Sensor-based control architecture for a car-like vehicle

This report presents a control architecture endowing a car-like vehicle moving in a dynamic and partially known environment with autonomous motion capabilities. Like most recent control architectures for autonomous robot systems, it combines three functional components: a set of basic real-time skills, a reactive execution mechanism and a decision module. The main novelty of the architecture proposed lies in the introduction of a fourth component akin to a meta-level of skills: the sensor-based man uvres, i.e. general templates that encode high-level expert human knowledge and heuristics about how a speci c motion task is to be performed. The concept of sensor-based man uvres permit to reduce the planning e ort required to address a given motion task, thus improving the overall response-time of the system, while retaining the good properties of a skill-based architecture, i.e. robustness, exibility and reactivity. The report focuses on the trajectory planning function (which is an important part of the decision module) and two types of sensor-based man uvres, trajectory following and parallel parking, that have been implemented and successfully tested on a real automatic car-like vehicle placed in di erent situations. Key-words: motion autonomy, control architecture, car-like vehicle.