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

53 Clevon 1 | Digest from the AGX Xaviers for control and comms with the UGV’s subsystems. The lower-level computations are handled by seven STM32-powered embedded microcontrollers, although the company notes that they might be overpowered for isolated, subsystem- specific microcontrollers, but their cost- effectiveness was viewed as valuable for future-proofing much of the internal network at a low price. The main and sub-level computers communicate via CAN FD, an extended version of CAN designed for use in high- performance vehicle ECUs, which Appo notes can push up to 5 Mbit/s of data over each CAN line. “CAN FD is very leading edge in the automotive sector, but its use in robotics has also grown,” he says. “We’re also using it to communicate between the main computer and the radar, but as the computer has multiple CAN interfaces we use one line for the embedded computers and another, separate line for the radar. “Meanwhile, the cameras’ outputs are received over GSML2 links; serial is used for the IMU, and automotive Ethernet for comms from the main computer. The software was developed using the latest C++ standards – C++20 and C++23 for embedded and high-level software respectively – with custom toolchains and powerful servers used for cross-compiling. “And for all our updates, we start with binary code going to the main computer, which then updates the embedded modules over CAN. Custom tools were key to keeping our development and testing loops tight – we can build the C++ code for our core software from scratch in 20 seconds, that’s literally how fast it goes.” GNSS inertial systems The Clevon 1’s IMU is made in-house using a reference design from CEVA Technologies, specifically its BNO080 MEMS IMU. “It’s an especially popular chip design for mobile phones,” Appo says. “And RTK GNSS on top of that achieves centimetric accuracy in traffic and narrow roads.” The GNSS antenna sits centrally atop the Clevon 1’s tower section, which connects via coaxial cable to a u-blox receiver module inside the company’s network comms module. This is a custom router built in-house that contains the u-blox chip, multiple Ethernet I/Os and a main network controller. “For RTK updates, we can use NTRIP services or our own base stations,” Appo says. “And to simplify our comms systems further for our next vehicle iterations, we’re looking into getting an antenna module from Techship or INPAQ to integrate our GNSS antenna and two 4G antennas inside its single enclosure.” Clevon anticipates that its new antenna system will integrating 2 x 2 MIMO 5G non-mmWave, wi-fi 6e with integrated Bluetooth and dual V2X channels, along with GNSS. “We’ve developed our extended Kalman filters in-house, which was a complicated process, not only for fusing GNSS and IMU data but remember that we have odometry – both wheel position and current speed – and all the other information we can get from the car,” Appo notes. “We refer to it as our self-estimation algorithm. We’re able to localise our vehicle in a way that achieves very precise path-following, and we’re continually refining the algorithm to add more sensor information and smoothness of data handling.” Perception systems Clevon’s engineers know however that working in logistics means driving through urban canyons and infrastructure that can render GNSS unsafe. That, and the need to drive through real- world traffic, means a multi-sensor suite for live 3D perception and localisation (with the use of deep neural networks for intelligent comprehension of surroundings) is critical. For vision and object detection, classification and avoidance (with colour), six automotive-grade Sekonix cameras are fitted around the Clevon 1, each providing a 120 º (overlapping) horizontal FoV and a 60 º vertical FoV. A liquid spray system is remotely triggered by the teleoperator to wash the camera lenses, although Appo comments that in the future, cleaning will be performed autonomously using the computer vision to recognise signs of dirt. “We’re not currently using Lidar for anything because a decent one costs as much as a Clevon 1,” Appo says. “Also, we’d need more than one for full FoV awareness, so at around $10,000 per Uncrewed Systems Technology | December/January 2023 The rear camera, which might in future be accompanied by a rear-facing radar, and additional radars on the front and sides