Swarm Robotics for Search and Rescue Operations

This research project studies decentralized swarm-robotics algorithms for search-and-rescue scenarios, where a group of low-cost robots must cooperatively explore an unknown, potentially hazardous environment (such as a collapsed-building simulation) to locate survivors while maintaining partial network connectivity.

A bio-inspired frontier-exploration algorithm, augmented with a simple pheromone-like virtual marker system to discourage redundant coverage, is implemented and evaluated in a multi-robot simulation environment, measuring area-coverage rate, time-to-first-detection, and robustness to individual robot failures.

The paper compares the decentralized swarm approach against a centralized coordinator baseline, finding that the swarm approach degrades more gracefully when robots are lost or communication is intermittent — a property of particular importance in real disaster-response conditions — while discussing the coverage-efficiency cost of decentralization.

Detailed Project Description

Swarm Robotics for Search and Rescue Operations demonstrates closed-loop motion control: sensor input is turned into motor commands so the machine moves precisely and safely.

How It Works
1. Motors/servos are driven through a driver board from the controller's PWM outputs.
2. Commands come from a remote (Wi-Fi/BLE/RF/gesture) or from an autonomous algorithm.
3. Sensors (IMU, encoders, distance, GPS, LiDAR) provide feedback for stabilisation and obstacle avoidance.
4. A control loop adjusts motor outputs several times per second.

Technology Roles
- Python
- ROS
- Swarm Robotics
- Multi-Robot Simulation

Implementation Steps
1. Collect the components and study each module's datasheet (pin-out, voltage, interface).
2. Wire the circuit on a breadboard following the pin mapping; power sensors from the correct rail (3.3 V / 5 V).
3. Install ROS (Robot Operating System), Gazebo Simulator, Python and the required libraries; flash a minimal test sketch for each module separately.
4. Write the main firmware: read sensors → process → drive outputs / send data.
5. Configure network or cloud credentials and test communication end-to-end.
6. Calibrate thresholds, add error handling (reconnect, sensor time-out) and run a long-duration test.
7. Finalise on a PCB / enclosure and document wiring, code and results.

Key Features
- Remote or autonomous control
- Sensor feedback
- Obstacle handling
- Adjustable speed / precision

Expected Output
On completion the student will have a machine that follows the commanded path or task reliably, with telemetry visible to the operator.

Deliverables
- Complete source code with comments
- Circuit diagram and pin mapping
- Project report with screenshots and results
- Demonstration-ready build