Path planning algorithms for robotic aquaculture monitoring

Anthony Davis; Srijita Mukherjee; Paul S. Wills; Bing Ouyang

doi:10.1117/12.2618783

31 May 2022 Path planning algorithms for robotic aquaculture monitoring

Anthony Davis, Srijita Mukherjee, Paul S. Wills, Bing Ouyang

Proceedings Volume 12097, Big Data IV: Learning, Analytics, and Applications; 120970K (2022) https://doi.org/10.1117/12.2618783
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

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 (GAM) 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.

Conference Presentation

Citation Download Citation

Anthony Davis, Srijita Mukherjee, Paul S. Wills, and Bing Ouyang "Path planning algorithms for robotic aquaculture monitoring", Proc. SPIE 12097, Big Data IV: Learning, Analytics, and Applications, 120970K (31 May 2022); https://doi.org/10.1117/12.2618783

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