A System for Autonomous Seaweed Farm Inspection with an Underwater Robot

• Main Authors: Arnwald, A. (Author), Bhat, S. (Author), Bore, N. (Author), Cong, Z. (Author), Folkesson, J. (Author), Gröndahl, F. (Author), Ling, L. (Author), Ljung, C. (Author), Özkahraman, Ö (Author), Severholt, J. (Author), Sprague, C.I (Author), Stenius, I. (Author), Thomas, J.-B (Author), Torroba, I. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Alga farm; algae farm; Aquaculture; Aquaculture industry; AUV; behavior trees; Behaviour Trees; field testing; Field testing; Integration testing; mission planning; Mission planning; Robots; Seaweed; seaweed farm; Seaweed farm; simulation; Simulation; system integration; System integration; Underwater robots
• Online Access: View Fulltext in Publisher
• ISBN: 14248220 (ISSN)
• DOI: 10.3390/s22135064

This paper outlines challenges and opportunities in operating underwater robots (so-called AUVs) on a seaweed farm. The need is driven by an emerging aquaculture industry on the Swedish west coast where large-scale seaweed farms are being developed. In this paper, the operational challenges are described and key technologies in using autonomous systems as a core part of the operation are developed and demonstrated. The paper presents a system and methods for operating an AUV in the seaweed farm, including initial localization of the farm based on a prior estimate and dead-reckoning navigation, and the subsequent scanning of the entire farm. Critical data from sidescan sonars for algorithm development are collected from real environments at a test site in the ocean, and the results are demonstrated in a simulated seaweed farm setup. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.