Sign in Subscribe
Machine learning

Agile Trajectory Planning for Formation Flight Using a Virtual Core

Strategic Insights

— 1 min read

Researchers from Xidian University have developed a novel swarm trajectory planning framework that leverages a virtual core to control drone formations, enabling efficient and safe navigation through complex environments. The framework employs virtual core penalties and dynamic maximum speed allocation to maintain formation stability while avoiding obstacles. A collaborative large obstacle boundary search strategy and global swarm planning method are also designed to generate rapid and safe drone trajectories. Experimental results confirm the effectiveness of the proposed approach in various scenarios, including simulations and real-world environments.

Key Takeaways:

  • The current methods for formation flight primarily focus on maintaining formations, neglecting the swarm's agility and failing to leverage global information for obstacle avoidance.
  • The proposed swarm trajectory planning framework utilizes a virtual core to control the swarm, employing virtual core penalties and dynamic maximum speed allocation to balance formation keeping and swarm flexibility.
  • The framework also incorporates a collaborative large obstacle boundary search strategy and global swarm planning method for rapid and safe trajectory generation.
  • Experimental results demonstrate the effectiveness of the proposed approach in simulated and real-world environments.
  • The study was conducted with financial support from the National Natural Science Foundation of China through the 111 Project.
  • Researchers Biao Hou, Jingsen Zhang, and Rui Huang were involved in the research.
  • The study's findings were published in the IEEE Robotics and Automation Letters.

Statistics:

  • 10(8):8546-8553 - The issue and page numbers of the IEEE Robotics and Automation Letters where the study was published.
  • 2025 - The year in which the research was conducted and published.
  • 445 Hoes Lane, Piscataway, NJ 08855-4141, USA - The address of the IEEE-inst Electrical Electronics Engineers Inc.
  • 543 - The page number of the news article in Robotics & Machine Learning.
  • 71000 - The zip code of the People's Republic of China is not relevant, the country is the People's Republic of China.

Sources:

  • Agile Trajectory Planning and Large Obstacle Avoidance for Formation Flight Using a Virtual Core. Ieee Robotics and Automation Letters, 2025;10(8):8546-8553.
  • Study Findings on Robotics and Automation Are Outlined in Reports from Xidian University (Agile Trajectory Planning and Large Obstacle Avoidance for Formation Flight Using a Virtual Core). Robotics & Machine Learning. August 11, 2025; p 543.
  • Xidian University, Sch Artificial Intelligence, Xian 71000, People's Republic of China.
  • Ieee Robotics and Automation Letters can be contacted at: Ieee-inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA.

Read more