Contrôle temps-réel et efficace pour la course autonome
Li, Nan · theses.fr (ABES) · 2023
Recently, the autonomous driving domain has made tremendous advancements. By investigating the challenge of autonomous racing, a special form of autonomous driving, we seek to better understand how vehicles could be efficiently controlled in real-time settings for handling intricate dynamic situations. We develop an approach based on Nonlinear Model Predictive Control (NMPC), a cutting-edge control technique, that can attain the optimal progress time of the vehicle while accounting for nonlinear system dynamics and obstacle-related time-varying constraints. To deal with the presence of an opponent vehicle, we combine NMPC with Mixed Integer Programming (MIP) for encoding safe and efficient overtaking maneuvers. However, it is challenging to implement NMPC on embedded devices due to its high calculation complexity. One concern is ensuring real-time execution of the controller, which necessitates strict adherence to the time budget restriction and rigorous compliance with deadlines. Another problem is managing to make the control efficient, which calls for the maintenance of an adequate level of system performance. We propose a multi-step recomputation approach for the single-vehicle race mode, which is triggered based on specific events. One of the triggering conditions aims at ensuring that the real-time budget constraints are respected. The other triggering condition serves for reducing computational time while retaining quasi-optimal lap time performance. For head-to-head racing mode, we propose an algorithm as an online feasible alternative to MIP encoding. It efficiently aggregates overtaking decisions and schedules them at a deterministic control frequency to meet real-time requirements. In a generic system architecture, we also take into account other software components besides the controller, such as opponent detection and self-localization algorithms, which collectively constitute a Directed Acyclic Graph (DAG). To assign DAG components to available processors with varying degrees of parallelism, we propose a task execution model which decreases the latency, increases the control update rate, and eventually enhances the system performance. In summary, this thesis provides a set of mechanisms aimed at an efficient implementation of real-time control in autonomous systems.