Investigating optimal unmanned aircraft systems flight plans for the detection of marine ingress

From the shutting down of coastal tourism industries, the mass destruction of aquaculture, to the clogging of power station water intakes, marine ingress events have the potential to cause widespread disruption along our coastlines. To gain the ability to respond to such events, efforts are being made to advance the understanding of bloom events which predominantly present as large aggregations of jellyfish, or detached aquatic macroalgaes in the water column. This paper investigates the optimal flight search patterns with a focus on marine ingress bloom detection from unmanned aircraft systems (UAS). The detection performance of four flight search patterns are examined against five different bloom shapes. Monte-Carlo simulations are deployed to assess probable per-formance of flight search pattern against variable bloom shapes. A total of 50,000 simulated flights were con-ducted, offering a maximum of 500 million marine ingress objects for possible detection. A two phased flight approach is proposed, with first phase flights conducted as area search strategies, and second phase flights as datum searches for scenarios where some information of possible bloom location is available. Parallel sweep was found to be the best performing generalist flight search pattern, closely followed by the phase two search pattern expanding square. Crossing barrier was found to be competitive but appeared to lend itself towards specific detection scenarios with sector search being a consistently poor performing flight search pattern. This paper also investigates the comparative performance of visual line of sight (VLOS), extended visual line of sight (EVLOS), and beyond visual line of sight (BVLOS) operations. Increase of total survey area was found to increase bloom detection frequency, with BVLOS operations the highest performer successfully increasing bloom detection by a factor of 3.7. This paper exhibits the first assessment of flight search patterns within the context of drone-based detection of marine ingress bloom events. This should facilitate the development of an early warning detection system that can provide reliable warning to coastal industries prior to a marine ingress event occurring.