Issue 45 | Uncrewed Systems Technology Aug/Sept 2022 Tidewie USV Tupan | Performance monitoring | Bayonet 350 | UAVs insight | Xponential 2022 | ULPower UL350i and UL350iHPS | Elroy Air Chaparral | Gimbals | Clogworks Dark Matter

102 Focus | Gimbals of the electronics inside the vehicle do not cause build-ups of heat that are inaccessible to heat extraction systems). While that can lead to extra cabling and internal space concerns for the vehicle OEM to deal with, having a separate but still fully compatible processing system can nonetheless give considerable cost savings and design freedom over fully integrated gimbal-and- processor systems. As a halfway measure, some designs allow easy swapping-out of external GPUs through open architecture frameworks, to allow easy upgrading and dropping in of anyone’s classifier. That also means the end-user isn’t reliant on or stuck with the gimbal manufacturer’s own components. Beyond hardware concerns, the drive for reliability and intelligence in video processing functions is driving gimbal manufacturers to find ways of making the adoption and refinement of new AI modules easier. That is pushing the popularity of open software platforms driven by communities of programmers that will enable suppliers and customers alike to cherry-pick new algorithms and fixes to serve their needs. That capability is especially important, as no one-size-fits-all software bundle can ever serve an entire uncrewed systems market segment. Even different police precincts for instance will want gimbal systems that can detect and classify cars based on varying differentiators, such as licence plate symbols, movement speeds, thermal signatures and signs of damage. Geo-pointing and tracking Besides motors and motor controllers, various technologies contribute to the data inputs needed for smooth and accurate geo-pointing, tracking and zooming of gimbals onto targets. These can be especially critical for military and search & rescue users, whose UAVs may be racing against time to find people or objects in very unstable environments, as their gimbals will need to compensate for motion from multiple sources. While most of the factors contributing to geo-pointing relate to sensing data, a key mechanical component worth considering here is slip rings. While these are generally a well-established technology, any gimbal using low-end (or no) slip rings will be largely incapable of continuous panning. That can cause major issues when geo-pointing about the platform, as after a certain panning distance the gimbal will need to reset. One of the foremost sensing technologies for gimbals with sensored motion control is the use of encoders for position feedback data. High precision, resolution and linearity (or low non- linearity) are some of the most critical encoder parameters being eyed by gimbal manufacturers. Different encoder designs come with their own parts, form factors and performance qualities that should be considered before selecting them. Magnetic encoders for instance need a magnet as a point of reference, and while BLDC motors provide these in abundance, if the encoder’s sensing circuit fails to detect a magnet it will either output a random angle or an error message. Optical encoders meanwhile use a sensor to take note of positional changes as light passes through a patterned wheel or disc, but the latter can become contaminated by dust and moisture more easily than magnetic encoders. Hall effect magnetic sensors have become particularly popular among high- end gimbal manufacturers, as they can be made more compact than other kinds of encoders, and can also provide higher data sensitivities and resolutions. Inertial sensors meanwhile continue their gradual improvements in performance versus size and weight, and gimbal manufacturers will periodically update their products with newer inertial systems as they come out, particularly as MEMS systems become more powerful and FOGs are designed smaller and flatter, although the latter is rarely used in UAV gimbals except in systems destined for GNSS-denied conditions. One of the more striking changes to look for in the future might be the adoption of temperature-controlled IMUs, as the performance and reliability of inertial sensors will waver depending on the surrounding temperature; some test results indicate that they work best at roughly 60 C. Temperature control may be achieved in two ways. The first is by testing IMUs across different temperature conditions to develop algorithms and maps for correcting temperature-driven measurement drifts. The second is by modifying a gimbal to include resistors or other kind of heater that will warm the IMU to a target temperature. The latter is the more thorough August/September 2022 | Uncrewed Systems Technology Balance is one of the most important qualities to test and optimise in any gimbal, as it affects almost every aspect of performance (Courtesy of Embention)