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dc.contributor.author
Xu, Xiaoling  
dc.contributor.author
Marelli, Damian Edgardo  
dc.contributor.author
Meng, Wei  
dc.contributor.author
Zhang, Fumin  
dc.contributor.author
Cai, Qianqian  
dc.contributor.author
Fu, Minyue  
dc.date.available
2023-09-04T17:35:17Z  
dc.date.issued
2022-10  
dc.identifier.citation
Xu, Xiaoling; Marelli, Damian Edgardo; Meng, Wei; Zhang, Fumin; Cai, Qianqian; et al.; Multi-MAV Autonomous Full Coverage Search in Cluttered Forest Environments; Springer; Journal of Intelligent & Robotic Systems; 106; 2; 10-2022; 1-20  
dc.identifier.issn
0921-0296  
dc.identifier.uri
http://hdl.handle.net/11336/210418  
dc.description.abstract
This paper is concerned with autonomous forest full coverage search using multiple micro aerial vehicles (MAVs). Due to the complex and cluttered environment, i.e., many obstacles under the forest canopy, it is quite challenging to achieve full coverage search using fully autonomous MAVs, e.g., quadrotors. In this work, we propose a two-stage multi-MAV forest search strategy. The first batch of MAVs provides a coarse search and mapping result using pre-defined or auto-generated paths. Based on that, the second batch of MAVs continues to search the multiple isolated regions missed by the first batch. The main difficulties fall in the autonomous task allocation and optimal cooperative coverage path planning for the second batch of MAVs, to achieve the full coverage goal. To address this problem, a task allocation algorithm based on the branch and bound principle is introduced to find the optimal search order of the missed regions. Furthermore, an optimal coverage path planning algorithm considering obstacle avoidance is proposed to cover each region. Simulation results show that our proposed method improves the efficiency of coverage path planning for cooperative search and guarantees full area coverage.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Springer  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
CONNECTED VEHICLES  
dc.subject
COOPERATIVE SEARCH  
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FULL COVERAGE  
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MICRO AERIAL VEHICLES  
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TASK ALLOCATION  
dc.subject.classification
Control Automático y Robótica  
dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información  
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS  
dc.title
Multi-MAV Autonomous Full Coverage Search in Cluttered Forest Environments  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2023-07-04T15:52:40Z  
dc.journal.volume
106  
dc.journal.number
2  
dc.journal.pagination
1-20  
dc.journal.pais
Alemania  
dc.description.fil
Fil: Xu, Xiaoling. Guangdong University Of Technology; China  
dc.description.fil
Fil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina  
dc.description.fil
Fil: Meng, Wei. Guangdong University Of Technology; China  
dc.description.fil
Fil: Zhang, Fumin. No especifíca;  
dc.description.fil
Fil: Cai, Qianqian. Guangdong University Of Technology; China  
dc.description.fil
Fil: Fu, Minyue. Universidad de Newcastle; Australia  
dc.journal.title
Journal of Intelligent & Robotic Systems  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s10846-022-01723-z