Multi-UAV cooperative target search based on autonomous connectivity in uncertain network environment

Multiple UAVs cooperative target search has been widely used in various environments, such as emergency rescue and traffic monitoring. However, uncertain communication network among UAVs exhibits unstable links and rapid topological fluctuations due to mission complexity and unpredictable environmental states. This limitation hinders timely information sharing and insightful path decisions for UAVs, resulting in inefficient or even failed collaborative search. Aiming at this issue, this paper proposes a multi-UAV cooperative search strategy by developing a real-time trajectory decision that incorporates autonomous connectivity to reinforce multi-UAV collaboration and achieve search acceleration in uncertain search environments. Specifically, an autonomous connectivity strategy based on node cognitive information and network states is introduced to enable effective message transmission and adapt to the dynamic network environment. Based on the fused information, we formalize the trajectory planning as a multi-objective optimization problem by jointly considering search performance and UAV energy harnessing. A multi-agent deep reinforcement learning based algorithm is proposed to solve it, where the reward-guided real-time path is determined to achieve an energy-efficient search. Finally, extensive experimental results show that the proposed algorithm outperforms existing works in terms of average search rate and coverage rate with reduced energy consumption under uncertain search environments.