  Recent advances have improved autonomous navigation and mapping under payload
constraints, but current multi-robot inspection algorithms are unsuitable for
nano-drones due to their need for heavy sensors and high computational
resources. To address these challenges, we introduce ExploreBug, a novel hybrid
frontier range bug algorithm designed to handle limited sensing capabilities
for a swarm of nano-drones. This system includes three primary components: a
mapping subsystem, an exploration subsystem, and a navigation subsystem.
Additionally, an intra-swarm collision avoidance system is integrated to
prevent collisions between drones. We validate the efficacy of our approach
through extensive simulations and real-world exploration experiments involving
up to seven drones in simulations and three in real-world settings, across
various obstacle configurations and with a maximum navigation speed of 0.75
m/s. Our tests demonstrate that the algorithm efficiently completes exploration
tasks, even with minimal sensing, across different swarm sizes and obstacle
densities. Furthermore, our frontier allocation heuristic ensures an equal
distribution of explored areas and paths traveled by each drone in the swarm.
We publicly release the source code of the proposed system to foster further
developments in mapping and exploration using autonomous nano drones.
