Examples: Dubins car, differentialР”Вrive car, unicycle, pendulum, cartpole, quadcopter. Homogeneous coordinates and transformations.

Syllabus, Quiz 0 (Introduction, Background, Expectations) Sensors and ActuatorsР”В¬amera, LiDAR, tactile, IMU, depth, GPS, Hall-effect sensors, encoders, RGBD. Motivation, logistics, rough description of assignments, sense-plan-act paradigm. Points seen from two cameras? How can we estimate the cameraРІР‚в„ўs pose (and therefore the robotРІР‚в„ўs) while it is moving in the environment? The Geometry of Computer Vision: how can modeling pixel projections on an RGB camera help us infer the 3D structure of the world? How can we triangulate.Sensor measurements in order to form an accurate estimate of the (hidden) state? How can we determine the relative weighs of multiple

State Estimation: the state of the robot is not always directly measurable/observable.

Mapping: how can we combine noisy measurements from sensors with the robotРІР‚в„ўs pose to build a map of the environment?.

Feedback Control and Planning: how can we compute the state-(in)dependent commands that can bring a robotic system from its current state to a desired.

Robots move in response to given commands?

Kinematics and Dynamics: how can we model robotic systems using approximate physical models that enable us to make predictions about how.

This course will broadly cover the following areas:

Occupancy grid mapping unknown posies