A Nonlinear Model Predictive Control Method for Trajectory Planning of UAV Swarms

Abstract

Trajectory planning is a key enabling technology for UAV swarms operating in complex and obstacle-rich environments. This paper proposes a distributed nonlinear model predictive control (NMPC)-based trajectory planning method for UAV swarms, where terminal target reaching, prescribed formation maintenance, obstacle avoidance, and inter-UAV collision avoidance are incorporated into a unified predictive optimization framework. To reduce the online computational burden, a control parameterization strategy is introduced to describe the control input using M control segments, thereby reducing the dimension of the online decision variables. Furthermore, an exact-penalty-based constraint transcription method is developed to transform the original constrained optimal control problem into a lower-complexity finite-dimensional nonlinear programming problem, while efficiently handling velocity constraints, obstacle avoidance constraints, and inter-UAV collision avoidance constraints. Simulation results for a three-UAV swarm in a cluttered environment demonstrate that the proposed method can generate dynamically feasible and collision-free trajectories, while enabling the swarm to reach the assigned target positions and preserve the desired formation within a certain formation error. Furthermore, real UAV swarm flight experiments were conducted to further validate the practical feasibility and online applicability of the proposed distributed NMPC framework for UAV swarm trajectory planning.