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

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

Institute of Software Technology (7160)

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

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.

Original language	English
Title of host publication	ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence
Editors	Ana Paula Rocha, Luc Steels, Jaap van den Herik
Publisher	SciTePress
Pages	698-707
Number of pages	10
Volume	2
ISBN (Electronic)	978-989758484-8
Publication status	Published - 4 Feb 2021
Event	13th International Conference on Agents and Artificial Intelligence: ICAART 2021 - Virtuell, Austria Duration: 4 Feb 2021 → 6 Feb 2021 http://www.icaart.org/

Conference

Conference	13th International Conference on Agents and Artificial Intelligence
Abbreviated title	ICAART 2021
Country/Territory	Austria
City	Virtuell
Period	4/02/21 → 6/02/21
Internet address	http://www.icaart.org/

Keywords

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)

Cite this

Savage, J., Munoz Gutierrez, S., Contreras, L., Matamoros, M., Negrete, M., Rivera, C., Steinbauer, G., Fuentes, O., & Okada, H. (2021). Generating Reactive Robots' Behaviors using Genetic Algorithms. In A. P. Rocha, L. Steels, & J. van den Herik (Eds.), ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence (Vol. 2, pp. 698-707). SciTePress.

Generating Reactive Robots' Behaviors using Genetic Algorithms. / Savage, Jesus; Munoz Gutierrez, Stalin; Contreras, Luis et al.
ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence. ed. / Ana Paula Rocha; Luc Steels; Jaap van den Herik. Vol. 2 SciTePress, 2021. p. 698-707.

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

Savage, J, Munoz Gutierrez, S, Contreras, L, Matamoros, M, Negrete, M, Rivera, C, Steinbauer, G, Fuentes, O & Okada, H 2021, Generating Reactive Robots' Behaviors using Genetic Algorithms. in AP Rocha, L Steels & J van den Herik (eds), ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence. vol. 2, SciTePress, pp. 698-707, 13th International Conference on Agents and Artificial Intelligence, Virtuell, Austria, 4/02/21.

@inproceedings{9dc54fe2013d4b1a8c2cec2ffa95e77b,

title = "Generating Reactive Robots' Behaviors using Genetic Algorithms",

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.",

keywords = "Evolutionary Algorithms, Robot Behaviors, Finite State Machines, Hidden Markov Models, Robot behaviors, Hidden markov models, Evolutionary algorithms, Finite state machines",

author = "Jesus Savage and {Munoz Gutierrez}, Stalin and Luis Contreras and Mauricio Matamoros and Marco Negrete and Carlos Rivera and Gerald Steinbauer and Oscar Fuentes and Hiroyuki Okada",

year = "2021",

month = feb,

day = "4",

language = "English",

volume = "2",

pages = "698--707",

editor = "Rocha, {Ana Paula} and Luc Steels and {van den Herik}, Jaap",

booktitle = "ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence",

publisher = "SciTePress",

address = "Portugal",

note = "13th International Conference on Agents and Artificial Intelligence : ICAART 2021, ICAART 2021 ; Conference date: 04-02-2021 Through 06-02-2021",

url = "http://www.icaart.org/",

}

TY - GEN

T1 - Generating Reactive Robots' Behaviors using Genetic Algorithms

AU - Savage, Jesus

AU - Munoz Gutierrez, Stalin

AU - Contreras, Luis

AU - Matamoros, Mauricio

AU - Negrete, Marco

AU - Rivera, Carlos

AU - Steinbauer, Gerald

AU - Fuentes, Oscar

AU - Okada, Hiroyuki

PY - 2021/2/4

Y1 - 2021/2/4

N2 - 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.

AB - 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.

KW - Evolutionary Algorithms

KW - Robot Behaviors

KW - Finite State Machines

KW - Hidden Markov Models

KW - Robot behaviors

KW - Hidden markov models

KW - Evolutionary algorithms

KW - Finite state machines

UR - http://www.scopus.com/inward/record.url?scp=85103836244&partnerID=8YFLogxK

M3 - Conference paper

VL - 2

SP - 698

EP - 707

BT - ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence

A2 - Rocha, Ana Paula

A2 - Steels, Luc

A2 - van den Herik, Jaap

PB - SciTePress

T2 - 13th International Conference on Agents and Artificial Intelligence

Y2 - 4 February 2021 through 6 February 2021

ER -

Generating Reactive Robots' Behaviors using Genetic Algorithms

Abstract

Conference

Keywords

ASJC Scopus subject areas

Fields of Expertise

Treatment code (Nähere Zuordnung)

Other files and links

Fingerprint

Cite this