Outdoor flocking of quadcopter drones with decentralized model predictive control
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Published on: August 27, 2021
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.
Abstract:
In this paper, we present a multi-drone system featured with a decentralized model predictive control (DMPC) flocking algorithm. The drones gather localized information from neighbors and update their velocities using the DMPC flocking algorithm. In the multi-drone system, data packages are transmitted through XBee® wireless modules in broadcast mode, yielding such an anonymous and decentralized system where all the calculations and controls are completed on an onboard minicomputer of each drone. Each drone is a double-layered agent system with the coordination layer running multi-drone flocking algorithms and the flight control layer navigating the drone, and the final formation of the flock relies on both the communication range and the desired inter-drone distance. We give both numerical simulations and field tests with a flock of five drones, showing that the DMPC flocking algorithm performs well on the presented multi-drone system in both the convergence rate and the ability of tracking a desired path.
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