Robotics Foundations

Anatomy, actuators, sensors and frames. What robots are, their actuators and sensors, the matrix maths behind them, and frames, rotations and homogeneous transforms.

• Introduction to Robotics & Automation — What robots are and how automation works. Learn the history and definitions of robots, the anatomy of a robot (links, joints, actuators, sensors, controller, end effector), joint types and degrees of freedom (Grübler–Kutzbach mobility), robot classes (articulated, SCARA, Cartesian/gantry, cylindrical, delta/parallel, collaborative, mobile, humanoid, drones), key specifications (payload, reach, repeatability vs accuracy, speed), workspace, fixed vs programmable vs flexible automation, and the robotics software stack — with Python for mobility, a 2R arm's workspace and payload torque.
• Actuators — Actuators make robots move. Learn electric motors (servo, stepper, BLDC), gear reducers (planetary, harmonic, cycloidal), hydraulic and pneumatic actuators, sizing torque/speed/inertia ratio, and emerging soft and series-elastic actuators.
• Sensors — Robots need to sense themselves and their world. Learn encoders and resolvers, force/torque sensors, IMUs, proximity and vision sensors, LiDAR and time-of-flight, resolution vs accuracy, and sensor fusion.
• Matrices & Determinants — Matrices organise systems of numbers and linear maps. Learn matrix types (square, diagonal, identity, symmetric, triangular, orthogonal), addition and multiplication, transpose and conjugate transpose, determinants by cofactor expansion and row reduction, minors and cofactors, the inverse and adjugate, and properties like det(AB) = det A det B.
• Spatial Descriptions: Frames, Rotations & Homogeneous Transforms — The mathematics of position and orientation. Learn coordinate frames, position vectors, rotation matrices (properties, elementary rotations about x, y, z), composition and inverse of rotations, Euler angles and roll–pitch–yaw (and gimbal lock), axis–angle and quaternions, homogeneous transformation matrices (4 × 4) that combine rotation and translation, chaining transforms (world → base → tool → object), inverse transforms, and frame conventions in ROS (tf) — with NumPy code composing frames, transforming points and converting a rotation to a quaternion.