  In this paper, we propose a cost-effective strategy for heterogeneous UAV
swarm systems for cooperative aerial inspection. Unlike previous swarm
inspection works, the proposed method does not rely on precise prior knowledge
of the environment and can complete full 3D surface coverage of objects in any
shape. In this work, agents are partitioned into teams, with each drone assign
a different task, including mapping, exploration, and inspection. Task
allocation is facilitated by assigning optimal inspection volumes to each team,
following best-first rules. A voxel map-based representation of the environment
is used for pathfinding, and a rule-based path-planning method is the core of
this approach. We achieved the best performance in all challenging experiments
with the proposed approach, surpassing all benchmark methods for similar tasks
across multiple evaluation trials. The proposed method is open source at
https://github.com/ntu-aris/caric_baseline and used as the baseline of the
Cooperative Aerial Robots Inspection Challenge at the 62nd IEEE Conference on
Decision and Control 2023.
