A new control architecture for MuCAR

Abstract

The Munich Cognitive Autonomous Robot Car 3rd Generation (MuCAR-3) has won several international achievements in the past. Recently, the system's control architecture (meaning the interplay between perception, planning and control) was overhauled. Our goals were to simplify the interaction between modules as well as to meet higher requirements for both smoothness and precision. The decoupling of modules helps with tackling more challenging scenarios and facilitates the development of each module. Since state machines struggle with scalability, its interactions with other modules were minimized. We now use a generalized planning layer rather than so-called maneuvers. This paper aims at showcasing the difference between our previous and current architecture. We focus on the improvements that were achieved even for very simple scenarios — in this case off-road platooning. Using the same control algorithms, we achieve both improvements in smoothness and precision, two classically orthogonal goals. Tests were conducted in simulation and verified with MuCAR-3 on our test site.