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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-03, Vol.22 (6), p.2297
Main Authors: Luna, Marco Andrés, Ale Isaac, Mohammad Sadeq, Ragab, Ahmed Refaat, Campoy, Pascual, Flores Peña, Pablo, Molina, Martin
Format: Article
Language:English
Subjects:
aerial sensing
Algorithms
Altitude
bin-packing problem
Computer Simulation
coverage path planning
Decomposition
Emergency response
Evacuations & rescues
Linear programming
Methods
multi-UAV
Optimization
Police
R&D
Research & development
Sensors
Software
Trajectory planning
Unmanned aerial vehicles
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22062297