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This chapter covers the core systems that enable autonomous robots: how robots represent and reason about motion, how their sensors turn the physical world into measurements, and how perception, simulation, and action come together in a working stack. The probabilistic side of the picture (recursive Bayesian estimation, Kalman filters, occupancy mapping, and SLAM) lives in its own chapter: State Estimation & Mapping.

Kinematics & Dynamics

Configuration spaces, homogeneous coordinates, motion representations, and wheeled robot kinematics.

Sensor Models

Camera models and calibration, plus probabilistic beam and likelihood-field models for range sensors.

Systems Integration

Gazebo simulation, ROS applications, and world authoring.
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