Publicação
Evolving Self-Assembly in Autonomous Homogeneous Robots: Experiments with Two Physical Robots
| Resumo: | This research work illustrates an approach to the design of controllers for self-assembling robots in which the self-assembly is initiated and regulated by perceptual cues that are brought forth by the physical robots through their dynamical interactions. More specifically, we present a homogeneous control system that can achieve assembly between two modules (two fully autonomous robots) of a mobile self-reconfigurable system without a priori introduced behavioral or morphological heterogeneities. The controllers are dynamic neural networks evolved in simulation that directly control all the actuators of the two robots. The neurocontrollers cause the dynamic specialization of the robots by allocating roles between them based solely on their interaction. We show that the best evolved controller proves to be successful when tested on a real hardware platform, the swarm-bot. The performance achieved is similar to the one achieved by existing modular or behavior-based approaches, also due to the effect of an emergent recovery mechanism that was neither explicitly rewarded by the fitness function, nor observed during the evolutionary simulation. Our results suggest that direct access to the orientations or intentions of the other agents is not a necessary condition for robot coordination: Our robots coordinate without direct or explicit communication, contrary to what is assumed by most research works in collective robotics. This work also contributes to strengthening the evidence that evolutionary robotics is a design methodology that can tackle real-world tasks demanding fine sensory-motor coordination. |
|---|---|
| Autores principais: | Ampatzis, Christos |
| Outros Autores: | Tuci, Elio; Trianni, Vito; Christensen, Anders Lyhne; Dorigo, Marco |
| Assunto: | Self-assembly Role allocation Neural network Artificial evolution Evolutionary robotics |
| Ano: | 2009 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | ISCTE |
| Idioma: | inglês |
| Origem: | Repositório ISCTE |
| _version_ | 1868443548427223040 |
|---|---|
| author | Ampatzis, Christos |
| author2 | Tuci, Elio Trianni, Vito Christensen, Anders Lyhne Dorigo, Marco |
| author2_role | author author author author |
| author_facet | Ampatzis, Christos Tuci, Elio Trianni, Vito Christensen, Anders Lyhne Dorigo, Marco |
| author_role | author |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Ampatzis, Christos\"},{\"Person.name\":\"Tuci, Elio\"},{\"Person.name\":\"Trianni, Vito\"},{\"Person.name\":\"Christensen, Anders Lyhne\"},{\"Person.name\":\"Dorigo, Marco\"}] |
| datacite.creators.creator.creatorName.fl_str_mv | Ampatzis, Christos Tuci, Elio Trianni, Vito Christensen, Anders Lyhne Dorigo, Marco |
| datacite.date.Accepted.fl_str_mv | 2009-07-24T00:00:00Z |
| datacite.date.available.fl_str_mv | 2013-08-12T15:06:57Z |
| datacite.date.embargoed.fl_str_mv | 2013-08-12T15:06:57Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Self-assembly Role allocation Neural network Artificial evolution Evolutionary robotics |
| datacite.titles.title.fl_str_mv | Evolving Self-Assembly in Autonomous Homogeneous Robots: Experiments with Two Physical Robots |
| dc.creator.none.fl_str_mv | Ampatzis, Christos Tuci, Elio Trianni, Vito Christensen, Anders Lyhne Dorigo, Marco |
| dc.date.Accepted.fl_str_mv | 2009-07-24T00:00:00Z |
| dc.date.available.fl_str_mv | 2013-08-12T15:06:57Z |
| dc.date.embargoed.fl_str_mv | 2013-08-12T15:06:57Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10071/5439 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Massachusetts Institute of Technology |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Self-assembly Role allocation Neural network Artificial evolution Evolutionary robotics |
| dc.title.fl_str_mv | Evolving Self-Assembly in Autonomous Homogeneous Robots: Experiments with Two Physical Robots |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | This research work illustrates an approach to the design of controllers for self-assembling robots in which the self-assembly is initiated and regulated by perceptual cues that are brought forth by the physical robots through their dynamical interactions. More specifically, we present a homogeneous control system that can achieve assembly between two modules (two fully autonomous robots) of a mobile self-reconfigurable system without a priori introduced behavioral or morphological heterogeneities. The controllers are dynamic neural networks evolved in simulation that directly control all the actuators of the two robots. The neurocontrollers cause the dynamic specialization of the robots by allocating roles between them based solely on their interaction. We show that the best evolved controller proves to be successful when tested on a real hardware platform, the swarm-bot. The performance achieved is similar to the one achieved by existing modular or behavior-based approaches, also due to the effect of an emergent recovery mechanism that was neither explicitly rewarded by the fitness function, nor observed during the evolutionary simulation. Our results suggest that direct access to the orientations or intentions of the other agents is not a necessary condition for robot coordination: Our robots coordinate without direct or explicit communication, contrary to what is assumed by most research works in collective robotics. This work also contributes to strengthening the evidence that evolutionary robotics is a design methodology that can tackle real-world tasks demanding fine sensory-motor coordination. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| id | iscte_4a2be1dfd504776fbac6ab4e8953732a |
| identifier.url.fl_str_mv | http://hdl.handle.net/10071/5439 |
| instacron_str | iscte |
| institution | ISCTE |
| instname_str | ISCTE |
| language | eng |
| network_acronym_str | iscte |
| network_name_str | Repositório ISCTE |
| oai_identifier_str | oai:repositorio.iscte-iul.pt:10071/5439 |
| organization_str_mv | urn:organizationAcronym:iscte |
| person_str_mv | Ampatzis, Christos Tuci, Elio Trianni, Vito Christensen, Anders Lyhne Dorigo, Marco |
| publishDate | 2009 |
| publisher.none.fl_str_mv | Massachusetts Institute of Technology |
| reponame_str | Repositório ISCTE |
| repository_id_str | urn:repositoryAcronym:iscte |
| service_str_mv | urn:repositoryAcronym:iscte |
| spelling | porThis research work illustrates an approach to the design of controllers for self-assembling robots in which the self-assembly is initiated and regulated by perceptual cues that are brought forth by the physical robots through their dynamical interactions. More specifically, we present a homogeneous control system that can achieve assembly between two modules (two fully autonomous robots) of a mobile self-reconfigurable system without a priori introduced behavioral or morphological heterogeneities. The controllers are dynamic neural networks evolved in simulation that directly control all the actuators of the two robots. The neurocontrollers cause the dynamic specialization of the robots by allocating roles between them based solely on their interaction. We show that the best evolved controller proves to be successful when tested on a real hardware platform, the swarm-bot. The performance achieved is similar to the one achieved by existing modular or behavior-based approaches, also due to the effect of an emergent recovery mechanism that was neither explicitly rewarded by the fitness function, nor observed during the evolutionary simulation. Our results suggest that direct access to the orientations or intentions of the other agents is not a necessary condition for robot coordination: Our robots coordinate without direct or explicit communication, contrary to what is assumed by most research works in collective robotics. This work also contributes to strengthening the evidence that evolutionary robotics is a design methodology that can tackle real-world tasks demanding fine sensory-motor coordination.application/pdfengMassachusetts Institute of TechnologyporEvolving Self-Assembly in Autonomous Homogeneous Robots: Experiments with Two Physical RobotsAmpatzis, ChristosTuci, ElioTrianni, VitoChristensen, Anders LyhneDorigo, MarcoHandlehttp://hdl.handle.net/10071/5439ISSNIsPartOf1064-54622013-08-12T15:06:57Z2009-07-24T00:00:00Z2009-07-24http://purl.org/coar/access_right/c_abf2open accessporSelf-assemblyporRole allocationporNeural networkporArtificial evolutionporEvolutionary robotics364445 byteshttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorio.iscte-iul.pt/bitstreams/6627bb99-31d0-4ee1-9504-ebbe1fe991ce/downloadliteraturehttp://purl.org/coar/resource_type/c_6501journal article |
| spellingShingle | Evolving Self-Assembly in Autonomous Homogeneous Robots: Experiments with Two Physical Robots Ampatzis, Christos Self-assembly Role allocation Neural network Artificial evolution Evolutionary robotics |
| status | SINGLETON |
| subject.fl_str_mv | Self-assembly Role allocation Neural network Artificial evolution Evolutionary robotics |
| title | Evolving Self-Assembly in Autonomous Homogeneous Robots: Experiments with Two Physical Robots |
| title_full | Evolving Self-Assembly in Autonomous Homogeneous Robots: Experiments with Two Physical Robots |
| title_fullStr | Evolving Self-Assembly in Autonomous Homogeneous Robots: Experiments with Two Physical Robots |
| title_full_unstemmed | Evolving Self-Assembly in Autonomous Homogeneous Robots: Experiments with Two Physical Robots |
| title_short | Evolving Self-Assembly in Autonomous Homogeneous Robots: Experiments with Two Physical Robots |
| title_sort | Evolving Self-Assembly in Autonomous Homogeneous Robots: Experiments with Two Physical Robots |
| topic | Self-assembly Role allocation Neural network Artificial evolution Evolutionary robotics |
| topic_facet | Self-assembly Role allocation Neural network Artificial evolution Evolutionary robotics |
| url | http://hdl.handle.net/10071/5439 |
| visible | 1 |