Transforming Emergency Response
Leveraging cutting-edge AI to coordinate drone swarms for rapid disaster assessment and response
Intelligent Automation
Advanced reinforcement learning algorithms enabling autonomous decision-making and coordination in complex environments.
Swarm Intelligence
Distributed intelligence allowing drones to work together efficiently while adapting to changing conditions.
Reliable Performance
Robust systems ensuring consistent operation even in challenging disaster scenarios.
Core Features
Advanced drone swarm system powered by reinforcement learning
Simulation Suite
Complete simulation environment for testing and validating swarm behaviors using industry-standard tools.
- 3D environment simulation
- Real-time visualization
- Hardware-in-loop testing
Flight Control
Robust flight control system integrating ArduPilot with custom drone controllers for precise swarm coordination.
- Custom MAVROS interface
- Multi-drone management
- Failsafe protocols
RL Agent
Advanced reinforcement learning system enabling autonomous decision-making and swarm coordination.
- Multi-agent learning
- Adaptive behavior
- Swarm optimization
Project Timeline
Key milestones in our research journey
Project Inception
Initial research and concept development phase, establishing core objectives and methodologies.
Algorithm Development
Creation and optimization of reinforcement learning algorithms for swarm coordination.
Simulation Testing
Extensive testing in virtual environments using Gazebo and ROS2 integration.
Hardware Implementation
Integration with physical drone systems and real-world testing scenarios.
Project Structure
Understanding our system architecture and components
System Framework
Explore our comprehensive system architecture and component interactions
View FrameworkCore Components
Flight controllers, sensors, and actuators working in harmony
AI Agent System
Reinforcement learning algorithms for autonomous decision-making
Communication Layer
Real-time data exchange and swarm coordination protocols
Data Processing
Big data analytics and real-time processing systems
Research Publications
Our upcoming contributions to autonomous drone swarm systems
A Novel Framework for Autonomous Drone Swarm Systems: Architecture and Implementation
Core framework development and system architecture for multi-drone coordination
Centralized Planning with Decentralized Execution: A Reinforcement Learning Approach for Drone Swarm Coordination
Implementation and analysis of RL-based swarm control strategies
Get in Touch
Interested in collaboration? Let's connect!
Our Team
Meet the brilliant minds behind the innovation
Dr. Mo'taz Abdul Aziz Al-Hami
Project Supervisor
Leading expert in autonomous systems and artificial intelligence, guiding the project's research direction and methodology.
Bashar Ameen Hlail
Systems Framework Specialist
Implemented the drone swarm framework, integrating Linux systems, communication protocols, and reinforcement learning for robust performance.
Emad Issa Mohammad Makhlouf
AI & Autonomous Systems Lead
Specializing in reinforcement learning and autonomous systems, leading the development of swarm coordination algorithms.
Maram Sameer Mohammad Almasarweh
Data Analytics Lead
Expert in big data analytics and processing, implementing efficient data handling solutions using Hadoop and Spark.