Uncrewed Systems Technology 047 l Aergility ATLIS l AI focus l Clevon 1 UGV l Geospatial insight l Intergeo 2022 report l AUSA 2022 report I Infinity fuel cell l BeeX A.IKANBILIS l Propellers focus I Phoenix Wings Orca

95 BeeX A.IKANBILIS | In operation among wind turbines without constant corrective GNSS or software updates will probably be navigating incorrectly. “Our AI algorithm stack is split into two sections, one of which is for baseline navigation work, as in how the HAUV thinks and moves,” Chia says. “We operate very differently from vehicles that rely entirely on waypoints, because our HAUV can choose to deviate from them in order to prioritise the actual inspection goal our customers want. “It follows that the second part is the analysis of mission-specific areas of concern – essentially the targets our end- users want the A.IKANBILIS to recognise, zoom in on and search around for detail.” BeeX’s original purpose for installing an imaging sonar onboard was purely so that the A.IKANBILIS could sense where it was going. However, during development, Chia and her team realised that the sonar images could be stitched together with the camera and inertial information to create a new data product. That was a 3D mosaic of everything the vehicle could sense throughout its survey, and it provided a means of giving the end-user more high-quality data without adding more sensors or needing to swap them in or out for a second dive. “Our technique of cross-referencing between camera and sonar images minimises the number of false positives that the vehicle detects underwater, primarily because we carried out a lot of real-world tests while developing the system in Singapore’s coastal waters, where we had even less visibility than at Nordsee One,” Chia recounts. “These were mainly trial inspections of near-shore infrastructure, particularly of 50-100 cm pile structures along the wharf terminals with a similar shape to wind turbine monopiles, and some sloped land reclamation projects that would exhibit similar sensor interferences as the piles. Ports and harbours tend to have very murky waters that often make camera solutions unviable, but since our roots are in self-driving cars, we had considerable experience of using multiple payloads simultaneously.” The tests were scaled up in May 2022 with the inspection of a 3 m-diameter monopile, which was repeated to optimise the A.IKANBILIS’ adaptive autonomy until the team was confident it could successfully inspect the 6.7 m monopile at Nordsee One. “So, as much as the camera and illuminators are important for the end- user to gauge the condition of the wind farm – and we allow customers to install additional payloads specifically for data collection – our autonomy and inspection capabilities are designed to work with or without visibility in the water,” Chia says. A data snapshot The Nordsee One survey was performed repeatedly over the course of 5 days. Although most of the valuable data (for Northland Power) was captured on the first day, BeeX’s engineers were invited to develop, deploy and test new autonomous behaviours on the A.IKANBILIS using subsequent launches, as each survey’s telemetry was highly valuable for training the HAUV’s autonomy. The modular adaptive autonomy stack enabled a fast turnaround time and straightforward installations of new behavioural blocks developed ad hoc. After hauling the HAUV out of the water using the tether each time, and performing a minor freshwater cleaning of the Uncrewed Systems Technology | December/January 2023 The HAUV is recovered using its tether, before being cleaned and sending ‘snapshots’ to the wind farm operator of the most important areas observed during the inspection