From first principles
to autonomous motion.
All 96 sessions are fully written and available now. Each week follows Learn → Build → Experiment → Improve. Use the handbook and runnable examples; let longer builds span sessions. Hardware validation remains separate from authored content.
Programming, electronics & motion
Meet your robot
Identify what you can command, measure and explain.
Outcome: Inventory and observable behaviour
Python with a purpose
Use variables, loops and functions to express robot behaviour.
Outcome: A repeatable command sequence
Circuits and power
Explain voltage, current, resistance and safe power paths.
Outcome: A labelled circuit and LED experiment
Motors and motion
Compare motor commands with actual travel.
Outcome: A controlled straight-line trial
Measurement & kinematics
Measure motion
Estimate speed from repeated distance and time measurements.
Outcome: A speed dataset with uncertainty
Wheel encoders
Calibrate distance per encoder count.
Outcome: A counts-to-distance calibration
Turning and kinematics
Predict differential-drive motion using wheel distances.
Outcome: A calibrated turn model
Characterize your robot
Compare models across surfaces and battery conditions.
Outcome: A baseline performance report
Feedback & PID control
Close the loop
Use measured speed to correct a command.
Outcome: An open-loop versus feedback comparison
Proportional control
Relate error and gain to response and oscillation.
Outcome: A gain sweep
Integral and derivative
Explore offset, windup and sensitivity to noise.
Outcome: A constrained PID experiment
Tune and validate
Tune on one route and validate on another.
Outcome: A motion-controller validation report
Sensors & obstacle avoidance
Read the world
Measure sensor variation under controlled conditions.
Outcome: A sensor calibration dataset
Detect obstacles
Choose thresholds and explain false detections.
Outcome: A tested obstacle detector
React reliably
Implement a finite-state obstacle response.
Outcome: A state diagram and recovery tests
Combine evidence
Resolve disagreement between sensors.
Outcome: A robust avoidance demonstration
Localization & navigation
Estimate position
Integrate wheel motion and quantify drift.
Outcome: An odometry trace
Correct drift
Compare odometry with a known landmark.
Outcome: A position-correction experiment
Plan a route
Represent a grid and find a collision-free path.
Outcome: A simulated path planner
Follow waypoints
Connect a planned route to motion commands.
Outcome: A waypoint validation report
Autonomous navigation capstone
Define the mission
Write measurable requirements and a test course.
Outcome: A capstone proposal
Integrate the system
Combine sensing, estimation, planning and control.
Outcome: An integration build
Test under pressure
Test unseen conditions and analyze failures.
Outcome: A reliability and failure report
Demonstrate and defend
Explain the design using measurements and limitations.
Outcome: A final demonstration and portfolio