Unmanned Systems Technology 008 | Alti Transition UAS | Ground control systems | Xponential 2016 report | Insitu Orbital N20 | UAVs | Solar power | Oceanology International 2016 report

18 In conversation | Dr Zongbo Wang and high-level decision making based on the data, so this is very suitable for UAVs,” he says. “That is because it’s easy to schedule the tasks, especially for some critical scheduling tasks and making sure there are no delays. If you have the MPU and FPGA combined then you can partition the tasks for more real-time and time- critical functions in the FPGA fabric. “If you only have an MPU then you have to rely on an operating system, so you are mixing different tasks – you need low-level comms handling that includes connection to the ground station, and you need position estimation. Then you need some sensor information, and this is the third important element of the control algorithm. “This then uses the data from the mission tasks to determine how fast to turn the rotors and so on. Now an MPU makes all these decisions together, and I don’t think that should be the most optimised case.” Power consumption There are major advantages with an FPGA as the demands on the UAV control systems grow. Dr Wang points to other approaches using graphics processing units (GPUs) which may have up to 512 processing elements and are being used in some UAV designs. “All the chip makers are looking to reduce power, and if you use an FPGA to do the processing then you may find it uses even less power than a GPU,” he says. “FPGAs have not been more widely used because the technology in them is more challenging for engineers from an aerospace background. There are questions about how to partition the design of the control system between the FPGA and the ARM processors. FPGAs are seen as too complicated to programme for a flight control engineer.” So the latest design tool from Xilinx, called SDSoC, takes a design written in C or C++ code and profiles the code to find the areas that would most benefit from being implemented in the logic fabric. This then allows some elements of the design to run on the Linux operating system on one core, with the high- performance functions running on the logic fabric, providing the best of both worlds without having to change the way the engineer designs the system. Aerotenna has taken the ArduPilot open source autopilot and performed this optimisation alongside the radar sensor design. “Our plan is to partition the processing to use the FPGA logic to do some of the tasks alongside the ARM cores, such as PWM coding and decoding – every UAV needs that. In an MPU it is hundreds of lines, while in the FPGA it is less than 100 lines to implement such a function in real time,” he says. This approach opens up new ways of building UAV control systems with different design blocks of intellectual property (IP). “There will be a lot of these IP blocks,” says Dr Wang. “We will use a drag-and-drop model so that you drag the function from the IP library and connect it to the ARM core. Then the core will run only the high-level functions and use the FPGA logic to do the rest.” The first step for this is the combined autopilot and radar system called μSharp. This incorporates adaptive sensing technology that optimises its response, regardless of flight status. It detects obstacles all around without blind spots and corrects flight course in time to prevent collisions. Dr Wang says, “We have the demo systems and are moving to production. We can work with commercial drones of less than 1.5 kg – the most widely available systems on the market – and are doing this development in parallel.” Sensor fusion This technology is the way forward for UAV designs, he says. “I believe radar can do a great job and has advantages over a lot of sensors, but the future needs to be a package of sensors, and a combination of radar, ultrasound and camera integrated in UAVs to do a smart job. That’s the second important element which is sensor fusion, and that will take the application of FPGA in UAVs to another level. “Now we have an excellent collision avoidance system but we are not stopping there; we are putting cameras on the platform with sensor fusion.” With thanks to Aaron Behman of Xilinx for his contribution to this article. June/July 2016 | Unmanned Systems Technology Using an FPGA is changing how UAVs are controlled by making the processing of algorithms more efficient