Lesson Plan (Grades 9–12): Autonomous Drone Navigation Challenge

Lesson Title: Autonomous Drone Navigation Challenge

Grade Level: Grades 9–12

Subject Area: Computer Science (Robotics & Algorithms) / Engineering (Control Systems) / Physics (Sensor Integration)

Overview Unmanned aerial vehicles (UAVs), or drones, are increasingly used for tasks ranging from aerial photography to package delivery and disaster response. Central to such applications is autonomous navigation, which enables a drone to follow predefined waypoints, detect and avoid obstacles, and adjust its flight path in real-time. In this multi-session STEM lesson, students assume the role of robotics engineers, programming quadcopter drones to complete an obstacle-laden waypoint course using GPS navigation, ultrasonic rangefinders for collision avoidance, and camera feeds for vision-based corrections. Working in teams, learners integrate hardware and software: wiring sensors, writing Python scripts with DroneKit or MAVSDK, and leveraging OpenCV for simple marker detection. Through iterative testing, in simulation and on a real or practice drone, they collect performance data (flight time, path accuracy, avoidance success) and refine their algorithms for speed and reliability. This lesson equips teachers with detailed objectives, accurate materials lists, comprehensive procedures, differentiation strategies, assessment rubrics, and extension ideas to foster critical thinking, collaboration, and technical proficiency.

Objectives and Standards

Learning Objectives

1. Navigation Fundamentals: Explain how GPS waypoints guide a drone through latitude, longitude, and altitude coordinates.
2. Sensor Fusion: Describe how ultrasonic rangefinders detect obstacles and how on-board cameras provide visual confirmation via color-marker detection.
3. Programming & Control: Write Python scripts using DroneKit-Python or MAVSDK to arm the drone, take off, navigate through a sequence of waypoints, and land safely.
4. Obstacle Avoidance Logic: Implement real-time obstacle detection and rerouting by polling ultrasonic sensors and adjusting target waypoints dynamically.
5. Vision Integration: Use OpenCV to process camera feeds, detect colored markers at designated points, and correct the drone’s position based on visual error feedback.
6. Performance Optimization: Measure flight metrics—total mission time, average waypoint error, obstacle-clearance success rate—and iteratively refine code for improved speed and accuracy.

Standards Alignment

• NGSS
  • HS-ETS1-2: Design a solution to a complex real-world problem by breaking it down into smaller, more manageable problems that can be solved through engineering.
  • HS-ETS1-4: Use a computer simulation to model the impact of proposed solutions to a complex real-world problem.
• CSTA K–12 Computer Science Standards
  • 2-DA-07: Structure data and use computational tools to understand relationships and make meaning from large data sets (sensor logs).
  • 2-AP-17: Systematically test and refine programs using a range of test cases.
• Common Core Mathematics
  • HS-N-Q.A.1: Use units as a way to understand problems and to guide the solution of multi-step problems (coordinate conversions, distance calculations).
• NGSS Crosscutting Concepts
  • Systems and System Models: Viewing the drone and its sensors as an integrated control system.
  • Stability and Change: Understanding how iterative algorithm adjustments affect system performance under varying external conditions.