Nonlinear Model Predictive Control for Leaderless UAV Formation Flying with Collision Avoidance under Directed Graphs

TL;DR

This paper introduces a distributed nonlinear model predictive control approach for leaderless UAV formation flying that ensures collision avoidance and formation maintenance in cluttered environments using directed communication graphs.

Contribution

A novel NMPC method based on MRACon framework for leaderless UAV formation control with collision avoidance under directed graphs is proposed.

Findings

Effective in simulation and hardware experiments

Ensures collision avoidance and formation stability

Works with directed communication networks

Abstract

This paper studies the leaderless formation flying problem with collision avoidance for a group of unmanned aerial vehicles (UAVs), which requires the UAVs to navigate through cluttered environments without colliding while maintaining the formation. The communication network among the UAVs is structured as a directed graph that includes a directed spanning tree. A novel distributed nonlinear model predictive control (NMPC) method based on the model reference adaptive consensus (MRACon) framework is proposed. Within this framework, each UAV tracks an assigned reference output generated by a linear reference model that utilizes relative measurements as input. Subsequently, the NMPC method penalizes the tracking error between the output of the reference model and that of the actual model while also establishing constraint sets for collision avoidance and physical limitations to achieve distributed and safe formation control. Finally, simulations and hardware experiments are conducted to verify the effectiveness of the proposed method.