Little Red Rover Beta Testing

Overview

Beta testing and development work for Little Red Rover (LRR), Cornell’s first educational robot designed for introductory robotics courses. This project involved testing, debugging, and performance evaluation to prepare the platform for broader classroom and research deployment.

About Little Red Rover

Little Red Rover is an educational robot developed at Cornell to provide hands-on learning experiences in robotics fundamentals. The rover serves as a practical teaching tool for students learning motion planning, navigation algorithms, and robotic control systems.

Technical Contributions

ROS-Based Motion Planning Implementation

• Implemented and debugged ROS-based motion planning pipelines in Python
• Developed autonomous navigation capabilities using Robot Operating System framework
• Integrated sensor data processing for real-time environment perception
• Created modular code architecture for easy classroom adaptation

Path Planning & Navigation Algorithms

A* Path-Finding Implementation:

• Implemented A* algorithm for optimal obstacle navigation
• Developed collision-free trajectory generation system
• Optimized path planning for various terrain and obstacle configurations
• Ensured real-time performance for educational demonstrations

Key Features:

• Heuristic-based search for efficient pathfinding
• Dynamic obstacle avoidance
• Waypoint generation and trajectory smoothing
• Grid-based environment representation

Performance Evaluation & Analysis

Experimental Setup:

• Designed and executed systematic experiments to evaluate rover performance
• Measured navigation accuracy, path efficiency, and response times
• Tested edge cases including tight spaces, dynamic obstacles, and sensor failures
• Analyzed algorithm behavior under various environmental conditions

Analysis Tools:

• Python: Core scripting and algorithm implementation
• NumPy: Numerical computations and data processing
• Matplotlib: Visualization of paths, trajectories, and performance metrics

Technical Stack

• Framework: Robot Operating System (ROS)
• Language: Python
• Libraries: NumPy, Matplotlib
• Algorithms: A* pathfinding, collision detection
• Tools: ROS navigation stack, rviz visualization

Testing Results

Key Findings

• Identified sensor calibration issues affecting navigation accuracy
• Discovered timing bugs in ROS node communication
• Found edge cases in narrow passage navigation
• Documented platform limitations for educational use cases

Impact & Outcomes

Platform Readiness:

• Successfully prepared LRR for deployment in introductory robotics courses
• Documented comprehensive setup and troubleshooting guides
• Validated educational value through pilot testing sessions
• Established baseline performance benchmarks for future improvements

Educational Value:

• Provides hands-on experience with real robotics systems
• Demonstrates practical applications of path planning algorithms
• Enables students to experiment with ROS in controlled environment
• Supports learning objectives in robotics fundamentals courses

Technical Contributions:

• Production-ready motion planning implementation
• Robust navigation system tested across diverse scenarios
• Comprehensive bug documentation for future maintenance
• Performance baselines for algorithm optimization

Future Enhancements

• Integration with more advanced SLAM algorithms
• Multi-robot coordination capabilities
• Enhanced sensor fusion for improved perception
• Web-based interface for remote rover control

Links

• Platform: Little Red Rover (LRR) - Cornell Educational Robotics
• Topics: Robotics, ROS, Python, Path Planning, A* Algorithm, Motion Planning, Beta Testing