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Kilobot Swarm Robotics: Bio-Inspired Collective Behavior

Overview

This project explores swarm robotics using Kilobots to simulate and implement bio-inspired collective behaviors, particularly those observed in ant colonies. The project includes both simulation work in CoppeliaSim (V-REP) and real hardware implementation using physical Kilobots.

Project Description

Kilobots are small, low-cost robots designed for swarm robotics research. This project demonstrates:

  • Leader-follower behavior: One "queen" robot leads while others follow
  • Bio-inspired movement patterns: Mimicking ant colony foraging and communication behaviors
  • Visual feedback system: LED indicators showing robot states and battery levels
  • Collective decision making: Distributed algorithms for group coordination

Hardware Components

Kilobots Specifications

  • Power: Lithium battery powered
  • Communication: Infrared-based local communication
  • Locomotion: Vibration-based movement system
  • Feedback: RGB LED indicators
  • Programming Interface: Overhead infrared transmitter (the component you mentioned)

LED Status Indicators

  • 🔵 Blue: Fully charged, Queen robot status
  • 🔴 Red: Low battery/charging required
  • 🟢 Green: Normal operation, follower robot

Project Structure

kilobot-swarm-project/
├── simulation/
│   ├── coppeliasim_scenes/
│   └── vrep_models/
├── hardware/
│   ├── kilobot_code/
│   └── algorithms/
├── videos/
│   ├── Kilobot_Movement.mp4
│   └── Kilobot_Simulation.mp4
└── README.md

Features

Simulation (CoppeliaSim/V-REP)

  • 3D visualization of Kilobot movements
  • Testing of swarm algorithms in virtual environment
  • Parameter tuning and behavior analysis
  • Safe testing before hardware deployment

Hardware Implementation

  • Real-world validation of simulated behaviors
  • Leader-follower dynamics with one queen robot
  • Battery-aware operation with visual indicators
  • Scalable swarm size (tested with multiple Kilobots)

Demo Videos

📹 Simulation Demo: Click here and select "Download raw file" to view the CoppeliaSim simulation

📹 Hardware Demo: Click here and select "Download raw file" to view the physical Kilobots in action

Note: Click on the video files above, then click "Download raw file" or "View raw" to download and watch the demonstrations.

Bio-Inspired Behaviors Implemented

Ant Colony Behaviors

  • Trail Following: Robots follow paths established by the queen
  • Collective Movement: Coordinated group locomotion
  • Leader Selection: Dynamic queen robot identification
  • Communication: Local information sharing through IR communication

Getting Started

Prerequisites

  • CoppeliaSim (V-REP) for simulation
  • Kilobot programming environment
  • Overhead IR transmitter for hardware programming

Running the Simulation

  1. Open CoppeliaSim
  2. Choose the Kilobots
  3. Run the simulation to observe swarm behavior

Hardware Setup

  1. Charge all Kilobots (blue LED indicates full charge)
  2. Program robots with the provided code using overhead transmitter
  3. Designate one robot as queen (blue LED)
  4. Place robots in testing area and observe collective behavior

Technical Implementation

Algorithm Overview

  • State Machine: Each robot operates with defined states (leader/follower)
  • Communication Protocol: IR-based message passing for coordination
  • Motion Control: Vibration-based differential locomotion
  • Battery Management: Power-aware operation with visual feedback

Key Challenges Solved

  • Reliable robot-to-robot communication in noisy environments
  • Battery life optimization for extended operation
  • Scalable algorithms that work with varying swarm sizes
  • Real-time coordination without centralized control

Learning Outcomes

This project provides hands-on experience with:

  • Swarm robotics principles
  • Bio-inspired algorithm design
  • Robot simulation and validation
  • Hardware-software integration
  • Distributed systems programming

Contributing

Feel free to fork this project and submit pull requests for improvements. Areas of interest include:

  • New bio-inspired behaviors
  • Algorithm optimizations
  • Extended simulation scenarios
  • Hardware modifications

License

This project is open source. Please cite this work if used in academic research.

This project demonstrates the fascinating intersection of biology, robotics, and distributed systems, showing how simple robots can exhibit complex collective behaviors inspired by nature.

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