Description
Mobile Robotsare increasingly working in close interaction with human beings in environments as diverse as homes, hospitals, public spaces, public transportation systems and disaster areas. The situation is similar when it comes toAutonomous Vehicles, which are equipped with robot-like capabilities (sensing, decision and control).
Such robots must balance constraints such assafety, efficiency and autonomy, while addressing the novel problems ofacceptabilityandhuman-robot interaction. Given the high stakes involved, developing these technologies is clearlya major challenge for both the industry and the human society.
Course Objective
The objective of this course is to introduce thekey concepts required to program mobile robots and autonomous vehicles. The course presents bothformal and algorithmic tools, and for its last week's topics (behavior modeling and learning), it will also providerealistic examples and programming exercises in Python.
This course is designed around areal-time decision architecture using Bayesian approaches. It covers topics such as:
- Sensor-based mapping and localization: presentation of the most popular methods to perform robot localization, mapping and to track mobile objects.
- Fusing noisy and multi-modal data to improve robustness: introduction of both traditional fusion methods as well as more recent approaches based on dynamic probabilistic grids.
- Integrating human knowledge to be used for scene interpretation and decision making: discussion on how to interpret the dynamic scene, predict its evolution, and evaluate the risk of potential collisions in order to take safe and efficient navigation decisions.
