Heterogeneous teams of Unmanned Aerial Vehicles (UAVs) can enhance the exploration capabilities of aerial robots by exploiting different strengths and abilities of varying UAVs. This paper presents a novel method for exploring unknown indoor spaces with a team of UAVs of different sizes and sensory equipment. We propose a frontier-based exploration with two task allocation strategies: a greedy strategy that assigns Points of Interest (POIs) based on Euclidean distance and UAV priority and an optimization strategy that solves a minimum-cost flow problem. The proposed method utilizes the SphereMap algorithm to assess the accessibility of the POIs and generate paths that account for obstacle distances, including collision avoidance maneuvers among UAVs. The proposed approach was validated through simulation testing and real-world experiments that evaluated the method's performance on board the UAVs.

翻译：异构无人机集群能够通过利用不同无人机的优势与能力，增强空中机器人的探索性能。本文提出了一种利用不同尺寸与传感设备的无人机集群探索未知室内空间的新方法。我们提出了一种基于前沿探索的框架，并采用两种任务分配策略：一种基于欧氏距离与无人机优先级的贪婪策略来分配兴趣点，另一种通过求解最小费用流问题实现优化分配。所提方法利用SphereMap算法评估兴趣点的可达性，并生成考虑障碍物距离的路径，包括无人机间的避碰机动。通过仿真测试与真实环境实验，在无人机机载平台上对所提方法的性能进行了验证。