Session Length: 50-60 minutes
Learning Style: Autonomous challenges, design thinking
Key Learnings (Sessions 1-10):
- Session 1-2: Mastering basic Arduino coding for autonomy and sensor feedback.
- Session 3-4: Introduction to complex decision-making using multiple sensors.
- Session 5-6: Building simple autonomous navigation systems for obstacle avoidance.
- Session 7-8: Using the robot’s screen for feedback based on sensor data.
- Session 9-10: Designing a system that navigates, detects, and reacts to its environment autonomously.
Equipment Needed: (Included in the kit) M3D Go, PlatformIO, Internet, sensors, servo-motors, load-carrying accessories, Laptop (not in the kit)
Course Breakdown
1. Sensor Superstars: Data Gathering
Students will explore the various sensors available on the bot, such as distance, line, and proximity sensors. They will write simple code to collect data from each sensor and display it on the robot’s screen, fostering an understanding of how sensors gather information from the environment.
2. Conditional Logic: If-Then Adventures
Building on their understanding of conditionals, students will write more complex if-else statements to control the bot’s behavior based on sensor input. They’ll experiment with creating decision trees to determine how the bot should respond in various scenarios.
3. Obstacle Finder: Scanning the Environment
Students will program the bot to rotate and scan the environment using the distance sensor to detect obstacles. They’ll learn to implement algorithms that allow the bot to map out a space based on the distance readings, enhancing its ability to navigate.
4. Brightness Tracking
In this lesson, students will enhance their line-following code to adapt to varying brightness levels. They’ll implement algorithms that allow the bot to follow a path based on brightness differences, improving its ability to navigate along a designated route.
5. Teamwork Makes the Dream Work
Students will program multiple bots to work together in a task, such as moving objects or navigating a maze. They will learn about synchronization and communication protocols, enabling their bots to share information and coordinate actions effectively.
6. Navigation Basics: Moving with Purpose
In this session, students will focus on programming the bot to navigate a simple maze. They’ll learn to use basic movement commands (forward, backward, turn) to navigate the maze while avoiding obstacles. This hands-on activity will help reinforce spatial awareness and basic coding concepts without delving into complex algorithms.
7. Dynamic Obstacles: Reacting in Real Time
Students will program the bot to detect and react to dynamic obstacles (e.g., moving objects). This involves coding reactive behaviors and enhancing the bot’s ability to navigate through unpredictable environments, fostering problem-solving skills.
8. Data Logging and Analysis: Understanding Performance
Students will learn to log sensor data during their bot’s operation, enabling them to analyze performance metrics. They will develop a simple program to record speed, distance, and sensor readings, culminating in a discussion about optimizing performance based on data.
9. Dumper Dynamics: Automated Loading and Unloading
n this lesson, students will enhance the dumper’s functionality, programming it to automatically load and unload items based on sensor input. This introduces more complex automation and control techniques, allowing for intricate task sequencing.
10. Final Challenge: The Robotics Showdown
For the final lesson, students will create an autonomous robotics challenge that combines all their learning. They will design a course that requires navigation, obstacle avoidance, and cooperation with other bots, showcasing their projects and explaining the coding logic behind their solutions.