Learning Targets:

1. Wire DC motors via an H-bridge motor driver, wheel encoders, and an ultrasonic sensor to the microcontroller according to datasheets; verify continuity and correct polarity with a multimeter.

2. Configure the toolchain (Arduino IDE or PlatformIO), flash firmware, and confirm bidirectional serial telemetry at a specified baud rate.

3. Program GPIO and PWM to drive motors and implement encoder interrupts achieving a stable sampling rate (≥100 Hz) with missed-interrupt rate <1%.

4. Implement sensor polling and publish timestamped telemetry over UART/USB at ≥10 Hz; log at least 2 minutes of data without buffer overruns.

5. Enforce electrical and operational safeguards (current limiting, motor enable/disable, emergency stop) and demonstrate safe shutdown on command or fault.

Modules

Learning Targets:

1. Derive forward and inverse kinematics for a differential-drive platform and compute pose updates from wheel tick counts over a recorded trajectory.

2. Implement discrete-time PID velocity control for each wheel; tune gains (e.g., Ziegler–Nichols or step-response) to achieve rise time and overshoot within given bounds.

3. Integrate odometry at ≥20 Hz and estimate robot pose; compute RMSE against ground-truth markers staying below a specified threshold (e.g., <0.1 m over 5 m).

4. Compare open-loop versus closed-loop tracking by generating plots in Python/Matplotlib or MATLAB; report quantitative improvements in steady-state error and disturbance rejection.

Modules

Learning Targets:

1. Calibrate IMU (biases/scale) and align sensor frames; verify post-calibration drift within specified limits over a 2-minute static test.

2. Implement a complementary or Kalman filter to fuse wheel odometry and IMU for heading estimation; quantify reduction in heading error versus individual sensors.

3. Process LiDAR or ultrasonic readings to detect obstacles between 0.2–2.0 m and publish occupancy information; measure precision and recall on a defined test course.

4. Implement a basic computer-vision task (e.g., line or ArUco marker detection) in OpenCV and maintain detection performance under at least two lighting conditions.

Modules

Learning Targets:

1. Create a ROS 2 package with nodes, messages, and launch files; build with colcon without errors and run nodes using ros2 launch.

2. Simulate the robot in Gazebo with appropriate kinematics and noise models; validate TF tree integrity (base_link, odom, map) using tf2 tools.

3. Generate a 2D occupancy grid via SLAM (e.g., GMapping or RTAB-Map) and save the map for reuse; verify coverage and resolution against the simulated environment.

4. Configure AMCL for localization and achieve median pose error below a specified threshold on a validation route visualized in RViz.

5. Implement a global planner (A*/Dijkstra) and a local planner (e.g., DWA/TEB) to reach multiple waypoints while avoiding obstacles; report success rate and path length.

Modules

Learning Targets:

1. Design subsystem and system-level test plans with measurable acceptance criteria covering control, perception, and navigation; execute and record outcomes.

2. Benchmark key metrics (tracking error, mapping error, navigation success rate, energy consumption) and present results with plots and concise analysis.

3. Conduct a failure modes and effects analysis (FMEA) and implement mitigations (watchdog timers, bounds checking, failsafe stop); demonstrate at least one mitigation in operation.

4. Use Git to manage code revisions and issues; integrate basic CI tests or linting and produce a clear README with build, run, and safety instructions.

Modules