Motion Planning for Multi-UAVs Based on Distributed SE(3) Graph Optimization
Jinlong Lei, Cunhao Li, Peng Yi · Guidance Navigation and Control · 2025
Unmanned aerial vehicle (UAV) swarms, with their superior maneuverability and efficient coordination capabilities, are widely applied in complex missions such as inspection, search and rescue. In dense and crowded low-altitude environments, the coupled planning of position and orientation is critical to ensuring the safe navigation of UAV swarms. To address this challenge, we develop a cooperative motion planning framework based on graph optimization for generating trajectories in [Formula: see text] space. Each UAV is modeled as an ellipsoid for collision detection, allowing it to safely pass through narrow passages smaller than its own diameter. To ensure the smoothness and safety of the trajectories, the planning process integrates feasibility constraints arising from dynamics and geometry, resulting in a multi-UAV pose planning formulation. A distributed trajectory planning framework, called Pose Graph-based ADMM (PG-ADMM), is subsequently developed for the UAV swarm by leveraging the alternating direction method of multipliers (ADMM) within the pose graph optimization framework. Finally, the effectiveness and practicality of the PG-ADMM algorithm are systematically evaluated through a series of simulation scenarios.