Bio-inspired navigation and exploration system for a hexapod robotic platform

This paper presents a biologically inspired system for guiding and controlling a virtual hexapod robot. Our navigation and exploration system is composed of subsystems that execute processes of path integration, action selection, actuator control and correction of the robot’s orientation. For the su...

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Autores:
Pardo Cabrera, Josh
Rivero Ortega, Jesús David
Hurtado López, Julián
Ramírez Moreno, David Fernando
Tipo de recurso:
Article of journal
Fecha de publicación:
2022
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
eng
OAI Identifier:
oai:red.uao.edu.co:10614/14721
Acceso en línea:
https://hdl.handle.net/10614/14721
https://red.uao.edu.co/
Palabra clave:
Neural networks
Path integration
Central pattern generator
Orientation correction
Vector summation
Bio-inspired robotics
Rights
openAccess
License
Derechos reservados - IOP Publishing, 2022
id REPOUAO2_3c2b7b690a28bf0c27bf86c02c044c3e
oai_identifier_str oai:red.uao.edu.co:10614/14721
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dc.title.eng.fl_str_mv Bio-inspired navigation and exploration system for a hexapod robotic platform
title Bio-inspired navigation and exploration system for a hexapod robotic platform
spellingShingle Bio-inspired navigation and exploration system for a hexapod robotic platform
Neural networks
Path integration
Central pattern generator
Orientation correction
Vector summation
Bio-inspired robotics
title_short Bio-inspired navigation and exploration system for a hexapod robotic platform
title_full Bio-inspired navigation and exploration system for a hexapod robotic platform
title_fullStr Bio-inspired navigation and exploration system for a hexapod robotic platform
title_full_unstemmed Bio-inspired navigation and exploration system for a hexapod robotic platform
title_sort Bio-inspired navigation and exploration system for a hexapod robotic platform
dc.creator.fl_str_mv Pardo Cabrera, Josh
Rivero Ortega, Jesús David
Hurtado López, Julián
Ramírez Moreno, David Fernando
dc.contributor.author.none.fl_str_mv Pardo Cabrera, Josh
Rivero Ortega, Jesús David
Hurtado López, Julián
Ramírez Moreno, David Fernando
dc.subject.proposal.eng.fl_str_mv Neural networks
Path integration
Central pattern generator
Orientation correction
Vector summation
Bio-inspired robotics
topic Neural networks
Path integration
Central pattern generator
Orientation correction
Vector summation
Bio-inspired robotics
description This paper presents a biologically inspired system for guiding and controlling a virtual hexapod robot. Our navigation and exploration system is composed of subsystems that execute processes of path integration, action selection, actuator control and correction of the robot’s orientation. For the subsystem that serves the path integration function we modified an existing model of bio-inspired vector summation by adding the capability of performing online calculation. For the action selection subsystem that allows to switch between the behaviors of exploration, approaching a target and homing we modified an existing model of decision making for mediating social behaviors in mice. We added an additional circuit that projects a signal to the units representing each of the behaviors. In the case of the actuator control subsystem, the structure of a central pattern generator model that incorporates feedback and adaptation was used as the base for generating and transforming signals for the actuators. Finally, the orientation correction subsystem is a novel model that determines an error value from a desired and the current orientations. The proposed models were simulated as independent scripts and then implemented as ROS (Robot Operating System) nodes for controlling a robot simulation in Gazebo
publishDate 2022
dc.date.issued.none.fl_str_mv 2022-05-16
dc.date.accessioned.none.fl_str_mv 2023-05-09T14:57:08Z
dc.date.available.none.fl_str_mv 2023-05-09T14:57:08Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv 26318695
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/10614/14721
dc.identifier.instname.spa.fl_str_mv Universidad Autónoma de Occidente
dc.identifier.reponame.spa.fl_str_mv Repositorio Educativo Digital UAO
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identifier_str_mv 26318695
Universidad Autónoma de Occidente
Repositorio Educativo Digital UAO
url https://hdl.handle.net/10614/14721
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dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.citationendpage.spa.fl_str_mv 18
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dc.relation.cites.spa.fl_str_mv Pardo Cabrera, J., Rivero Ortega, J.D., Hurtado López, J., Ramírez Moreno, D.F. (2022). Bio-inspired navigation and exploration system for a hexapod robotic platform. Engineering Research Express, 4(2), 1-18. https://hdl.handle.net/10614/14721
dc.relation.ispartofjournal.eng.fl_str_mv Engineering Research Express
dc.relation.references.none.fl_str_mv [1] Patek S and Summers A 2017 Invertebrate biomechanics Current Biology 27 R371–5
[2] Gao Z, Shi Q, Fukuda T, Li C and Huang Q 2019 An overview of biomimetic robots with animal behaviors Neurocomputing 332 339–50
[3] Bagheri Z M, Cazzolato B S, Grainger S, O’Carroll D C and Wiederman S D 2017 An Autonomous Robot Inspired by Insect Neurophysiology Pursues Moving Features in Natural Environments 14 046030
[4] Floreano D, Ijspeert A J and Schaal S 2014 Robotics and neuroscience Current Biology 24 R910–20
[5] Yang G-Z et al 2018 The grand challenges ofScience robotics Science Robotics 3 eaar7650
[6] Selverston A I 2010 Invertebrate central pattern generator circuits Philosophical Transactions of the Royal Society B: Biological Sciences 365 2329–45
[7] Mombaur Ket al 2017 Chapter 4—control of motion and compliance Bioinspired Legged Locomotion ed M A Sharbafi and A Seyfarth (United Kingdom: Butterworth-Heinemann) pp 135–346
[8] Ijspeert A J 2008 Central pattern generators for locomotion control in animals and robots: a review Neural Netw. 21 642–53
[9] Popovic M B, Lamkin-Kennard K A and Bowers M P 2019 5-control and physical intelligence Biomechatronics ed M B Popovic (New York, NY: Academic) pp 109–38
[10] Zhou S, Guo Z, Wong K, Zhu H, Huang Y, Hu X and Zheng Y-P 2021 Pathway-Specific Cortico-Muscular Coherence in Proximal-toDistal Compensation During Fine Motor Control of Finger Extension After Stroke 18 056034
[11] Tedeschi F and Carbone G 2014 Design issues for hexapod walking robots Robotics 3 181–206
[12] Raibert M H 1986 Legged Robots That Balance(Massachusetts, MA: Massachusetts Institute of Technology)
[13] Yongtian H, Eguren D, Azorín J M, Grossman R G, Luu T P and Contreras-Vidal J L 2018 Brain-Machine Interfaces for Controlling Lower-Limb Powered Robotic Systems 15 021004
[14] Böttcher S 2006Principles of Robot Locomotion (https://www2.cs.siu.edu/~hexmoor/classes/CS404-S09/RobotLocomotion.pdf)
[15] Sprowitz A, ajallooeian M, Tuleu A and Ijspeert A 2014 Kinematic primitives for walking and trotting gaits of a quadruped robot with compliant legs Frontiers in Computational Neuroscience 8 27
[16] Moro F L, Spröwitz A, Tuleu A, Vespignani M, Tsagarakis N G, Ijspeert A J and Caldwell D G 2013 Horse-like walking, trotting, and galloping derived from kinematic motion primitives(kMPs) and their application to walk/trot transitions in a compliant quadruped robot Biol. Cybern. 107 309–20
[17] Mysore S P and Kothari N B 2020 Mechanisms of competitive selection: a canonical neural circuit framework eLife 9 e51473
[18] Hoke K L, Hebets E A and Shizuka D 2017 Neural circuitry for target selection and action selection in animal behaviorIntegr. Comp. Biol. 57 808–19
[19] Héricé C, Khalil R, Moftah M, Boraud T, Guthrie M and Garenne A 2016 Decision making under uncertainty in a spiking neural network model of the basal ganglia Journal of Integrative Neuroscience 15 515–38
[20] Hurtado-López J, Ramirez-Moreno D F and Sejnowski T J 2017 Decision-making neural circuits mediating social behaviorsJ. Comput. Neurosci. 43 127–42
[21] Barron-Zambrano J H, Torres-Huitzil C and Girau B 2015 Perception-driven adaptive cpg-based locomotion for hexapod robots Neurocomputing 170 63–78 advances on Biological Rhythmic Pattern Generation: Experiments, Algorithms and Applications Selected Papers from the 2013 International Conference on Intelligence Science and Big Data Engineering (IScIDE 2013)Computational Energy Management in Smart Grids
[22] Kim J, Sharma G and Iyengar S S 2010 Famper: a fully autonomous mobile robot for pipeline exploration 2010 IEEE International Conference on Industrial Technology pp 517–23
[23] Murphy R R 2004 Human-robot interaction in rescue roboticsIEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 34 138–53
[24] Heinze S, Narendra A and Cheung A 2018 Principles of insect path integration Current Biology : CB 28 R1043–58
[25] Stone T, Webb B, Adden A, Weddig N B, Honkanen A, Templin R, Wcislo W, Scimeca L, Warrant E and Heinze S 2017 An anatomically constrained model for path integration in the bee brain Current Biology (https://doi.org/10.1016/j.cub.2017.08.052)
[26] Issa J B and Zhang K 2012 Universal conditions for exact path integration in neural systems Proc. Natl Acad. Sci. 109 6716–20
[27] Page H J I and Jeffery K J 2018 Landmark-based updating of the head direction system by retrosplenial cortex: a computational model Frontiers in Cellular Neuroscience 12 191
[28] Savelli F and Knierim J J 2019 Origin and role of path integration in the cognitive representations of the hippocampus: computational insights into open questionsJ. Exp. Biol. 222
[29] Zeng T, Li X and Si B 2020 Stereoneurobayesslam: a neurobiologically inspired stereo visual slam system based on direct sparse method arXiv:2003.03091
[30] Saranli U, Buehler M and Koditschek D E 2001 Rhex: a simple and highly mobile hexapod robot The International Journal of Robotics Research 20 616–31
[31] Wilson H R 1999 Spikes, Decisions and Actions (Oxford: Oxford University Press)
[32] Trappenberg T 2010 Fundamentals of Computational Neuroscience(New York, NY: Oxford University Press)
[33] Lee H, Kim D-W, Remedios R, Anthony T, Chang A, Madisen L, Zeng H and Anderson D 2014 Scalable control of mounting and attack by esr1 neurons in the ventromedial hypothalamus Nature 509
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spelling Pardo Cabrera, Josh30c867ef64a739b68d65110b9b0df9abRivero Ortega, Jesús David7b77a86ada033a49b73eaa7ab7bb80dcHurtado López, Juliánvirtual::2355-1Ramírez Moreno, David Fernandovirtual::4307-12023-05-09T14:57:08Z2023-05-09T14:57:08Z2022-05-1626318695https://hdl.handle.net/10614/14721Universidad Autónoma de OccidenteRepositorio Educativo Digital UAOhttps://red.uao.edu.co/This paper presents a biologically inspired system for guiding and controlling a virtual hexapod robot. Our navigation and exploration system is composed of subsystems that execute processes of path integration, action selection, actuator control and correction of the robot’s orientation. For the subsystem that serves the path integration function we modified an existing model of bio-inspired vector summation by adding the capability of performing online calculation. For the action selection subsystem that allows to switch between the behaviors of exploration, approaching a target and homing we modified an existing model of decision making for mediating social behaviors in mice. We added an additional circuit that projects a signal to the units representing each of the behaviors. In the case of the actuator control subsystem, the structure of a central pattern generator model that incorporates feedback and adaptation was used as the base for generating and transforming signals for the actuators. Finally, the orientation correction subsystem is a novel model that determines an error value from a desired and the current orientations. The proposed models were simulated as independent scripts and then implemented as ROS (Robot Operating System) nodes for controlling a robot simulation in Gazebo18 páginasapplication/pdfengIOP Publishing LtdDerechos reservados - IOP Publishing, 2022https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Bio-inspired navigation and exploration system for a hexapod robotic platformArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a8518214Pardo Cabrera, J., Rivero Ortega, J.D., Hurtado López, J., Ramírez Moreno, D.F. (2022). Bio-inspired navigation and exploration system for a hexapod robotic platform. Engineering Research Express, 4(2), 1-18. https://hdl.handle.net/10614/14721Engineering Research Express[1] Patek S and Summers A 2017 Invertebrate biomechanics Current Biology 27 R371–5[2] Gao Z, Shi Q, Fukuda T, Li C and Huang Q 2019 An overview of biomimetic robots with animal behaviors Neurocomputing 332 339–50[3] Bagheri Z M, Cazzolato B S, Grainger S, O’Carroll D C and Wiederman S D 2017 An Autonomous Robot Inspired by Insect Neurophysiology Pursues Moving Features in Natural Environments 14 046030[4] Floreano D, Ijspeert A J and Schaal S 2014 Robotics and neuroscience Current Biology 24 R910–20[5] Yang G-Z et al 2018 The grand challenges ofScience robotics Science Robotics 3 eaar7650[6] Selverston A I 2010 Invertebrate central pattern generator circuits Philosophical Transactions of the Royal Society B: Biological Sciences 365 2329–45[7] Mombaur Ket al 2017 Chapter 4—control of motion and compliance Bioinspired Legged Locomotion ed M A Sharbafi and A Seyfarth (United Kingdom: Butterworth-Heinemann) pp 135–346[8] Ijspeert A J 2008 Central pattern generators for locomotion control in animals and robots: a review Neural Netw. 21 642–53[9] Popovic M B, Lamkin-Kennard K A and Bowers M P 2019 5-control and physical intelligence Biomechatronics ed M B Popovic (New York, NY: Academic) pp 109–38[10] Zhou S, Guo Z, Wong K, Zhu H, Huang Y, Hu X and Zheng Y-P 2021 Pathway-Specific Cortico-Muscular Coherence in Proximal-toDistal Compensation During Fine Motor Control of Finger Extension After Stroke 18 056034[11] Tedeschi F and Carbone G 2014 Design issues for hexapod walking robots Robotics 3 181–206[12] Raibert M H 1986 Legged Robots That Balance(Massachusetts, MA: Massachusetts Institute of Technology)[13] Yongtian H, Eguren D, Azorín J M, Grossman R G, Luu T P and Contreras-Vidal J L 2018 Brain-Machine Interfaces for Controlling Lower-Limb Powered Robotic Systems 15 021004[14] Böttcher S 2006Principles of Robot Locomotion (https://www2.cs.siu.edu/~hexmoor/classes/CS404-S09/RobotLocomotion.pdf)[15] Sprowitz A, ajallooeian M, Tuleu A and Ijspeert A 2014 Kinematic primitives for walking and trotting gaits of a quadruped robot with compliant legs Frontiers in Computational Neuroscience 8 27[16] Moro F L, Spröwitz A, Tuleu A, Vespignani M, Tsagarakis N G, Ijspeert A J and Caldwell D G 2013 Horse-like walking, trotting, and galloping derived from kinematic motion primitives(kMPs) and their application to walk/trot transitions in a compliant quadruped robot Biol. Cybern. 107 309–20[17] Mysore S P and Kothari N B 2020 Mechanisms of competitive selection: a canonical neural circuit framework eLife 9 e51473[18] Hoke K L, Hebets E A and Shizuka D 2017 Neural circuitry for target selection and action selection in animal behaviorIntegr. Comp. Biol. 57 808–19[19] Héricé C, Khalil R, Moftah M, Boraud T, Guthrie M and Garenne A 2016 Decision making under uncertainty in a spiking neural network model of the basal ganglia Journal of Integrative Neuroscience 15 515–38[20] Hurtado-López J, Ramirez-Moreno D F and Sejnowski T J 2017 Decision-making neural circuits mediating social behaviorsJ. Comput. Neurosci. 43 127–42[21] Barron-Zambrano J H, Torres-Huitzil C and Girau B 2015 Perception-driven adaptive cpg-based locomotion for hexapod robots Neurocomputing 170 63–78 advances on Biological Rhythmic Pattern Generation: Experiments, Algorithms and Applications Selected Papers from the 2013 International Conference on Intelligence Science and Big Data Engineering (IScIDE 2013)Computational Energy Management in Smart Grids[22] Kim J, Sharma G and Iyengar S S 2010 Famper: a fully autonomous mobile robot for pipeline exploration 2010 IEEE International Conference on Industrial Technology pp 517–23[23] Murphy R R 2004 Human-robot interaction in rescue roboticsIEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 34 138–53[24] Heinze S, Narendra A and Cheung A 2018 Principles of insect path integration Current Biology : CB 28 R1043–58[25] Stone T, Webb B, Adden A, Weddig N B, Honkanen A, Templin R, Wcislo W, Scimeca L, Warrant E and Heinze S 2017 An anatomically constrained model for path integration in the bee brain Current Biology (https://doi.org/10.1016/j.cub.2017.08.052)[26] Issa J B and Zhang K 2012 Universal conditions for exact path integration in neural systems Proc. Natl Acad. Sci. 109 6716–20[27] Page H J I and Jeffery K J 2018 Landmark-based updating of the head direction system by retrosplenial cortex: a computational model Frontiers in Cellular Neuroscience 12 191[28] Savelli F and Knierim J J 2019 Origin and role of path integration in the cognitive representations of the hippocampus: computational insights into open questionsJ. Exp. Biol. 222[29] Zeng T, Li X and Si B 2020 Stereoneurobayesslam: a neurobiologically inspired stereo visual slam system based on direct sparse method arXiv:2003.03091[30] Saranli U, Buehler M and Koditschek D E 2001 Rhex: a simple and highly mobile hexapod robot The International Journal of Robotics Research 20 616–31[31] Wilson H R 1999 Spikes, Decisions and Actions (Oxford: Oxford University Press)[32] Trappenberg T 2010 Fundamentals of Computational Neuroscience(New York, NY: Oxford University Press)[33] Lee H, Kim D-W, Remedios R, Anthony T, Chang A, Madisen L, Zeng H and Anderson D 2014 Scalable control of mounting and attack by esr1 neurons in the ventromedial hypothalamus Nature 509Neural networksPath integrationCentral pattern generatorOrientation correctionVector summationBio-inspired roboticsComunidad generalPublication77636374-92e2-4d63-9b49-bdb0a2ea1182virtual::2355-161e20236-82c5-4dcc-b05c-0eaa9ac06b11virtual::4307-177636374-92e2-4d63-9b49-bdb0a2ea1182virtual::2355-161e20236-82c5-4dcc-b05c-0eaa9ac06b11virtual::4307-1https://scholar.google.com/citations?user=7Pqx31YAAAAJ&hl=esvirtual::2355-1https://scholar.google.com/citations?user=RTce1fkAAAAJ&hl=esvirtual::4307-10000-0002-3773-0598virtual::2355-10000-0003-2372-3554virtual::4307-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000828963virtual::2355-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000353744virtual::4307-1ORIGINALBio_inspired_navigation_and_exploration_system_for_a_hexapod.pdfBio_inspired_navigation_and_exploration_system_for_a_hexapod.pdftexto completo del artículoapplication/pdf1663679https://red.uao.edu.co/bitstreams/eb75406d-ca93-444d-8f60-707bb18038c4/download2e00136ef4d5bc7a548ab8edac7982fbMD51LICENSElicense.txtlicense.txttext/plain; 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