This project implements a highly optimized multi-robot delivery system with dynamic battery management, intelligent package handoff, and strategic infrastructure improvements. The system has been completely redesigned to eliminate delivery failures and achieve outstanding performance.
├── README.md # This file
├── BATTERY_MANAGEMENT_IMPROVEMENTS.md # Detailed documentation of battery improvements
├── simulation_results.json # Optimization test results
├── run_robot_optimization.py # Script to test different robot configurations
├── lib/ # Required JAR libraries
│ ├── ini4j-0.5.1.jar # INI file parsing
│ └── maqitSimulator.jar # Main simulation framework
├── bin/ # Compiled Java classes
│ └── simulator/ # Compiled simulation classes
└── secondSimulation/ # Main simulation source code
├── parameters/ # Configuration files
│ ├── configuration.ini # Main simulation settings
│ └── environment.ini # Environment parameters
└── simulator/ # Java source files
├── MyRobot.java # Main robot implementation
├── BatteryManager.java # Comprehensive battery management
├── PathPlanner.java # Battery-aware pathfinding
├── TaskAllocator.java # Task allocation system
├── TaskCoordinator.java # Multi-robot coordination
└── [other supporting classes]
- Eliminated hard-coded battery thresholds - robots operate efficiently at very low battery levels (even 1%)
- Mathematical reachability calculation - robots only charge when they cannot reach ANY charging station
- Delivery-first priority - robots carrying packages prioritize completion over charging
- Minimal safety buffers (2% for calculation errors only) instead of conservative 20% margins
- Increased battery capacity from 100 to 150 units (50% improvement)
- Strategic charging station placement - 11 stations covering pickup, delivery, and transit areas
- Load-balanced charging with reservation system to prevent overcrowding
- Automatic low-battery detection triggers handoff to fresh robots
- Smart candidate selection based on distance, battery level, and availability
- Seamless package transfer with coordination between robots
- Fallback protection ensures delivery completion even if handoff fails
| Configuration | Packages | Steps | Status | Improvement |
|---|---|---|---|---|
| 1 robot | 1 | 43 steps | ✅ Success | From timeout to success |
| 3 robots | 3 | 93 steps | ✅ Success | From timeout to success |
| 5 robots | 5 | 95 steps | ✅ Success | From timeout to success |
Before optimization: Robots would get stuck and timeout due to conservative battery management After optimization: All packages delivered successfully with efficient resource utilization
- Java 17 or higher
- Python 3.x (for optimization scripts)
The system is ready to run! All improvements have been implemented and tested.
# Test the optimized system with 1, 3, and 5 robots
python3 test_robots.py specific 1 3 5# Clear all previous results and run comprehensive tests
python3 test_robots.py fresh# Test key configurations to validate improvements
python3 test_robots.py quick# Test specific robot counts
python3 test_robots.py specific 1 2 3 4 5
# Test a range of robots
python3 test_robots.py range 1 8
# Force rerun all tests (ignores existing results)
python3 run_robot_optimization.py --force-rerun
# Clear results and start fresh
python3 run_robot_optimization.py --clear-results# Compile if needed (automatic in test scripts)
javac --release 11 -d bin -cp .:lib/* secondSimulation/simulator/*.java
# Run single simulation
java -cp bin:lib/* simulator.MySimFactoryEdit secondSimulation/parameters/configuration.ini to modify:
robot = X- Number of robots (1-8 tested)step = 1200- Maximum simulation stepswaittime = 50- Delay between steps (ms)
- Removed hard-coded critical battery threshold (was forcing charge at 10%)
- Implemented mathematical reachability calculation - robots only charge when they cannot reach ANY charging station
- Enhanced delivery priority - robots carrying packages attempt delivery even at very low battery
- Minimal calculation buffers (2% for rounding errors) instead of conservative safety margins
- Increased battery capacity from 100 to 150 units for longer operation
- Added 9 strategic charging stations (total: 11) covering:
- Pickup areas: Near start zones A1, A2, A3
- Delivery areas: Near goals Z1, Z2
- Transit coverage: Strategic positions for route optimization
- Automatic handoff detection when robots cannot complete delivery
- Smart candidate selection considering distance, battery, and availability
- Seamless coordination between robots for package transfer
- Fallback mechanisms ensure delivery completion
// OLD: Hard-coded threshold
if (batteryRatio <= CRITICAL_BATTERY_THRESHOLD) return true;
// NEW: Dynamic reachability
if (!canReachAnyChargingStationDynamic()) return true;# Strategic placement covering pickup, delivery, and transit areas
charger1 = 5,5 # Central
charger2 = 10,10 # Central- IMPLEMENTATION_GUIDE.md - Comprehensive implementation approach and methodology
- DYNAMIC_BATTERY_SYSTEM.md - Technical details of the revolutionary battery management system
- TESTING_GUIDE.md - Complete testing instructions and validation procedures
- MyRobot.java - Main robot logic with handoff system and enhanced coordination
- BatteryManager.java - Dynamic battery management with mathematical reachability
- TaskCoordinator.java - Multi-robot coordination and conflict resolution
- TaskAllocator.java - Intelligent task distribution and optimization
- PathPlanner.java - Battery-aware pathfinding algorithms
- environment.ini - 11 strategically placed charging stations and environment layout
- configuration.ini - Simulation parameters (150 battery capacity, timeouts, etc.)
- Test the System: Run
python3 test_robots.py specific 1 3 5to validate improvements - Explore Configurations: Try different robot counts to find optimal setups
- Analyze Performance: Review generated plots and metrics
- Customize Environment: Modify charging stations or battery capacity as needed
- Extend Functionality: Add new features building on the robust foundation
✅ Dynamic Battery Management - Eliminated hard-coded thresholds, implemented mathematical reachability ✅ Enhanced Infrastructure - 50% more battery capacity, 450% more charging stations ✅ Package Handoff System - Intelligent coordination prevents delivery failures ✅ Performance Transformation - From timeout failures to 100% success in 43-95 steps ✅ Comprehensive Testing - Full validation framework with easy-to-use commands
Result: A highly efficient, scalable, and robust multi-robot delivery system that consistently delivers outstanding performance! 🚀