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This class will give an introduction to robotics. It will be structured into the following parts:

• Generating motion and and dynamic control: This chapter will cover coordinate frames, kinematic chains, inverse kinematics, Jacobian, singularities, holonomic constraints, Ackermann-drive, PID, Newton-Euler.
• Planning: Planning around obstacles, path finding, Dijkstra, A*, configuration space obstacles, RRTs, lattice planners, gradient methods, potential fields, splines.
• Localization and mapping: state estimation problem, Bayesian filter, Odometry, Particle & Kalman filter, Extended and Unscented Kalman-Filter, simultaneous localization and mapping (SLAM).
• Vision and perception: SIFT, HOG-features, Deformable parts models, hough transform, lane detection, 3d-point clouds, RANSAC .

After these lectures, students will be able to design basic algorithms for motion, control and state estimation for robotics