Computer Vision-based Algae Removal Planner for Multi-robot Teams

Water pollution has caused increased incidence of algal growth around the globe. Harmful algae blooms result in massive economic losses. In this paper, a multi-robot based task planner is designed to remove excessive algae from water bodies and to identify algae build-up so that prompt action can be taken against its accumulation. Computer vision is incorporated to enable algae detection and area estimation based on training, comparing, and evaluating various advanced deep learning models using our custom algae dataset. We further propose a novel algorithm for robot resource allocation between bounding boxes of detected algae based on multivariable optimization. This systematic solution is evaluated in a simulated environment, demonstrating how the robots are optimally assigned to the detected algae patches for algae removal.