A robotics simulation framework for autonomous search and rescue operations using the V-REP (CoppeliaSim) simulator.

This repository contains a Python-based framework for simulating autonomous robot navigation, mapping, and search operations in unknown environments. The system uses a Pioneer P3DX robot model in the V-REP (now called CoppeliaSim) simulator to perform tasks such as:

• Environment mapping using occupancy grid techniques
• Random wandering with obstacle avoidance
• Beacon localization
• Path planning and navigation
• Return-to-home functionality

The simulation demonstrates robotics concepts including sensor fusion, simultaneous localization and mapping (SLAM), path planning algorithms, and autonomous navigation strategies.

• Autonomous Navigation: Random wandering with obstacle avoidance using proximity sensors
• Environment Mapping: Occupancy grid mapping to create a representation of the environment
• Path Planning: A* algorithm implementation for optimal path finding
• Beacon Detection: Simulated beacon for search and rescue target identification
• Return-to-Home: Autonomous navigation back to starting position after target discovery
• Data Visualization: Plotting of maps, paths, and navigation data
• Extensible Framework: Modular design allowing for easy addition of new robot behaviors and sensors

• Python 3.x
• V-REP/CoppeliaSim simulator
• Required Python packages:
  • NumPy
  • SciPy
  • Matplotlib
  • Pickle (included in standard library)

1. Install V-REP/CoppeliaSim simulator from Coppelia Robotics
2. Install required Python packages:

   pip install numpy scipy matplotlib

3. Clone this repository
4. Ensure the V-REP remote API is properly configured (the vrep.py module should be accessible)

1. Start the V-REP/CoppeliaSim simulator
2. Load one of the provided scene files:
   • cwMap_VREP_test_mapping.ttt - Basic mapping scenario
   • cwMap_VREP_test_random_wandering.ttt - Random wandering scenario
   • cwMap_VREP_test_return_home.ttt - Complete search and return scenario
3. Run the main script:

   python scenario.py

The codebase is organized into several key components:

• Controller (controller.py): Manages the simulation loop and coordinates the robot's sense-think-act cycle
• Robot (robots.py): Implements the Pioneer P3DX robot with movement and sensing capabilities
• Sensors (sensors.py): Defines proximity sensors, compass, and beacon detection
• Mapper (mapper.py): Implements occupancy grid mapping and path planning algorithms
• Helper (helper.py): Provides utility functions for V-REP communication and mathematical operations
• Logger (logger.py): Handles logging of simulation events and data
• Scenario (scenario.py): Defines the sequence of tasks for the robot to perform

1. The controller initializes the robot and simulation environment
2. In each cycle, the robot:
   • Senses the environment using proximity sensors
   • Updates its internal state (position, orientation, map)
   • Executes the current task based on the scenario definition
3. Mapping data is continuously updated and can be visualized
4. Path planning is performed when needed for return-to-home functionality
5. Simulation statistics are recorded for analysis

The repository includes several pre-configured scenarios:

1. Basic Mapping: The robot centers itself in a room and performs a mapping operation
2. Random Wandering: The robot explores the environment using a random wandering algorithm while mapping
3. Search and Return: The robot searches for a beacon, then plans and executes a path back to its starting position

The simulation produces several outputs:

• Occupancy grid maps (stored in the data/ directory)
• Visualizations of maps and paths (stored in the plots/ directory)
• CSV files with position and motor data (stored in the output/ directory)
• Detailed logs of the simulation (stored in the logs/ directory)

• V-REP/CoppeliaSim Documentation
• Pioneer P3DX Specifications
• Occupancy Grid Mapping Tutorial

This project is licensed under the MIT License - see the LICENSE file for details.