Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams
<p dir="ltr">This work is about mission planning in teams of mobile autonomous agents. We consider tasks that are spatially distributed, non-atomic, and provide an utility for integral and also partial task completion. Agents are heterogeneous, therefore showing different efficiency...
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| مؤلفون آخرون: | , |
| منشور في: |
2021
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| _version_ | 1864513522176622592 |
|---|---|
| author | Eduardo Feo-Flushing (23276023) |
| author2 | Luca Maria Gambardella (23276452) Gianni A. Di Caro (23276455) |
| author2_role | author author |
| author_facet | Eduardo Feo-Flushing (23276023) Luca Maria Gambardella (23276452) Gianni A. Di Caro (23276455) |
| author_role | author |
| dc.creator.none.fl_str_mv | Eduardo Feo-Flushing (23276023) Luca Maria Gambardella (23276452) Gianni A. Di Caro (23276455) |
| dc.date.none.fl_str_mv | 2021-05-12T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3076919 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Spatially-Distributed_Missions_With_Heterogeneous_Multi-Robot_Teams/31445008 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Distributed computing and systems software Multi-robot systems mobile robots cooperative systems planning decision support systems optimization methods genetic algorithms mathematical programming Task analysis Resource management Routing Scalability Computational modeling Optimization Vehicle routing |
| dc.title.none.fl_str_mv | Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">This work is about mission planning in teams of mobile autonomous agents. We consider tasks that are spatially distributed, non-atomic, and provide an utility for integral and also partial task completion. Agents are heterogeneous, therefore showing different efficiency when dealing with the tasks. The goal is to define a system-level plan that assigns tasks to agents to maximize mission performance. We define the mission planning problem through a model including multiple sub-problems that are addressed jointly: task selection and allocation, task scheduling, task routing, control of agent proximity over time. The problem is proven to be NP-hard and is formalized as a mixed integer linear program (MILP). Two solution approaches are proposed: one heuristic and one exact method. Both combine a generic MILP solver and a genetic algorithm, resulting in efficient anytime algorithms. To support performance scalability and to allow the effective use of the model when online continual replanning is required, a decentralized and fully distributed architecture is defined top-down from the MILP model. Decentralization drastically reduces computational requirements and shows good scalability at the expenses of only moderate losses in performance. Lastly, we illustrate the application of the mission planning framework in two demonstrators. These implementations show how the framework can be successfully integrated with different platforms, including mobile robots (ground and aerial), wearable computers, and smart-phone devices.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3076919" target="_blank">https://dx.doi.org/10.1109/access.2021.3076919</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_f63cf602c921adbc679a9874794d054a |
| identifier_str_mv | 10.1109/access.2021.3076919 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/31445008 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Spatially-Distributed Missions With Heterogeneous Multi-Robot TeamsEduardo Feo-Flushing (23276023)Luca Maria Gambardella (23276452)Gianni A. Di Caro (23276455)EngineeringControl engineering, mechatronics and roboticsInformation and computing sciencesArtificial intelligenceDistributed computing and systems softwareMulti-robot systemsmobile robotscooperative systemsplanningdecision support systemsoptimization methodsgenetic algorithmsmathematical programmingTask analysisResource managementRoutingScalabilityComputational modelingOptimizationVehicle routing<p dir="ltr">This work is about mission planning in teams of mobile autonomous agents. We consider tasks that are spatially distributed, non-atomic, and provide an utility for integral and also partial task completion. Agents are heterogeneous, therefore showing different efficiency when dealing with the tasks. The goal is to define a system-level plan that assigns tasks to agents to maximize mission performance. We define the mission planning problem through a model including multiple sub-problems that are addressed jointly: task selection and allocation, task scheduling, task routing, control of agent proximity over time. The problem is proven to be NP-hard and is formalized as a mixed integer linear program (MILP). Two solution approaches are proposed: one heuristic and one exact method. Both combine a generic MILP solver and a genetic algorithm, resulting in efficient anytime algorithms. To support performance scalability and to allow the effective use of the model when online continual replanning is required, a decentralized and fully distributed architecture is defined top-down from the MILP model. Decentralization drastically reduces computational requirements and shows good scalability at the expenses of only moderate losses in performance. Lastly, we illustrate the application of the mission planning framework in two demonstrators. These implementations show how the framework can be successfully integrated with different platforms, including mobile robots (ground and aerial), wearable computers, and smart-phone devices.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3076919" target="_blank">https://dx.doi.org/10.1109/access.2021.3076919</a></p>2021-05-12T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3076919https://figshare.com/articles/journal_contribution/Spatially-Distributed_Missions_With_Heterogeneous_Multi-Robot_Teams/31445008CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/314450082021-05-12T12:00:00Z |
| spellingShingle | Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams Eduardo Feo-Flushing (23276023) Engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Distributed computing and systems software Multi-robot systems mobile robots cooperative systems planning decision support systems optimization methods genetic algorithms mathematical programming Task analysis Resource management Routing Scalability Computational modeling Optimization Vehicle routing |
| status_str | publishedVersion |
| title | Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams |
| title_full | Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams |
| title_fullStr | Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams |
| title_full_unstemmed | Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams |
| title_short | Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams |
| title_sort | Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams |
| topic | Engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Distributed computing and systems software Multi-robot systems mobile robots cooperative systems planning decision support systems optimization methods genetic algorithms mathematical programming Task analysis Resource management Routing Scalability Computational modeling Optimization Vehicle routing |