Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications
This paper deals with the problems and the solutions of fast coverage path planning (CPP) for multiple UAVs. Through this research, the problem is solved and analyzed with both a software framework and algorithm. The implemented algorithm generates a back-and-forth path based on the onboard sensor f...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/6/2297 |
| _version_ | 1797442285111083008 |
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| author | Marco Andrés Luna Mohammad Sadeq Ale Isaac Ahmed Refaat Ragab Pascual Campoy Pablo Flores Peña Martin Molina |
| author_facet | Marco Andrés Luna Mohammad Sadeq Ale Isaac Ahmed Refaat Ragab Pascual Campoy Pablo Flores Peña Martin Molina |
| author_sort | Marco Andrés Luna |
| collection | DOAJ |
| description | This paper deals with the problems and the solutions of fast coverage path planning (CPP) for multiple UAVs. Through this research, the problem is solved and analyzed with both a software framework and algorithm. The implemented algorithm generates a back-and-forth path based on the onboard sensor footprint. In addition, three methods are proposed for the individual path assignment: simple bin packing trajectory planner (SIMPLE-BINPAT); bin packing trajectory planner (BINPAT); and Powell optimized bin packing trajectory planner (POWELL-BINPAT). The three methods use heuristic algorithms, linear sum assignment, and minimization techniques to optimize the planning task. Furthermore, this approach is implemented with applicable software to be easily used by first responders such as police and firefighters. In addition, simulation and real-world experiments were performed using UAVs with RGB and thermal cameras. The results show that POWELL-BINPAT generates optimal UAV paths to complete the entire mission in minimum time. Furthermore, the computation time for the trajectory generation task decreases compared to other techniques in the literature. This research is part of a real project funded by the H2020 FASTER Project, with grant ID: 833507. |
| first_indexed | 2024-03-09T12:40:40Z |
| format | Article |
| id | doaj.art-2f7a386bd5634740855c6c5a36eb584e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:40:40Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2f7a386bd5634740855c6c5a36eb584e2023-11-30T22:18:58ZengMDPI AGSensors1424-82202022-03-01226229710.3390/s22062297Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing ApplicationsMarco Andrés Luna0Mohammad Sadeq Ale Isaac1Ahmed Refaat Ragab2Pascual Campoy3Pablo Flores Peña4Martin Molina5Computer Vision and Aerial Robotics Group, Centre for Automation and Robotics, Universidad Politécnica de Madrid, 28040 Madrid, SpainComputer Vision and Aerial Robotics Group, Centre for Automation and Robotics, Universidad Politécnica de Madrid, 28040 Madrid, SpainDrone-Hopper Company, 28919 Leganés, SpainComputer Vision and Aerial Robotics Group, Centre for Automation and Robotics, Universidad Politécnica de Madrid, 28040 Madrid, SpainDrone-Hopper Company, 28919 Leganés, SpainDepartment of Artificial Intelligence, Universidad Politécnica de Madrid, 28040 Madrid, SpainThis paper deals with the problems and the solutions of fast coverage path planning (CPP) for multiple UAVs. Through this research, the problem is solved and analyzed with both a software framework and algorithm. The implemented algorithm generates a back-and-forth path based on the onboard sensor footprint. In addition, three methods are proposed for the individual path assignment: simple bin packing trajectory planner (SIMPLE-BINPAT); bin packing trajectory planner (BINPAT); and Powell optimized bin packing trajectory planner (POWELL-BINPAT). The three methods use heuristic algorithms, linear sum assignment, and minimization techniques to optimize the planning task. Furthermore, this approach is implemented with applicable software to be easily used by first responders such as police and firefighters. In addition, simulation and real-world experiments were performed using UAVs with RGB and thermal cameras. The results show that POWELL-BINPAT generates optimal UAV paths to complete the entire mission in minimum time. Furthermore, the computation time for the trajectory generation task decreases compared to other techniques in the literature. This research is part of a real project funded by the H2020 FASTER Project, with grant ID: 833507.https://www.mdpi.com/1424-8220/22/6/2297multi-UAVcoverage path planningaerial sensingbin-packing problem |
| spellingShingle | Marco Andrés Luna Mohammad Sadeq Ale Isaac Ahmed Refaat Ragab Pascual Campoy Pablo Flores Peña Martin Molina Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications Sensors multi-UAV coverage path planning aerial sensing bin-packing problem |
| title | Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications |
| title_full | Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications |
| title_fullStr | Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications |
| title_full_unstemmed | Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications |
| title_short | Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications |
| title_sort | fast multi uav path planning for optimal area coverage in aerial sensing applications |
| topic | multi-UAV coverage path planning aerial sensing bin-packing problem |
| url | https://www.mdpi.com/1424-8220/22/6/2297 |
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