This course covers the concepts, techniques, algorithms, and state-of-the-art approaches for robot localization, mapping, and planning. The course starts from basic concepts in 2D kinematics and probability and then introduces probabilistic approaches for data fusion. Then, the course introduces the trajectory planning problem in the time domain and free space. The motion planning problem is defined in a canonical version of the problem and the concept of configuration space is introduced. A selection of representative planning techniques is covered from probabilistic to heuristic techniques. Finally, some mapping representations and algorithms are presented. | Prerequisite: CS-UY 1114 and MA-UY 2034 and PH-UY 1013 or equivalents (see Minor in Robotics)

Robotics (Undergraduate)
3 credits – 14 Weeks

Sections (Fall 2023)

ROB-UY 3303-000 (19176)
09/05/2023 – 12/15/2023 Mon,Wed
2:00 PM – 3:00 PM (Early afternoon)
at Brooklyn Campus
Instructed by Loianno, Giuseppe