Agile Trajectory Planning and Large Obstacle Avoidance for Formation Flight Using a Virtual Core
Jingsen Zhang, Hou Biao, Rui Huang
AI summary
Problem
Current formation flight methods neglect swarm agility and fail to handle large obstacles effectively, often disrupting formation consistency or requiring heavy communication overhead.
Approach
The method guides the swarm using a non-physical virtual core, applies penalties and dynamic speed limits to maintain cohesion and flexibility, and uses a collaborative boundary search to navigate around large obstacles without full map sharing.
Key results
- Virtual core penalties and dynamic speed allocation balance formation keeping with swarm flexibility.
- Collaborative boundary search enables safe large obstacle avoidance without global map exchange.
- Comprehensive simulations and real-world tests validate improved agility and trajectory consistency.
- First approach to achieve large-scale obstacle avoidance in drone formations while maintaining formation stability.
Why it matters
Enables safer, more agile multi-drone operations in complex environments like search-and-rescue where large obstacles frequently disrupt traditional swarm navigation.
Abstract
Current methods for formation flight primarily focus on maintaining formations, often neglecting the swarm’s agility. Furthermore, most of these approaches fail to leverage global information from the swarm for obstacle avoidance, mak- ing them incapable of generating efficient and safe trajectories in large obstacle scenarios. To address these limitations, this letter proposes a novel swarm trajectory planning framework that utilizes a virtual core to control the swarm. We employ virtual core penalties and dynamic maximum speed allocation to strike a balance between swarm flexibility and formation keeping, allowing the drones to avoid obstacles more smoothly and safely while maintaining formation stability. For large obstacle avoidance, we design a collaborative large obstacle boundary search strategy and a global swarm planning method to enable the rapid and safe generation of drone trajectories . To validate the performance of the proposed method, we develop a comprehensive set of experimental scenarios that include both simulations and real-world environments. The experimental results confirm the effectiveness of our approach.