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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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.
Original languageEnglish
Title of host publicationICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence
EditorsAna Paula Rocha, Luc Steels, Jaap van den Herik
Number of pages10
ISBN (Electronic)978-989758484-8
Publication statusPublished - 4 Feb 2021
Event13th International Conference on Agents and Artificial Intelligence: ICAART 2021 - Virtuell, Austria
Duration: 4 Feb 20216 Feb 2021


Conference13th International Conference on Agents and Artificial Intelligence
Abbreviated titleICAART 2021
Internet address


  • Evolutionary Algorithms
  • Robot Behaviors
  • Finite State Machines
  • Hidden Markov Models
  • Robot behaviors
  • Hidden markov models
  • Evolutionary algorithms
  • Finite state machines

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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