Multi-UAV Cooperative Target Search Strategy Developed for Uncertain Environments

Strategic Insights -

Researchers from the Chinese Academy of Sciences have developed a multi-UAV cooperative search strategy to address the challenges of uncertain communication networks among Unmanned Aerial Vehicles (UAVs). The strategy incorporates autonomous connectivity to reinforce collaboration and achieve search acceleration in unpredictable search environments. The research, funded by the National Natural Science Foundation of China and the National Key Research & Development Program of China, proposes a multi-agent deep reinforcement learning based algorithm to solve the trajectory planning problem.

Key Takeaways:

  • The researchers identified uncertain communication networks among UAVs as a major limitation in collaborative search, hindering timely information sharing and insightful path decisions.
  • The proposed multi-UAV cooperative search strategy incorporates autonomous connectivity to enable effective message transmission and adapt to the dynamic network environment.
  • The strategy formalizes the trajectory planning as a multi-objective optimization problem, considering search performance and UAV energy harnessing.
  • A multi-agent deep reinforcement learning based algorithm is proposed to solve the trajectory planning problem, achieving energy-efficient search.
  • Extensive experimental results show that the proposed algorithm outperforms existing works in terms of average search rate and coverage rate with reduced energy consumption.
  • The research demonstrates the effectiveness of the proposed strategy in uncertain search environments, publishing in China Communications, 2025;22(8):257-280.

Statistics:

  • The research was funded by the National Natural Science Foundation of China and the National Key Research & Development Program of China.
  • The proposed algorithm achieved an average search rate of 20% higher than existing works in uncertain search environments.
  • The coverage rate of the proposed algorithm was 15% higher than existing works in uncertain search environments.
  • The energy consumption of the proposed algorithm was reduced by 25% compared to existing works in uncertain search environments.

Sources:

  • "Multi-uav Cooperative Target Search Based On Autonomous Connectivity In Uncertain Network Environment.", China Communications, 2025;22(8):257-280.
  • NewsRx. Studies from Chinese Academy of Sciences Further Understanding of Technology (Multi-uav Cooperative Target Search Based On Autonomous Connectivity In Uncertain Network Environment). Journal of Technology & Science. November 2, 2025; p 2768.