Path Planning Algorithms for Robotic Aquaculture Monitoring

TL;DR

This paper introduces a novel path planning algorithm for drone-based aquaculture monitoring, demonstrating improved efficiency and solution quality over existing methods for large-scale pond coverage.

Contribution

The paper presents the HAUCS Path Planning Algorithm (HPP), a new routing method optimized for large aquaculture farms, outperforming GLOP and attention models in solution quality and runtime.

Findings

HPP outperforms GLOP and AM in large pond instances.

GLOP is more efficient for small to medium farms but slower for larger ones.

HPP achieves better solution quality and faster runtimes for farms with over 200 ponds.

Abstract

Aerial drones have great potential to monitor large areas quickly and efficiently. Aquaculture is an industry that requires continuous water quality data to successfully grow and harvest fish. The Hybrid Aerial Underwater Robotic System (HAUCS) is designed to collect water quality data of aquaculture ponds to reduce labor costs for farmers. The routing of drones to cover each fish pond on an aquaculture farm can be reduced to the Vehicle Routing Problem. 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 (AM) for routing problems. GLOP is the most efficient solver for 50 to 200 ponds at the expense of long run times, while HPP outperforms the other methods in solution quality and run time for instances larger than 200 ponds.