Rescue Maze Galilei Robot
RoboCup Junior 2017 - Best Software Award Winner
Project Overview
Developed an autonomous rescue robot for the RoboCup Junior 2017 Rescue Maze competition in Nagoya, Japan, designed to navigate hazardous environments, locate victims, and perform rescue operations without human intervention. The robot addressed critical challenges in emergency response by demonstrating how autonomous systems can safely operate in dangerous conditions where human rescuers face significant risks.
Built with a sophisticated combination of Python programming, Arduino microcontroller integration, and Raspberry Pi computing power, the system implemented advanced pathfinding algorithms, computer vision for victim detection, and intelligent sensor fusion for environmental awareness. The architecture balanced computational complexity with real-time performance requirements essential for emergency response scenarios.
Achieved exceptional recognition by winning the Best Software Award at RoboCup Junior 2017 for innovative pathfinding algorithms and sensor integration techniques. The robot successfully demonstrated autonomous navigation through complex maze structures, accurate victim identification, and efficient rescue operations while competing against teams from over 40 countries worldwide.
Robot Architecture
Hardware & Software Integration
Computing Platform
- • Raspberry Pi: Main computational brain for high-level decisions
- • Arduino Uno: Real-time sensor processing and motor control
- • Python Framework: AI algorithms and computer vision processing
- • C++ Libraries: Low-level hardware interface and timing-critical operations
- • OpenCV: Computer vision and image processing capabilities
Sensor Suite
- • Ultrasonic Sensors: Distance measurement and obstacle detection
- • Color Sensors: Victim identification and environmental markers
- • Camera Module: Computer vision and victim detection
- • Gyroscope/Accelerometer: Orientation and movement tracking
- • Temperature Sensors: Heat signature detection for victims
Navigation System
- • Advanced pathfinding algorithms
- • Real-time maze mapping
- • Obstacle avoidance strategies
- • Dead-end detection and backtracking
- • Optimal route calculation
Vision System
- • Computer vision processing
- • Victim detection algorithms
- • Color-based identification
- • Environmental marker recognition
- • Real-time image analysis
AI Decision Engine
- • Autonomous decision making
- • Sensor fusion algorithms
- • Priority-based task execution
- • Emergency response protocols
- • Adaptive behavior patterns
Advanced Features & Capabilities
Innovative Pathfinding Algorithms
Award-winning pathfinding implementation combining classical maze-solving algorithms with adaptive strategies for unknown environments. The system dynamically builds maze maps while exploring, optimizes routes in real-time, and handles complex scenarios like dead ends and circular paths.
- • A* Search Algorithm: Optimal pathfinding with heuristic guidance
- • Wall Following: Systematic maze exploration strategy
- • Dead End Detection: Intelligent backtracking mechanisms
- • Dynamic Mapping: Real-time maze structure learning
- • Route Optimization: Shortest path calculation for return journeys
- • Adaptive Behavior: Learning from exploration patterns
Advanced Sensor Integration
Sophisticated sensor fusion system combining multiple data sources for robust environmental awareness and victim detection. The integration handles sensor noise, compensates for hardware limitations, and provides reliable information for autonomous decision-making in challenging conditions.
- • Multi-sensor Fusion: Combining ultrasonic, visual, and thermal data
- • Noise Filtering: Signal processing for reliable measurements
- • Calibration Systems: Automatic sensor calibration and validation
- • Redundancy Planning: Backup systems for sensor failures
- • Real-time Processing: Low-latency sensor data interpretation
- • Environmental Adaptation: Adjusting to varying lighting and conditions
Intelligent Victim Detection
Computer vision-based victim identification system using color recognition, heat signature analysis, and pattern matching to accurately locate and classify victims in emergency scenarios. The system distinguishes between different victim types and prioritizes rescue operations accordingly.
- • Color Recognition: Victim identification by visual markers
- • Heat Detection: Thermal signature analysis for living victims
- • Pattern Matching: Shape and size-based victim classification
- • Priority Assessment: Triage-based rescue sequencing
- • False Positive Filtering: Reducing incorrect victim identifications
- • Documentation System: Recording victim locations and status
Technical Challenges Solved
Real-time Processing Under Competition Constraints
Challenge
- • Strict 8-minute time limit for complete maze exploration
- • Complex sensor processing requiring significant computational power
- • Balancing accuracy with speed in pathfinding algorithms
- • Limited hardware resources on embedded platforms
Solution
- • Optimized algorithms with efficient data structures
- • Parallel processing using multi-threading techniques
- • Smart sensor sampling to reduce computational overhead
- • Hardware-specific optimizations for Raspberry Pi platform
Sensor Noise and Environmental Variability
Challenge
- • Ultrasonic sensor noise affecting distance measurements
- • Varying lighting conditions impacting computer vision
- • Interference between multiple sensors operating simultaneously
- • Calibration drift during extended operation periods
Solution
- • Advanced filtering algorithms and sensor fusion techniques
- • Adaptive lighting compensation for computer vision
- • Sensor timing coordination to minimize interference
- • Real-time calibration monitoring and adjustment
Unknown Maze Navigation and Mapping
Challenge
- • Complete maze structure unknown at competition start
- • Dead ends requiring intelligent backtracking strategies
- • Optimal path calculation with incomplete information
- • Memory constraints for storing complex maze maps
Solution
- • Dynamic maze mapping with efficient data structures
- • Breadth-first search combined with wall-following algorithms
- • Heuristic-based pathfinding for optimal exploration
- • Compressed map representation for memory efficiency
Competition Results & Recognition
RoboCup Junior 2017 Achievements
Technical Achievements
- Best Software Award: Recognition for innovative algorithm design
- Autonomous Navigation: Complete maze exploration without human intervention
- Victim Identification: 95% accuracy in emergency scenarios
- Real-time Performance: Meeting strict competition time constraints
Innovation Recognition
- Pathfinding Innovation: Novel maze exploration algorithms
- Sensor Integration: Advanced multi-sensor fusion techniques
- Modular Design: Extensible and maintainable architecture
- Competition Optimization: Performance tuning for contest requirements
Technical Specifications
Hardware Platform
- • Raspberry Pi 3: Main computational unit with ARM Cortex-A53
- • Arduino Uno: Real-time sensor processing and motor control
- • Camera Module: Raspberry Pi Camera v2 for computer vision
- • Ultrasonic Sensors: HC-SR04 for distance measurement
- • Color Sensors: TCS3200 for victim identification
- • IMU: MPU-6050 for orientation and movement tracking
Software Stack & Performance
- • Python 3.6: Main programming language for AI algorithms
- • OpenCV 3.4: Computer vision and image processing
- • NumPy/SciPy: Mathematical computations and data processing
- • Arduino IDE: Low-level hardware programming
- • Processing Speed: 30 FPS sensor data processing
- • Response Time: <100ms decision-making latency
Future Applications
Emergency Response Evolution
- • Search and Rescue: Real-world disaster response operations
- • Hazardous Environment Exploration: Nuclear and chemical incidents
- • Building Inspection: Post-earthquake structural assessment
- • Fire Response: Smoke-filled environment navigation
- • Medical Assistance: First aid delivery in dangerous areas
- • Surveillance Systems: Security and monitoring applications
Technology Transfer Opportunities
- • Autonomous Vehicles: Navigation and obstacle avoidance
- • Industrial Automation: Warehouse and factory robotics
- • Healthcare Robotics: Hospital navigation and assistance
- • Space Exploration: Planetary rover navigation systems
- • Smart Home Systems: Domestic service robot applications
- • Agricultural Robotics: Crop monitoring and harvesting
Lessons Learned
Real-time Systems and Competition Pressure
Developing for competition environments teaches invaluable lessons about performance optimization under pressure. The 8-minute time constraint forced creative solutions that balanced algorithmic sophistication with practical execution speed. This experience highlighted the importance of thorough testing, robust error handling, and graceful degradation when systems don't perform as expected.
Hardware-Software Integration Complexity
Working with multiple hardware platforms (Raspberry Pi and Arduino) taught the critical importance of understanding the interface between high-level algorithms and low-level hardware control. Sensor noise, timing constraints, and communication protocols became as important as the algorithmic design. This project emphasized that successful robotics requires expertise across the entire technology stack.
Algorithm Design for Unknown Environments
Designing algorithms for completely unknown environments requires a fundamentally different approach than working with predefined maps or datasets. The robot had to balance exploration with exploitation, handle incomplete information gracefully, and adapt strategies based on discovered environmental features. This experience provided deep insights into autonomous decision-making under uncertainty.
International Competition and Team Collaboration
Competing at an international level in Japan provided invaluable experience in presenting technical work to expert judges and peers from diverse backgrounds. The Best Software Award recognition validated the importance of clear documentation, modular design, and innovative problem-solving approaches. This experience demonstrated how technical excellence must be combined with effective communication for maximum impact.