Collaborative Coverage Strategy Using Multiple UAVs-UGVs in CRN Mapping
Chemical, Radiological, and Nuclear (CRN) contamination poses a significant threat, potentially leading to mass casualties and long-term environmental repercussions. This paper presents a collaborative framework utilizing a heterogeneous coverage control approach to measure and generate an estimated...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10980321/ |
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| author | Agung Nugroho Jati Bambang Riyanto Trilaksono Egi Muhammad Idris Hidayat Widyawardana Adiprawita |
| author_facet | Agung Nugroho Jati Bambang Riyanto Trilaksono Egi Muhammad Idris Hidayat Widyawardana Adiprawita |
| author_sort | Agung Nugroho Jati |
| collection | DOAJ |
| description | Chemical, Radiological, and Nuclear (CRN) contamination poses a significant threat, potentially leading to mass casualties and long-term environmental repercussions. This paper presents a collaborative framework utilizing a heterogeneous coverage control approach to measure and generate an estimated density distribution map of a designated area. Multiple Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) are deployed strategically within partitioned regions, determined through weighted Voronoi tessellation. This method integrates both the robots’ internal parameters and environmental factors. The distinct operational domains of UAVs and UGVs facilitate region decomposition by accounting for variations in CRN dispersion, obstacle representation, and environmental conditions. The resulting cross-partitioned regions are systematically merged to enhance robot distribution efficiency. Each robot autonomously measures within its allocated region, updates contamination data, and generates a dispersion map. The proposed strategy enables an adaptive robot distribution, eliminating uncontaminated grids and improving mapping accuracy. Compared to existing methods, including homogeneous schemes, our approach reduces data variance in CRN-contaminated regions while maintaining mapping efficiency. |
| format | Article |
| id | doaj-art-a2a93d67fdb8452b8fea0673be65819a |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a2a93d67fdb8452b8fea0673be65819a2025-08-20T01:55:28ZengIEEEIEEE Access2169-35362025-01-0113856528566810.1109/ACCESS.2025.356577910980321Collaborative Coverage Strategy Using Multiple UAVs-UGVs in CRN MappingAgung Nugroho Jati0https://orcid.org/0000-0002-0869-7733Bambang Riyanto Trilaksono1https://orcid.org/0000-0002-1149-1832Egi Muhammad Idris Hidayat2Widyawardana Adiprawita3School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, West Java, IndonesiaSchool of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, West Java, IndonesiaSchool of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, West Java, IndonesiaSchool of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, West Java, IndonesiaChemical, Radiological, and Nuclear (CRN) contamination poses a significant threat, potentially leading to mass casualties and long-term environmental repercussions. This paper presents a collaborative framework utilizing a heterogeneous coverage control approach to measure and generate an estimated density distribution map of a designated area. Multiple Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) are deployed strategically within partitioned regions, determined through weighted Voronoi tessellation. This method integrates both the robots’ internal parameters and environmental factors. The distinct operational domains of UAVs and UGVs facilitate region decomposition by accounting for variations in CRN dispersion, obstacle representation, and environmental conditions. The resulting cross-partitioned regions are systematically merged to enhance robot distribution efficiency. Each robot autonomously measures within its allocated region, updates contamination data, and generates a dispersion map. The proposed strategy enables an adaptive robot distribution, eliminating uncontaminated grids and improving mapping accuracy. Compared to existing methods, including homogeneous schemes, our approach reduces data variance in CRN-contaminated regions while maintaining mapping efficiency.https://ieeexplore.ieee.org/document/10980321/Collaborative robotsCRN mappingcoverage control |
| spellingShingle | Agung Nugroho Jati Bambang Riyanto Trilaksono Egi Muhammad Idris Hidayat Widyawardana Adiprawita Collaborative Coverage Strategy Using Multiple UAVs-UGVs in CRN Mapping IEEE Access Collaborative robots CRN mapping coverage control |
| title | Collaborative Coverage Strategy Using Multiple UAVs-UGVs in CRN Mapping |
| title_full | Collaborative Coverage Strategy Using Multiple UAVs-UGVs in CRN Mapping |
| title_fullStr | Collaborative Coverage Strategy Using Multiple UAVs-UGVs in CRN Mapping |
| title_full_unstemmed | Collaborative Coverage Strategy Using Multiple UAVs-UGVs in CRN Mapping |
| title_short | Collaborative Coverage Strategy Using Multiple UAVs-UGVs in CRN Mapping |
| title_sort | collaborative coverage strategy using multiple uavs ugvs in crn mapping |
| topic | Collaborative robots CRN mapping coverage control |
| url | https://ieeexplore.ieee.org/document/10980321/ |
| work_keys_str_mv | AT agungnugrohojati collaborativecoveragestrategyusingmultipleuavsugvsincrnmapping AT bambangriyantotrilaksono collaborativecoveragestrategyusingmultipleuavsugvsincrnmapping AT egimuhammadidrishidayat collaborativecoveragestrategyusingmultipleuavsugvsincrnmapping AT widyawardanaadiprawita collaborativecoveragestrategyusingmultipleuavsugvsincrnmapping |