一种面向异构空中系统的高性价比协同探索与检测策略
A Cost-Effective Cooperative Exploration and Inspection Strategy for Heterogeneous Aerial System
AI总结：
针对异构无人机集群协同检测，论文提出无需精确环境先验、结合体素地图与规则路径规划的任务分配和全覆盖方法，性能优于基准并已开源。
摘要：
本文提出了一种用于协同空中检测的高性价比异构无人机集群系统策略。与以往的集群检测工作不同，所提方法不依赖于对环境的精确先验知识，并且能够完成任意形状物体的完整三维表面覆盖。在本研究中，智能体被划分为多个团队，每架无人机被分配不同任务，包括建图、探索和检测。任务分配通过遵循最佳优先规则、为每个团队分配最优检测体来实现。环境采用基于体素地图的表示进行寻路，而基于规则的路径规划方法是该方法的核心。借助所提方法，我们在所有具有挑战性的实验中均取得最佳性能，并在多次评估试验中超越了类似任务的所有基准方法。所提方法已在 https://github.com/ntu-aris/caric_baseline 开源，并用作2023年第62届IEEE决策与控制会议“协同空中机器人检测挑战赛”的基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
