The Formula Student is an international educational engineering competition in which teams of students from around the world design, build, and race their own formula race cars. The competition includes 3 categories: Electric, Driverless, and Combustible cars. The challenge is not only to build the fastest race car, but also to show the best behavior in endurance, acceleration, or skid pad for example.
As an expert in Inertial Navigation Systems and partner of several teams, we interviewed various teams of engineers using our Inertial Measurement Unit (IMU) combined with Global Navigation Satellite System (GNSS) to understand what the key elements to success are.
The Importance of the IMU/GNSS for Precise Car Dynamics
The IMU/GNSS provides decisive information on the car state such as position, speed, yaw rate, slip angle, acceleration and orientation to the competing teams’ cars, as stated by D. Kiesewalter, from AMZ Racing: “We required an IMU for several reasons. Primarily to determine the position state of our car. We also needed to have efficient dynamics control & a reliable and accurate determination of Euler Angles (roll, pitch, and heading).” This way, engineers of electric and combustible cars can understand what to improve by comparing the actual state to the theoretical one.
Mastering acceleration is primordial during Formula races. When the car accelerates too much, it can drift, which causes the wheels to wear out. To minimize tire wear and get the most of the engine’s power and performance, acceleration has to be checked.
Tracking the race car trajectory is essential. A circuit analysis is conducted thanks to the IMU/GNSS data, especially position, and helps determine if the car is well positioned inside the circuit or when turning.
Let’s not forget that the Formula Student is a race. One of the competition goals is to go faster on the track than the other teams. Speed is therefore a crucial factor to study, thanks to the IMU/GNSS. But it is even more important for electric race cars, as they need to track the consumed energy.
Driverless race cars: Taking the best of Heading and Navigation out of the IMU/GNSS
If a single-antenna GPS based heading is enough for racing cars, driverless vehicles require a more precise heading provided by a dual-antenna IMU/GNSS. It allows faster initialization and delivers true heading even in stationary position. J. Liberal Huarte from UPC Driverless (ETSEIB) explains that heading and localization are essential for other parts of the equipment to function properly: “When we operate with LiDAR technologies, the fact that you are headed 1 degree to one side or the other influences a lot the position. So, precise heading is a big requirement. And also, localization and mapping: it is very important to localize yourself in the X, Y.” Therefore, implementing a Dual GNSS/IMU in this type of race car is the best solution, as it provides true heading and position, which also helps stabilize the LiDAR.
Heading is as important as precise navigation for driverless race cars. Real Time Kinematic (RTK) allows an extremely accurate estimation of the position (1-2 cm). The more accurate the IMU/GNSS is, the more the car is able to stay in the circuit lane without drifting.
The IMU/GNSS also helps conduct a circuit analysis that determines if the car is well positioned and so optimizes the trajectory.
Less implementation time = more time for the whole project
“We have very small test time, so if it goes fast, we can go faster on the track and test more”, states A. Kopp, Vehicle Dynamics Control, TUfast Racing. Teams don’t have much time to integrate the different parts of the vehicle and to test them. As CAN and ROS framework are mainly used by automobile engineers, IMU/GNSS that can be part of such workflows can save tremendous time of development. A clean C library provided with examples is another way to help teams with their integration.
SBG Systems supports new ways to design cars. Students are welcome to send their sponsorship application through our website!
Image Credit: © Formula Student Germany © maru ©FSG – media team