Publication
Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments
| dc.contributor.author | Castro, Gabriel G.R. | |
| dc.contributor.author | Santos, Tatiana M.B. | |
| dc.contributor.author | Andrade, Fabio A.A. | |
| dc.contributor.author | Lima, José | |
| dc.contributor.author | Haddad, Diego B. | |
| dc.contributor.author | Honório, Leonardo de M. | |
| dc.contributor.author | Pinto, Milena F. | |
| dc.date.accessioned | 2024-05-16T15:53:32Z | |
| dc.date.available | 2024-05-16T15:53:32Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots. | pt_PT |
| dc.description.sponsorship | The authors also would like to thank their home Institute, CEFET/RJ, the federal Brazilian research agencies CAPES (code 001) and CNPq, and the Rio de Janeiro research agency, FAPERJ, for supporting this work. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Castro, Gabriel de G.R.; Santos, Tatiana M.B.; Andrade, Fabio A.A.; Lima, José; Haddad, Diego B.; Honório, Leonardo de M.; Pinto, Milena F. (2024). Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments. Machines. EISSN 2075-1702. 12:3, p. 1-27 | pt_PT |
| dc.identifier.doi | 10.3390/machines12030200 | pt_PT |
| dc.identifier.eissn | 2075-1702 | |
| dc.identifier.uri | http://hdl.handle.net/10198/29778 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | MDPI | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Multi-robot | pt_PT |
| dc.subject | Coverage path planning | pt_PT |
| dc.subject | Dynamic environment | pt_PT |
| dc.title | Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 27 | pt_PT |
| oaire.citation.issue | 3 | pt_PT |
| oaire.citation.startPage | 1 | pt_PT |
| oaire.citation.title | Machines | pt_PT |
| oaire.citation.volume | 12 | pt_PT |
| person.familyName | Lima | |
| person.givenName | José | |
| person.identifier | R-000-8GD | |
| person.identifier.ciencia-id | 6016-C902-86A9 | |
| person.identifier.orcid | 0000-0001-7902-1207 | |
| person.identifier.rid | L-3370-2014 | |
| person.identifier.scopus-author-id | 55851941311 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | d88c2b2a-efc2-48ef-b1fd-1145475e0055 | |
| relation.isAuthorOfPublication.latestForDiscovery | d88c2b2a-efc2-48ef-b1fd-1145475e0055 |