Multi-UAV Collaborative System for the Identification of Surface Cyanobacterial Blooms and Aquatic Macrophytes

Abstract

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.