151-0325-00L  Planning and Decision Making for Autonomous Robots

SemesterAutumn Semester 2022
LecturersE. Frazzoli
Periodicityyearly recurring course
Language of instructionEnglish

AbstractPlanning safe and efficient motions for robots in complex environments, often shared with humans and other robots, is a difficult problem combining discrete and continuous mathematics, as well as probabilistic, game-theoretic, and ethical/regulatory aspects. This course will cover the algorithmic foundations of motion planning, with an eye to real-world implementation issues.
ObjectiveThe students will learn how to design and implement state-of-the-art algorithms for planning the motion of robots executing challenging tasks in complex environments.
ContentDiscrete planning, shortest path problems. Planning under uncertainty. Game-theoretic planning. Geometric Representations. Steering methods. Configuration space and collision checking. Potential and Navigation functions. Grids, lattices, visibility graphs. Mathematical Programming. Sampling-based methods. Planning with limited information. Multi-agent Planning.
Lecture notesCourse notes and other education material will be provided for free in an electronic form.
LiteratureThere is no required textbook, but an excellent reference is Steve Lavalle's book on "Planning Algorithms."
Prerequisites / NoticeStudents should have taken basic courses in optimization, control systems, probability theory, and should be familiar with modern programming languages and practices (e.g., Python, and/or C/C++). Previous exposure to robotic systems is a definite advantage.
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed