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An evolutionary approach for robust adaptation of robot behavior to sensor error

Authors:

Joshua Peter Hecker,

Melanie E. MosesAuthors Info & Claims

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

Pages 1437 - 1444

https://doi.org/10.1145/2464576.2482724

Published: 06 July 2013 Publication History

Abstract

Evolutionary algorithms can adapt the behavior of individual agents to maximize the fitness of populations of agents. We use a genetic algorithm (GA) to optimize behavior in a team of simulated robots that mimic foraging ants. We introduce positional and resource detection error models into this simulation, emulating the sensor error characterized by our physical iAnt robot platform. Increased positional error and detection error both decrease resource collection rates. However, they have different effects on GA behavior. Positional error causes the GA to reduce time spent searching for local resources and to reduce the likelihood of returning to locations where resources were previously found. Detection error causes the GA to select for more thorough local searching and a higher likelihood of communicating the location of found resources to other agents via pheromones. Agents that live in a world with error and use parameters evolved specifically for those worlds perform significantly better than agents in the same error-prone world using parameters evolved for an error-free world. This work demonstrates the utility of employing evolutionary methods to adapt robot behaviors that are robust to sensor errors.

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Cited By

Jimenez Romero CYegenoglu APérez Martín ADiaz-Pier SMorrison A(2023)Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networksSwarm Intelligence10.1007/s11721-023-00231-618:1(1-29)Online publication date: 14-Dec-2023
https://doi.org/10.1007/s11721-023-00231-6
Foss FStenrud THaddow P(2021)Investigating Genetic Network Programming for Multiple Nest Foraging2021 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI50451.2021.9659926(1-7)Online publication date: 5-Dec-2021
https://doi.org/10.1109/SSCI50451.2021.9659926
Nguyen TBanerjee B(2021)Reinforcement learning as a rehearsal for swarm foragingSwarm Intelligence10.1007/s11721-021-00203-8Online publication date: 29-Sep-2021
https://doi.org/10.1007/s11721-021-00203-8
Show More Cited By

Index Terms

An evolutionary approach for robust adaptation of robot behavior to sensor error
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Robotics
      1. Robotic autonomy
    2. Sensors and actuators
2. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Intelligent agents
      2. Multi-agent systems

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cover image ACM Conferences

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

July 2013

1798 pages

ISBN:9781450319645

DOI:10.1145/2464576

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Copyright © 2013 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 06 July 2013

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GECCO '13

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GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Jimenez Romero CYegenoglu APérez Martín ADiaz-Pier SMorrison A(2023)Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networksSwarm Intelligence10.1007/s11721-023-00231-618:1(1-29)Online publication date: 14-Dec-2023
https://doi.org/10.1007/s11721-023-00231-6
Foss FStenrud THaddow P(2021)Investigating Genetic Network Programming for Multiple Nest Foraging2021 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI50451.2021.9659926(1-7)Online publication date: 5-Dec-2021
https://doi.org/10.1109/SSCI50451.2021.9659926
Nguyen TBanerjee B(2021)Reinforcement learning as a rehearsal for swarm foragingSwarm Intelligence10.1007/s11721-021-00203-8Online publication date: 29-Sep-2021
https://doi.org/10.1007/s11721-021-00203-8
Lu QHecker JMoses M(2016)The MPFA: A multiple-place foraging algorithm for biologically-inspired robot swarms2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS.2016.7759561(3815-3821)Online publication date: Oct-2016
https://doi.org/10.1109/IROS.2016.7759561
Hecker JCarmichael JMoses M(2015)Exploiting clusters for complete resource collection in biologically-inspired robot swarms2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS.2015.7353409(434-440)Online publication date: Sep-2015
https://doi.org/10.1109/IROS.2015.7353409

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