Path Planning for the Hybrid Aerial Underwater Robotic System

Abstract

Marine food chains are highly stressed by aggressive fishing practices and environmental damage. Aquaculture has increasingly become a source of seafood which spares the deleterious impact to wild fisheries, but it requires continuous water quality data to successfully grow and harvest fish. Aerial drones have great potential to monitor large areas quickly and efficiently. The Hybrid Aerial Underwater Robotic System (HAUCS) is a swarm of unmanned aerial vehicles (UAVs) and underwater measurement devices designed to collect water quality data of aquaculture ponds. The routing of drones to cover each fish pond on an aquaculture farm can be reduced to the Vehicle Routing Problem (VRP). A dataset is created to simulate the distribution of ponds on a farm and is used to assess the HAUCS Path Planning Algorithm (HPP). Its performance is compared with the Google Linear Optimization Package (GLOP) and a Graph Attention Model (GAM) for routing around the simulated farms. The three methods are then implemented on a team of waterproof drones and experimentally verified at Southern Illinois University’s (SIU) Aquaculture Research Center. GLOP and GAM are demonstrated to be efficient path planning methods for small farms, while HPP is likely to be more suited to large farms. HAUCS shows great value as a future direction for intelligent aquaculture, but issues with obstacle avoidance and robust waterproofing need to be addressed before commercialization. The future of aquaculture promises more integrated and sustainable operations by mimicking natural systems and leveraging deeper understandings of biology.