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.

翻译：本文提出了一种适用于异构无人机群系统进行协同空中巡检的经济型策略。与以往群体巡检工作不同，该方法不依赖精确的环境先验知识，并能实现对任意形状物体的完整三维表面覆盖。研究中，智能体被划分为多个团队，每架无人机承担包括建图、探索及巡检在内的不同任务。任务分配通过为各团队分配最优巡检体积并结合最佳优先规则实现。采用基于体素地图的环境表示方法进行路径搜索，而基于规则的路径规划方法构成该方案的核心。在全部具有挑战性的实验中，所提方法均取得了最佳性能，在多个评估轮次中超越了所有同类基准方法。该方案已开源（https://github.com/ntu-aris/caric_baseline），并作为第62届IEEE决策与控制会议（2023年）协同空中机器人巡检挑战赛的基准方法。