Sampling-Based Hierarchical Trajectory Planning for Formation Flight

TL;DR

This work tackles safe, formation-preserving UAV flight in cluttered environments by introducing a hierarchical planning framework that separates front-end formation guidance path generation from a back-end MPPI-based distributed trajectory optimization. Each UAV builds a compact safe flight corridor (SFC) around its local map and shares these polytopes to guarantee formation connectivity while avoiding obstacles. The front-end uses sampling, multiple costs, and a task-assignment step to produce a sequence of formation configurations and corresponding guidance paths, which the back-end MPPI then converts into smooth, feasible trajectories that respect dynamics, safety, and inter-UAV separation. The approach demonstrates real-time capability on GPU and outperforms two state-of-the-art baselines in simulations across sparse to dense obstacle environments, including narrow corridors. The combination of centralized guidance with distributed optimization enables robust formation keeping in challenging environments and paves the way for real-world multi-UAV deployment.

Abstract

Formation flight of unmanned aerial vehicles (UAVs) poses significant challenges in terms of safety and formation keeping, particularly in cluttered environments. However, existing methods often struggle to simultaneously satisfy these two critical requirements. To address this issue, this paper proposes a sampling-based trajectory planning method with a hierarchical structure for formation flight in dense obstacle environments. To ensure reliable local sensing information sharing among UAVs, each UAV generates a safe flight corridor (SFC), which is transmitted to the leader UAV. Subsequently, a sampling-based formation guidance path generation method is designed as the front-end strategy, steering the formation to fly in the desired shape safely with the formation connectivity provided by the SFCs. Furthermore, a model predictive path integral (MPPI) based distributed trajectory optimization method is developed as the back-end part, which ensures the smoothness, safety and dynamics feasibility of the executable trajectory. To validate the efficiency of the developed algorithm, comprehensive simulation comparisons are conducted. The supplementary simulation video can be seen at https://www.youtube.com/watch?v=xSxbUN0tn1M.

Figures (8)

• Figure 1: A snapshot during the process of formation flight. The light grey polytope is the generated SFC by each UAV, the black lines and light red lines are the formation guidance path generated in Section \ref{['formation path']} and the trajectory optimized in Section \ref{['MPPI']} respectively.
• Figure 2: Framework of the sampling-based hierarchical trajectory planning method.
• Figure 3: SFC displayed by grey polytope is constructed around the UAV in 3-D environment with some obstacles. Fig. \ref{['sfc pic1']} and Fig. \ref{['sfc pic2']} are the main view and the top view of the SFC respectively.
• Figure 4: The black dot lines connect the inner points in a formation configuration and the red lines are the connecting paths. In Fig. \ref{['not connect']}, there is no formation connectivity between $\mathbf{f_c^a}$ and $\mathbf{f_c^b}$ due to the intersection of the path between $a3$ and $b3$ with the ellipse obstacle. Conversely, as shown in Fig. \ref{['connect']}, all paths connecting the correlation points between $\mathbf{f_c^a}$ and $\mathbf{f_c^c}$ are unobstructed, thus indicating that the two formation configurations are connected.
• Figure 5: Sampling process illustration in 2-D space.