Behavioural Swarm Optimisation for Stable Slung-Load Aerial Transportation
Behavioural Swarm Optimisation for Stable Slung-Load Aerial Transportation
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DOI：
10.1109/cec53210.2023.10254023
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Jingyu Chen;John Oyekan
中科院分区：
文献类型：
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作者：
Jingyu Chen;John Oyekan
自然群体中的智能体协作能够完成单个智能体难以完成或不可能完成的任务。例如，当负载重量是每架无人机的数倍时，一群自主无人驾驶飞行器（UAV）可以协同感知在不可通过的地形上运输的庞大负载。在这项工作中，我们提出了一种分层算法架构，该架构支持搜索和覆盖各种未知的有效载荷剖面，以供后续运输。无人机在没有路径规划的情况下，通过体系结构中的综合行为形成对有效载荷的抓取。实验表明，所提出的设计能够成功地应用于各种载荷的搜索和覆盖，并通过使用crazyfly微型无人机在现实世界中得到了验证。此外，所提出的抓取编队满足静态平衡，从而减少了负载群系统在运输过程中的方向变化。
Collaboration of agents in a natural swarm enables the accomplishment of tasks that would be difficult or impossible for a single agent to complete alone. For example, a swarm of autonomous Unmanned Aerial Vehicles (UAVs) enables the collaborative sensing of bulky loads for transportation over impassable terrains when the load weighs several times more than each UAV. In this work, we propose a hierarchical algorithmic architecture that supports the search and coverage of various unknown payload profiles for subsequent transportation. The grasping formation of UAVs over the payload emerges from the synthetic behaviours in the architecture without any path planning. Experiments show that our proposed design can be successfully applied in searching and coverage of various loads and has been validated in the real world through the use of Crazyflie micro-UAVs. Furthermore, the proposed grasping formation satisfies static equilibrium thereby reducing orientation changes in the load-swarm system during transportation.