments to obtain the optimized behaviors and compared them to show that the evolved FSM approach outperforms the other two techniques.
Originalspracheenglisch
TitelICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence
Redakteure/-innenAna Paula Rocha, Luc Steels, Jaap van den Herik
Herausgeber (Verlag)SciTePress
Seiten698-707
Seitenumfang10
Band2
ISBN (elektronisch)978-989758484-8
PublikationsstatusVeröffentlicht - 4 Feb 2021
Veranstaltung13th International Conference on Agents and Artificial Intelligence - Virtuell, Österreich
Dauer: 4 Feb 20216 Feb 2021
http://www.icaart.org/

Konferenz

Konferenz13th International Conference on Agents and Artificial Intelligence
KurztitelICAART 2021
LandÖsterreich
OrtVirtuell
Zeitraum4/02/216/02/21
Internetadresse

ASJC Scopus subject areas

  • !!Engineering (miscellaneous)
  • Software
  • Artificial intelligence

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)

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