Sophisticated multi-drone control system for autonomous formation flight and coordinated operations
This project involved engineering a sophisticated multi-drone control system using ArduPilot SITL (Software in the Loop) for autonomous formation flight and coordinated operations. The system enables multiple drones to operate together as a cohesive swarm, maintaining formation, avoiding obstacles, and executing complex mission plans autonomously.
The project addresses the growing need for coordinated unmanned aerial vehicle operations in applications such as search and rescue, surveillance, and environmental monitoring. By developing advanced swarm behavior algorithms, we created a system capable of intelligent decision-making and adaptive behavior in dynamic environments.
We chose ROS 2 for its robust distributed architecture and real-time capabilities, essential for coordinating multiple drones. ArduPilot SITL provided a realistic flight simulation environment, while Gazebo allowed us to create custom scenarios and test our algorithms safely before real-world deployment.
Screenshot of multiple drones maintaining formation flight
Placeholder: ../images/robotics/drone-swarm-formation.jpgMission planning and monitoring dashboard
Placeholder: ../images/robotics/drone-control-interface.png3D simulation environment with multiple drones
Placeholder: ../images/robotics/gazebo-drone-simulation.jpgVideo demonstration of autonomous formation changes
Placeholder: ../videos/robotics/drone-swarm-formation-demo.mp4Algorithm visualization showing collision avoidance
Placeholder: ../images/robotics/drone-path-planning.pngReal-time obstacle detection and avoidance demonstration
Placeholder: ../videos/robotics/drone-obstacle-avoidance.mp4The system follows a distributed architecture where each drone operates as an autonomous agent while communicating with the swarm through a central coordination node. We implemented a hierarchical control structure with three levels: individual drone control, local swarm coordination, and global mission planning.
We employed Reynolds' boids algorithm as the foundation for swarm behavior, enhanced with potential field methods for obstacle avoidance. The system uses distributed consensus algorithms to ensure all drones agree on formation parameters and mission objectives. Reinforcement learning was integrated to optimize formation efficiency and adapt to environmental changes.
Managing communication between multiple drones with varying signal strengths and potential delays was critical for maintaining formation integrity. We solved this by implementing a robust communication protocol with message prioritization, redundant communication channels, and predictive algorithms that account for communication delays in formation control.
As the number of drones increased, the computational complexity of coordination algorithms grew exponentially. We addressed this through hierarchical clustering, where drones are grouped into sub-swarms with local leaders, reducing the overall communication overhead and computational burden.
Implementing collision avoidance that works for both static and dynamic obstacles while maintaining formation proved challenging. Our solution combined artificial potential fields with rapidly-exploring random trees (RRT) for dynamic path planning, allowing real-time adaptation to changing environments.
This project significantly enhanced my understanding of distributed systems, multi-agent coordination, and real-time control systems. I gained expertise in ROS 2 architecture, advanced simulation techniques, and the practical challenges of implementing theoretical algorithms in real-world scenarios. The experience also improved my skills in collaborative software development and project management.
Given more time, I would implement machine learning-based adaptive formation control that learns optimal formations for different mission types. Additionally, integrating computer vision for environmental perception and adding support for heterogeneous drone types would make the system more versatile and applicable to real-world scenarios.
This was a collaborative project that demonstrated the power of teamwork in tackling complex engineering challenges. Each team member brought unique expertise that contributed to our success.
Team Members:
Supervisor: Dr. Mo'taz Al-Hami — +962 79 093 0707
Institution: Hashemite University, Robotics and Automation Lab
We used agile development methodologies with weekly sprint reviews and continuous integration practices. Our collaboration was enhanced through shared documentation, code reviews, and regular testing sessions that ensured all components worked seamlessly together.