Distributed Switching Model Predictive Control Meets Koopman Operator for Dynamic Obstacle Avoidance

TL;DR

This paper presents a novel distributed control framework for UAVs that combines Koopman operator-based prediction with switched model predictive control to enable safe, real-time navigation in dynamic environments with moving obstacles.

Contribution

It introduces a Koopman-enhanced distributed SMPC approach that accurately predicts obstacle dynamics and enables autonomous, collision-free UAV coordination in complex, changing environments.

Findings

Reliable formation control demonstrated in simulations

Effective real-time obstacle avoidance achieved

Scalable architecture suitable for urban UAV traffic

Abstract

This paper introduces a Koopman-enhanced distributed switched model predictive control (SMPC) framework for safe and scalable navigation of quadrotor unmanned aerial vehicles (UAVs) in dynamic environments with moving obstacles. The proposed method integrates switched motion modes and data-driven prediction to enable real-time, collision-free coordination. A localized Koopman operator approximates nonlinear obstacle dynamics as linear models based on online measurements, enabling accurate trajectory forecasting. These predictions are embedded into a distributed SMPC structure, where each UAV makes autonomous decisions using local and cluster-based information. This computationally efficient architecture is particularly promising for applications in surface transportation, including coordinated vehicle flows, shared infrastructure with pedestrians or cyclists, and urban UAV traffic. Simulation results demonstrate reliable formation control and real-time obstacle avoidance, highlighting the frameworks broad relevance for intelligent and cooperative mobility systems.