We find that using drones’ observations of the mapping status within their Field of View as a state is better than using their positions in terms of coverage and scalability. We compare two different state spaces by assessing coverage of the environment within the given limited time. This research project applies Multi-Agent Q-learning to autonomous navigation for mapping and multi-objective drone swarm exploration of a disaster area in terms of tradeoffs between coverage and scalability. Multi-Agent Reinforcement Learning is a promising approach because it can adapt to dynamic environments and relax computational complexity for optimization. Given that human resources are limited post-disaster, the development of autonomous drone swarms rather than a single drone can enable a further rapid and thorough assessment. In recent years, drones have been used for supporting post-disaster damage assessment of buildings, roads, and infrastructure.
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