Aquatic macrophyte is a generic denomination for macro-algae with active photosynthetic parts that remain totally or partially submerged in fresh or salty water, in rivers and lakes. Currently, algae monitoring is carried out manually by collecting samples to send for laboratory analysis. In most cases, harmful algal blooms are already widespread when the results are disclosed. This paper proposes the application of a team of heterogeneous Unmanned Aerial Vehicles (UAVs) that cooperate to increase the system’s overall observation range and reduce the reaction time. Leader UAV, featured with a deep-learning-based vision system, covers a pre-determined region and determines high-interest inspection areas in real-time. Through a multi-robot Informative Path Planning (MIPP) approach, the leader UAV coordinates a team of customized quadcopter (named ART2) to reach points of interest, managing their route dynamically. ART2s are able to land on water, and collect and test samples in situ by applying phosphorescence sensors. While path planning, task assignment, and route management are centralized operations, each UAV is conducted by a decentralized trajectory tracking control. Simulations performed in a realistic environment implemented on the Unity platform and experimental proof of concepts demonstrated the reliability of the proposed approach. The presented multi-UAV framework with heterogeneous agents also enables the reconfiguration and expansion of specific objectives, in addition to minimizing the task completion time by executing different processes in parallel. This preventive monitoring enables a plague control action in advance, solving it faster, cheaper, and more effectively.

水生植物是具有活跃光合作用部分的大型藻类的总称，这些藻类完全或部分浸没在河流和湖泊的淡水或咸水中。目前，藻类监测是通过收集样本送实验室分析来手动进行的。在大多数情况下，当结果公布时，有害藻华已经普遍存在。本文提出应用一组异构无人机（UAV）进行协作，以增加系统的整体观测范围并减少反应时间。Leader无人机搭载基于深度学习的视觉系统，覆盖预定区域，实时确定感兴趣的巡检区域。通过多机器人信息路径规划 (MIPP) 方法，领先无人机协调定制四轴飞行器团队（名为 ART2）到达兴趣点，动态管理其路线。ART2 能够降落在水面上，并通过应用磷光传感器就地收集和测试样本。虽然路径规划、任务分配和路线管理是集中操作，但每架无人机都是通过分散的轨迹跟踪控制进行的。在 Unity 平台上实现的现实环境中进行的模拟和概念的实验验证证明了所提出方法的可靠性。除了通过并行执行不同的进程来最小化任务完成时间之外，所提出的具有异构代理的多无人机框架还可以重新配置和扩展特定目标。这种预防性监测可以提前采取鼠疫控制行动，更快、更便宜、更有效地解决问题。