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- Title
Multi-UAV Mapping and Target Finding in Large, Complex, Partially Observable Environments.
- Authors
Walker, Violet; Vanegas, Fernando; Gonzalez, Felipe
- Abstract
Coordinating multiple unmanned aerial vehicles (UAVs) for the purposes of target finding or surveying points of interest in large, complex, and partially observable environments remains an area of exploration. This work proposes a modeling approach and software framework for multi-UAV search and target finding within large, complex, and partially observable environments. Mapping and path-solving is carried out by an extended NanoMap library; the global planning problem is defined as a decentralized partially observable Markov decision process and solved using an online model-based solver, and the local control problem is defined as two separate partially observable Markov decision processes that are solved using deep reinforcement learning. Simulated testing demonstrates that the proposed framework enables multiple UAVs to search and target-find within large, complex, and partially observable environments.
- Subjects
PARTIALLY observable Markov decision processes; DEEP reinforcement learning; REINFORCEMENT learning; SOFTWARE frameworks; DRONE aircraft
- Publication
Remote Sensing, 2023, Vol 15, Issue 15, p3802
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs15153802