多无人机在未知环境随机探索，用自组织神经网络实现均匀覆盖。
Multi-UAV Uniform Sweep Coverage in Unknown Environments: A Self-organizing Nervous System (SoNS)-Based Random Exploration
- 基于自组织神经网络的集群通信机制，让无人机无需定位即可协作。
- 仿真显示新方法比基准策略更快完成全覆盖，局部覆盖更均匀。
- 适合无定位信息下的大规模无人机自主探索任务。
本文研究未知凸环境中多无人机的均匀扫掠覆盖问题，要求同质无人机群在无位置与姿态信息条件下，均匀访问环境各区域以完成采样任务。随机游走探索在此场景中具有实用性，因其无需定位且易于在集群中实现。本文证明，自组织神经网络（SoNS）框架——通过局部通信使机器人集群自组织为分层自适应网络——是此类环境随机探索的有前景控制方法。为此，我们提出一种基于SoNS的随机游走策略：无人机先自组织为线形队列，随后以保持队形的方式执行随机游走以覆盖环境。我们在仿真中将该方法与多种去中心化随机游走策略对比，结果表明，所提方法在全局和局部区域均能更快实现完全覆盖，并具备更高的覆盖均匀性。
原文摘要 · Abstract (English)
This paper addresses multi-UAV uniform sweep coverage in an unknown convex environment, where a homogeneous UAV swarm must evenly visit every portion of the environment for a sampling task without access to their position and orientation. Random walk exploration is practical in this scenario because it requires no localization and is easy to implement on swarms. We demonstrate that the Self-Organizing Nervous System (SoNS) framework, which enables a robot swarm to self-organize into a hierarchical ad-hoc communication network using local communication, is a promising control approach for random exploration in such environments. To this end, we propose a SoNS-based random walk method in which UAVs self-organize into a line formation and then perform a random walk to cover the environment while maintaining that formation. We evaluate our approach in simulations against several decentralized random walk strategies. Results show that our SoNS-based random walk achieves full coverage faster and with greater coverage uniformity than these benchmark strategies, both globally and in local regions.
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