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Outdoor flocking of quadcopter drones with decentralized model predictive control.

Quan Yuan¹, Jingyuan Zhan¹, Xiang Li¹

¹Adaptive Networks and Control Lab, Department of Electronics Engineering, Fudan University, Shanghai 200433, China; Research Center of Smart Networks and Systems, School of Information Science and Engineering, Fudan University, Shanghai 200433, China.

ISA Transactions

|July 16, 2017

Summary

This study introduces a decentralized model predictive control (DMPC) flocking algorithm for multi-drone systems. The DMPC algorithm enables drones to autonomously coordinate and maintain formations, demonstrating effective path tracking and convergence rates.

Keywords:

Decentralized model predictive control Flocking Multi-agent system Quadcopter drone

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Area of Science:

Robotics
Control Systems
Artificial Intelligence

Background:

Coordinated multi-drone systems require robust algorithms for autonomous navigation and formation control.
Decentralized control architectures offer advantages in scalability and resilience for drone swarms.

Purpose of the Study:

To present a novel decentralized model predictive control (DMPC) flocking algorithm for multi-drone systems.
To evaluate the performance of the DMPC flocking algorithm in terms of convergence rate and path tracking capabilities.

Main Methods:

Implementation of a decentralized model predictive control (DMPC) flocking algorithm on a multi-drone system.
Utilizing XBee wireless modules for anonymous, decentralized data transmission between drones.
Employing a double-layered agent system on each drone, comprising coordination and flight control layers.

Main Results:

Numerical simulations and field tests with a five-drone flock validated the DMPC flocking algorithm's effectiveness.
The algorithm demonstrated strong performance in achieving desired drone formations and maintaining flock cohesion.
The system exhibited efficient convergence rates and accurate path tracking capabilities.

Conclusions:

The presented DMPC flocking algorithm is a viable solution for decentralized control in multi-drone systems.
The system's performance is influenced by communication range and desired inter-drone distances.
The research validates the potential of DMPC for complex swarm behaviors and coordinated tasks.