A Differentially Flat Trajectory Planning Approach for UAV Swarms With Energy Efficiency and Spatiotemporal Consistency

Unmanned aerial vehicle (UAV) swarms hold significant promise for scalable environment exploration, cooperative transportation and urban air mobility. Nevertheless, trajectory planning for swarms remains challenging due to high computational complexity, stringent spatiotemporal constraints and limited attention to energy efficiency. In this work, we present a unified trajectory planning framework that simultaneously promotes energy efficiency and ensures spatiotemporal consistency. Building upon the minimum control (MINCO) trajectory representation, we extend the differentially flat parameterisation from single‐UAV settings to multiagent systems, enabling efficient representation of swarm trajectories through sparse spatiotemporal parameters. Differentiable cost functions are designed to explicitly capture synchronised arrivals, temporal logic of tasks and collision‐avoidance constraints, whereas analytical gradients are derived to facilitate scalable optimisation. Extensive simulations with swarms of up to 15 UAVs demonstrate the scalability of our method in generating smooth and dynamically feasible trajectories. Compared to state‐of‐the‐art baselines, the proposed framework significantly improves the interagent safety margin (by 35%) and achieves substantial energy efficiency, reducing physical energy consumption by over 10% and control effort by up to 22%.